EERAdata Wiki - User contributions [en]

Gap analysis

2020-08-31T10:50:56Z

Christopherbs: /* Results for SMILES */

<accesscontrol>Administrators,consortium</accesscontrol>

This is the page for the gap analysis.

= Tacit knowledge "FAIRification and opening of low carbon energy research data" =
 Consortium members can add any time, they feel that something is important to note even though it is not mentioned in a standard deliverable. In other words, this page collects uncodified, tacit knowledge. It will help us later to compile suggestions and lessons learned. This kind of knowledge is collected two-fold:
#Anytime if someone feels that this should be noted. Please write down: Issue, Date, Author (could also be "anonymous"), the issue described in a few words or maximal lines>
#During the final day of workshops

=== Learning process: ===

*People are hesitant to adopt new IT technologies, this is even the case among researchers heavily relying on data, algorithms and collaborative online software (e.g., R, platforms, online conferencing, HPC, ...). The effort to encourage change is not to underestimate. Reasons are several, notably, lack of time and uncertainty about potential benefit as well as overall risk aversion preferences. EERAdata is using the online software "Only Office" to facilitate collaboration (in particular also during the Covid-19 period).
 '''FAIR and open criteria:'''
*Consortium members have a fair understanding of what FAIR/O is, but there is little knowledge and/or technical experience on how to approach the FAIRification and opening. All, however, share the view that we are at a critical point in time, where we need to implement these criteria. 
*To deepen knowledge about FAIR/O criteria, it is useful to test the criteria on a database one is familiar with. For this purpose (and to start brainstorming about the platform), AIT has developed a questionnaire for application in the use cases.
'''Metadata:'''
*A good starting point is to think about metadata and to look into existing metadata concepts in one's field. The first step is to understand that also metadata need to adhere to the FAIR/O principles.
*The next step is to increase knowledge on IT specific terms, i.e. to understand what the difference is between different '''[https://energydata.onlyoffice.eu/Products/Files/doceditor.aspx?fileid=5404340 metadata frameworks] (taxonomy, thesaurus, ontology) as well as classification of metadata (high-level, medium-level, low-level OR administrative, structural and descriptive metadata).  '''WP 2 being in charge of aligning approaches between use cases, participates in all use case kickoffs to bring everybody on the same page. The presentation is linked with "metadata frameworks".
*It is useful to supply consortium members with read aheads and watch aheads on metadata to prepare the '''[https://energydata.onlyoffice.eu/Products/Files/doceditor.aspx?fileid=5296590 first EERAdata workshop]'''. The workshop starts applications and discussions in the use cases break out sessions (and bringing insights back to the plenary), using selected databases.

= FAIR Assessment=

== FAIR Guiding Principles ==
{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ This table shows the FAIR guiding principles described by [https://www.doi.org/10.1038/sdata.2016.18 Wilkinson et al., 2016].

! To be Findable
|
* F1. (meta)data are assigned a globally unique and persistent identifier
* F2. data are described with rich metadata (defined by R1 below)
* F3. metadata clearly and explicitly include the identifier of the data it describes
* F4. (meta)data are registered or indexed in a searchable resource

|-
! To be Accessible
|
* A1. (meta)data are retrievable by their identifier using a standardized communications protocol
** A1.1 the protocol is open, free, and universally implementable
** A1.2 the protocol allows for an authentication and authorization procedure, where necessary
* A2. metadata are accessible, even when the data are no longer available

|-
! To be Interoperable
|
* I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
* I2. (meta)data use vocabularies that follow FAIR principles
* I3. (meta)data include qualified references to other (meta)data

|-
! To be Reusable
|
* R1. meta(data) are richly described with a plurality of accurate and relevant attributes
* R1.1. (meta)data are released with a clear and accessible data usage license
* R1.2. (meta)data are associated with detailed provenance
* R1.3. (meta)data meet domain-relevant community standards

|}

== Identified gaps after Workshop 1 (02/06/2020 – 04/06/2020) ==

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ This table shows a summary of the specific issues that were identified in Workshop 1. The aim was to categorise different issues to get a better understanding of how to tackle these challenges.
! FAIR/O !! Specific FAIR/O !! Gaps !! Gaps for energy domain !! Use case specific gaps !! Consequences for researchers
|-
! F
| F2
|| Metadata scope
|| Different fields require different metadata (potentially very specific)
|| UC3: lack of additional metadata for applications of materials
|| Data are less useful for the specific field
|-
! F
|| F1/F3
|| Identifiers
||
* Missing identifiers on websites
* Identifier not in downloaded data files
||
|| Makes research more difficult.
|-
! I
| I2
|| Taxonomy/ontology/common vocabulary and language issues
||
* Lack of standardisation
* Heterogeneous data makes standardisation hard
* No vocabulary documentation on websites
* Words used for same term in other languages may differ
* Databases in other languages than English
||
|| Research costs more time
|-
! I
| I3
|| References to other (meta)data
||
* No linking to source documents and related publications (for contextual knowledge)
* Possible need for links to other fields
|| UC3: link between microscopic and macroscopic materials (e.g., turbine blades)
||
* Data cannot be connected to the source
* Makes research more difficult.

|-
! R
| R1.1
|| Licensing
||
* No licenses available may mean the data is not reusable for the researcher
* Licenses not clear and accessible
* Obtaining licenses may result in more effort and costs
||
|| Data potentially not reusable

|-
! R
| R1.2
|| Reliability and provenance of data
|| When the data is collected from different and high number of sources, its reliability decreases
||
|| Data potentially not reusable

|-
! O
|
|| Privacy concerns and expected disadvantages
|| Not publishing data due to privacy concerns (sensitive data)
|| UC2: In case of distribution network data this is relevant
|| Data not accessible

|-
! Other
|
|| Conducting FAIR assessments
||
* No qualitative assessment of the data itself (which may be limited)
* Discrepancies between results conducted by humans versus machines
* Sometimes not even DC standards are met
* Metadata is not updated
* Lacking encryption of websites (https)
* Problems assessing whether metadata will be available after data is unavailable
* Interface design/layout may be unclear, incomplete or not intuitive for humans
* Authentication details (user registration login / good or bad, clarify)

||
* UC1 lack of data availability for time-series
||
* Assessment is uncertain (human assessment may need more clarification and understanding)
* Makes research more time-intensive

|}

== FAIR Self-Assessment tool ==

To conduct a human FAIR evaluation for different databases and their respective datasets, different free online tools were considered. Some seemed to be quite useful (e.g. [https://satifyd.dans.knaw.nl/ SATISFYD] ) and some were simple self-evaluation PDFs to be printed and filled out.

We decided to chose the [https://ardc.edu.au/resources/working-with-data/fair-data/fair-self-assessment-tool/ self-assessment tool] provided by the Australian Research Data Commons (ARDC). The decision was based manly upon the fact that the tools questions closely matched the FAIR categories specified by [https://www.doi.org/10.1038/sdata.2016.18 Wilkinson et al., 2016] which would make it easily comparable to the existing preferable FAIR conditions and aid with the repeatability of the results, and the half-automated scoring features that show the extent of the "FAIRness" of the analysed sample as a simple bar chart.

The result of the FAIR assessment is provided as a bar chart where an empty bar chart displays 0 % FAIR compatibility, and a full bar chart represents 100 % FAIR compatibility. The score is therefore not returned as a specific value. During the assessment below, the "Total Across FAIR" in Table ... was estimated according to the level of the bar chart from 0 % to 100 %.
Resulting scores where categorised as follows: Low: < 50%; Medium: 50-60%; High: > 60% .

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ All options for each question of the ARDCs self-assessment tool
!colspan="4"|Findable
!colspan="3"|Accessible
!colspan="3"|Interoperable
!colspan="3"|Reusable
|-
| F1. (meta)data are assigned a globally unique and persistent identifier
|| F2. data are described with rich metadata (defined by R1 below)
|| F3. metadata clearly and explicitly include the identifier of the data it describes
|| F4. (meta)data are registered or indexed in a searchable resource
|| A1. (meta)data are retrievable by their identifier using a standardized communications protocol
|| A1.1 the protocol is open, free, and universally implementable and A1.2 the protocol allows for an authentication and authorization procedure, where necessary
|| A2. metadata are accessible, even when the data are no longer available
|| I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
|| I2. (meta)data use vocabularies that follow FAIR principles
|| I3. (meta)data include qualified references to other (meta)data
|| R1.1. (meta)data are released with a clear and accessible data usage license
|| R1.2. (meta)data are associated with detailed provenance
|| R1.3. (meta)data meet domain-relevant community standards
|-
! Does the dataset have any identifiers assigned?
! Is the dataset identifier included in all metadata records/files describing the data?
! How is the data described with metadata?
! What type of repository or registry is the metadata record in?
! How accessible is the data?
! Is the data available online without requiring specialised protocols or tools once access has been approved?
! Will the metadata record be available even if the data is no longer available?
! What (file) format(s) is the data available in?
! What best describes the types of vocabularies/ontologies/tagging schemas used to define the data elements?
! How is the metadata linked to other data and metadata (to enhance context and clearly indicate relationships)?
! Which of the following best describes the license/usage rights attached to the data?
! How much provenance information has been captured to facilitate data reuse?
!
|-
| Globally unique, citable and persistant (e.g. DOI, PURL, ARK or Handle)
|| Yes
|| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
|| Data is in one place but discoverable through several registries
|| Publicly accessible
|| Standard web service API (e.g. OGC)
|| Yes
|| In a structured, open standard, machine-readable format
|| Standardised open and universal using resolvable global identifiers linking to explainations
|| Meadata is represented in a machine readable format, e.g. in a linked data format such as Resource Description Framework (RDF)
|| Standard machine-readable license (e.g. Creative Commons)
|| Fully recorded in a machine-readable format
||
|-
| Web adress (URL)
|| No
|| Comprehensively, but in a text-based, non-standard format
|| Generalist public repository
|| Fully accessible to person who meet explicitly stated conditions, e.g. ethics approval for sensitive data
|| Non-standard web service (e.g. OpenAPI/Swagger/informal API)
|| No
|| In a structured, open standard, non-machine-readable format
|| Standardised vocabularies/ontologies/schema without global identifiers
|| The metadata record includes URI links to related metadata, data and definitions
|| Standard text based license
|| Fully recorded in a text format
||
|-
| Local identifier
||
|| Brief title and description
|| Domain-specific repository
|| A de-identified / modified subset of the data is publicly accessible
|| File download from online location
|| Unsure
|| Mostly in a proprietary format
|| No standards have been applied in the description of data elements
|| There are no links to other metadata
|| Non-standard machine-readably license (clearly indicating under what conditions the data may be reused)
|| Partially recorded
||
|-
| No identifier
||
|| The data is not described
|| Local institutional repository
|| Embaged access after a specific date
|| By individual arrangement
||
||
|| Data elements not described
||
|| Non-standard text-based license
|| No provenance information is recorded
||
|-
|
||
||
|| The data is not described in any repository
|| Unspecified conditional access e.g. contact the data custodian for access
|| No access to data
||
||
||
||
|| No license
||
||
|-
|
||
||
||
|| Access to metadata only
||
||
||
||
||
||
||
||
|-
|
||
||
||
|| No access to data or metadata
||
||
||
||
||
||
||
||
|}

At this point it is important to note that the assessment of databases and/or datasets using this tool is solely considered a human assessment. This may lead to different evaluations by different users. To counteract this phenomenon, it was suggested that the evaluations should be completed using the 'four-eyes principle' to create more verifiable results. Also, it is planned to additionally evaluate the respective databases using an external tool to perform a machine-assessment.

In addition, not all questions are weighted equally and thus a worse result for one question may not have a large impact whereas only choosing the second-best option in another question will bring down the overall FAIR value considerably.

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ The ARDC tool provides detailed information about the requirements for each of the options of its tool. These descriptions are documented here.
|-
! rowspan="5" | F
|| F
|| About Findable
|| Making data Findable includes assigning a [https://www.ands.org.au/guides/persistent-identifiers-awareness persistent identifier] (like a [https://www.ands.org.au/guides/doi DOI] or [https://www.ands.org.au/guides/identify-my-data Handle] ), having rich [https://www.ands.org.au/working-with-data/metadata metadata] to describe the data and making sure it is findable through disciplinary and generalist discovery portals (local and international).

|-
| F1
|| About identifiers
|| Identifiers are essential for identifying, finding, retrieving, linking and citing datasets. A Web address (URL) can be used to specify the online location of a resource but over time URLs tend to change which leads to broken links to the data. To be useful, identifiers need to be persistent and unique. Digital Object Identifiers (DOIs) and other persistent identifiers (PIDs) provide a permanent citable reference to a particular dataset. The DOI is a permanent fixed reference to the dataset no matter where it is located online and enables citation and citation metrics. Services to create a persistent identifier are often offered by your affiliated institution or the repository you are using to describe your data. Talk to your library service for more information.

Read more: [https://www.ands.org.au/working-with-data/citation-and-identifiers Persistent identifiers]

|-
| F2
|| About identifiers
|| The identifier (preferably a persistent identifier) needs to be clearly stated in the metadata record describing the data collection, and also in any associated data files or metadata.

|-
|F3
|| About metadata descriptions
|| Comprehensive metadata records will include descriptive content that facilitates discovery, access and reuse of the data being described. While there is no 'one size fits all' list, comprehensive metadata should include:
* a globally unique persistent identifier e.g. a DOI
* a title
* related people, i.e. the data creator or custodian
* how to access the data and file formats
* a description of how the data were created and how to interpret the data subject or keywords
* citation information that clearly indicates how the data should be cited
* a machine-readable data licence
* provenance and contextual information such as:
* links to related publications, projects, services and software
* methodology and processes involved in data production
* spatial and temporal coverage (if relevant)
* object-level data description

Providing metadata in a standard schema allows it to be read and used by machines as well as humans.

Read more: [https://www.ands.org.au/working-with-data/metadata Metadata]

|-
| F4
|| About metadata repositories
|| A rich metadata description alone does not ensure a dataset’s ‘findability’ on the internet; the dataset needs to be registered or indexed in a searchable resource, such as a generalist (e.g. [https://figshare.com/ Figshare], [http://datadryad.org/ Dryad], [https://zenodo.org/ Zenodo], domain-specific (e.g. [https://pangaea.de/ PANGAEA], [https://ada.edu.au/ ADA], [https://www.icpsr.umich.edu/ ICPSR]), or institutional (e.g. [https://openresearch-repository.anu.edu.au/ ANU Research Repository] , [https://ses.library.usyd.edu.au/ Sydney eScholarship Repository], [https://data.gov.au/ data.gov.au]) data repository or registry. Ideally these repositories/registries are indexed by search engines such as Google and/or Google Scholar.

|-
! rowspan = "4" | A
| A
|| About Accessible
|| [https://www.ands.org.au/working-with-data/publishing-and-reusing-data/publishing To make data accessible] may include making the data [https://www.ands.org.au/working-with-data/articulating-the-value-of-open-data/open-data open] using a standardised protocol. However the data does not necessarily have to be open. There are sometimes good reasons why data [https://www.ands.org.au/working-with-data/sensitive-data cannot be made open], for example privacy concerns, national security or commercial interests. If it is not open there should be clarity and transparency around the conditions governing access and [https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse reuse].
|-
| A1
|| About data accessibility
|| Not all data that is discoverable can be freely accessed. Often there are embargoes, access controls, and access permissions associated with data due to a variety of issues such as privacy and commercial interests. Even with all these issues much [https://www.ands.org.au/working-with-data/sensitive-data sensitive data] can be shared. Many other issues that could be perceived as blockers to sharing data may be [http://datapub.cdlib.org/closed-data-excuses-excuses overcome].
|-
| A1.1/A1.2
|| About protocols
|| Ideally users would like to retrieve appropriate internet content directly and unhindered once they have located it. Internet protocols (e.g. http and ftp) define rules and conventions for communication between devices, and tools and services are available to facilitate this process, e.g. APIs.

Protocols:
* [https://www.lifewire.com/hypertext-transfer-protocol-817944 HTTP] (Hypertext Transfer Protocol) is the set of rules for transferring files (text, graphic images, sound, video, and other multimedia files) on the [https://whatis.techtarget.com/definition/World-Wide-Web World Wide Web]. As soon as a Web user opens their Web browser, the user is indirectly making use of HTTP.
* [https://www.lifewire.com/file-transfer-protocol-817943 FTP] (File Transfer Protocol) is a standard Internet [http://searchnetworking.techtarget.com/definition/protocol protocol] for transmitting files between computers on the Internet over TCP/IP connections. Read more: [https://www.lifewire.com/definition-of-protocol-network-817949 Web protocols]
* API (Application Programming Interface): When information is made available in any machine readable format, it becomes possible to make that information directly available to programs that request that information over the web. An API is the way this information is made directly available to other machines.
View more: [https://www.youtube.com/watch?v=s7wmiS2mSXY What is an API?]

OGC - http://www.opengeospatial.org/ogc

|-
| A2
|| About metadata availability
|| Effort is often required to maintain data resources online which can often lead to it being neglected especially over long periods of time. This often leads to broken links between the metadata and the data. Having at least a description of the data in the form of a metadata record means there is a record of the data's existence allowing the possibility of someone tracking it down.

|-
! rowspan="4" | I
|| I
|| About Interoperability
|| To be interoperable (i.e. data that is interpretable by a computer, so that they can be automatically combined with other data) the data will need to use community agreed [https://www.ands.org.au/guides/file-formats formats], language and [https://www.ands.org.au/guides/vocabularies-and-research-data vocabularies]. The [https://www.ands.org.au/working-with-data/metadata metadata] will also need to use a community agreed standards and vocabularies, and contain links to related information using [https://www.ands.org.au/working-with-data/citation-and-identifiers identifiers].
|-
|| I1
|| About file formats
|| When selecting file formats for archiving, the formats should ideally be:
* Non-proprietary
* Unencrypted
* Uncompressed
* In common usage by the research community
* Adherent to an open, documented standard, such as described by the State of California (see [http://www.leginfo.ca.gov/pub/07-08/bill/asm/ab_1651-1700/ab_1668_bill_20070524_amended_asm_v96.pdf AB 1668, 2007])
** Interoperable among diverse platforms and applications
** Fully published and available royalty-free
** Fully and independently implementable by multiple software providers on multiple platforms without any intellectual property restrictions for necessary technology
** Developed and maintained by an open standards organization with a well-defined inclusive process for evolution of the standard.

From: [https://library.stanford.edu/research/data-management-services/data-best-practices/best-practices-file-formats Stanford University Library - Best practices for file formats]

File format examples:
* Structured, open standard, machine-readable format e.g. (text) PDF/A, HTML, Plain text, (images) TIFF, JPEG 2000, GIF, (audio) MP3, AIFF, WAVE, (video) MOV, MPEG, AVI, (Tabular data) CSV
* structured, open standard, non-machine-readable format, e.g. PDF, HTML, JPG
* Proprietary format, e.g. doc (Word), .xls (Excel), .ppt (PowerPoint), .sav

Read more:
* [https://www.ands.org.au/__data/assets/pdf_file/0003/731775/File-Formats.pdf ANDS]
* [https://www.europeandataportal.eu/elearning/en/module9/#/id/co-01 European Data Portal]

|-
|| I2
|| About schema standards
|| Schema standards are schemas that have gone through a formal validation process by a standards organisation, such as the International Standards Organisation (ISO) or an equivalent body such as the Dublin Core Metadata Initiative (DCMI); or, commonly used and consistently applied metadata schemas that are well documented, endorsed, and maintained can also become ‘de-facto standards’, e.g. RIF-CS for describing data collections and services used in Research Data Australia.

Read more: [https://www.ands.org.au/guides/metadata-working Metadata schema]

Standardised open and universal schemas, e.g, [https://www.dcc.ac.uk/resources/metadata-standards/datacite-metadata-schema DataCite Metadata Schema], [https://www.dcc.ac.uk/resources/metadata-standards/prov PROV], [https://www.dcc.ac.uk/resources/metadata-standards/dublin-core Dublin Core]

Domain-specific standards, e.g. [http://human-phenotype-ontology.github.io/ HPO - Human Phenotype Ontology], [https://www.nlm.nih.gov/mesh/ MeSH - Medical Subject Headings] , [https://mcp-profile-docs.readthedocs.io/en/stable/ Marine Community Profile], [https://ddialliance.org/ DDI - Data Documentation Initiative].

For more examples of standards: [http://rd-alliance.github.io/metadata-directory/ Here].

Resolvable global identifiers - An identifier is any label used to name something uniquely (whether online or offline). URLs are an example of an identifier. So are serial numbers, and personal names. A persistent identifier is guaranteed to be managed and kept up to date over a defined time period.

Read more: [https://www.ands.org.au/guides/persistent-identifiers-awareness Persistent Identifiers]

|-
||I3
|| About metadata links
|| The goal is to create as many meaningful linkages as possible between (meta)data resources to enrich the contextual knowledge about the data, balanced against the time/energy involved in making a good data model.

'''Machine-readable (meta)data'''

(Meta)data in a format that can be automatically read and processed by a computer, such as [http://opendatahandbook.org/glossary/en/terms/csv/ CSV], [http://opendatahandbook.org/glossary/en/terms/json/ JSON], [http://opendatahandbook.org/glossary/en/terms/xml/ XML] etc. For more information: [http://opendatahandbook.org/glossary/en/terms/machine-readable/ Open Data Handbook]

'''Linked Data'''

Linked Data (also known as Linking Data) can be applied to improve the exploitation of the “Web of data.” The expression refers to the publishing of structured data in a way that typed links are created between data from different sources to provide a higher level of usability. By using Linked Data, it is possible to find other, related data. Structured data should meet four requirements to be called
Linked Data:

* URIs should be assigned to all entities of the dataset.
* HTTP URIs are required to ensure that all entities can be referenced and cited by users and user agents.
* Entities should be described using standard formats such as RDF/XML.
* Links should be created to other, related entity URIs.

All data that fulfil these requirements and are released for the public are called Linked Open Data (LOD). http://www.lesliesikos.com/semantic-web-machine-readable-structured-data-with-meaningful-annotations/

Read more under the heading (meta)data include qualified references to other (meta)data in the following resource: https://www.dtls.nl/fair-data/fair-principles-explained/

For more information on Linked Data standards - RDF - https://www.w3.org/RDF/

Other related linked data standards:
* OWL - https://www.w3.org/OWL/
* SKOS - https://www.w3.org/2004/02/skos/
* SPARQL - https://www.w3.org/TR/rdf-sparql-query/

|-
! rowspan="3" | R
|| R
|| About Reusable
|| Reusable data should maintain its initial richness. For example, it should not be abridged for the purpose of explaining the findings in one particular publication. It needs a clear machine-readable [https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse licence] and [https://www.ands.org.au/partners-and-communities/ands-communities/data-provenance-interest-group provenance] information on how the data was formed. It should also use discipline-specific data and [https://www.ands.org.au/working-with-data/metadata metadata] standards to give it rich contextual information that will allow for accurate interpretation and reuse.

|-
|| R1.1
|| About licenses
|| If data is not licensed no-one else can use it. In Australia, no licence is regarded as the same as 'all rights reserved', confining any reuse to very limited circumstances. Applying a Creative Commons licence to your data is a simple way to ensure that your data can be reused. The less restrictive the licence, the more that can be done with the data.
To make it easy for the Web to know when a work is available under a particular license, a “machine readable” version of the license provides a summary of the key freedoms and obligations written into a format that software systems, search engines, and other kinds of technology can understand.
Example from the [https://creativecommons.org/choose/ Creative Commons License Choser]:

What appears in the metadata:

This work is licensed under a [https://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International License].

Code snippet:

<code>
<a rel="license" href="http://creativecommons.org/licenses/by/4.0/">

<img alt="Creative Commons License" style="border-width:0"

src="https://i.creativecommons.org/l/by/4.0/88x31.png"/>

</a> This work is licensed under a

<a rel="license" href="http://creativecommons.org/licenses/by/4.0/">

Creative Commons Attribution 4.0 International License</a>.
<\code>

Read more: https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse

|-
||R1.2
|| About provenance
|| Data provenance is used to document where a piece of data comes from and the process and methodology by which it is produced. It is important to confirm the authenticity of data enabling trust, credibility and reproducibility. This is becoming increasingly important, especially in the eScience community where research is data intensive and often involves complex data transformations and procedures ([https://www.ands.org.au/partners-and-communities/ands-communities/data-provenance-interest-group Read more]). Provenance vocabularies such as [https://www.w3.org/2005/Incubator/prov/XGR-prov-20101214/#Recommendations Provenir Ontology (PROV-O) and others].

|}

== Results for SMILES and ShareWind ==

{| class="mw-collapsible wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Datasets from the SMILES and ShareWind platforms was analysed using the self-assessment tool provided by ARDC.
! rowspan="5" |
| rowspan="5" |
! Database Code
| rowspan="24" style="background: #ABE" |
| ED4
| ED9
|-
! Database Title
|| SMILES
| ShareWind
|-
! Database Link
|| https://www.ecria-smiles.eu/
| https://sharewind.eu/
|-
! Sample Link
|| [https://www.ecria-smiles.eu/data-files/-/document_library/W32cCAfL2jMX/view_file664466?_com_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX_redirect=https%3A%2F%2Fwww.ecria-smiles.eu%3A443%2Fdata-files%2F-%2Fdocument_library%2FW32cCAfL2jMX%2Fview%2F664423%3F_com_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX_redirect%3Dhttps%253A%252F%252Fwww.ecria-smiles.eu%253A443%252Fdata-files%253Fp_p_id%253Dcom_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX%2526p_p_lifecycle%253D0%2526p_p_state%253Dnormal%2526p_p_mode%253Dview here] (link has 585 characters)
| https://sharewind.eu/records/f9d31abbc2824284b8c1d28894d9619a (data found at https://zenodo.org/record/1162738)
|-
! Sample Size
|| 4 KB
| 37 MB
|-
| colspan="6" style="background: #ABE" |
|-
! rowspan="4" | Findable
| F1. (meta)data are assigned a globally unique and persistent identifier
! Does the dataset have any identifiers assigned?
|| Web adress (URL)
| Globally unique, citable and persistant (e.g. DOI, PURL, ARK or Handle)
|-

| F2. data are described with rich metadata (defined by R1 below)
! Is the dataset identifier included in all metadata records/files describing the data?
|| No
| No
|-

| F3. metadata clearly and explicitly include the identifier of the data it describes
! How is the data described with metadata?
|| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
|-

| F4. (meta)data are registered or indexed in a searchable resource
! What type of repository or registry is the metadata record in?
|| Domain-specific repository
| Data is in one place but discoverable through several registries
|-

! rowspan="3" | Accessible

| A1. (meta)data are retrievable by their identifier using a standardized communications protocol
! How accessible is the data?
|| Publicly accessible
| Publicly accessible
|-
| A1.1 the protocol is open, free, and universally implementable; A1.2 the protocol allows for an authentication and authorization procedure, where necessary
! Is the data available online without requiring specialised protocols or tools once access has been approved?
|| File download from online location
| Standard web service API (e.g. OGC)
|-
| A2. metadata are accessible, even when the data are no longer available
! Will the metadata record be available even if the data is no longer available?
|| Unsure
| Yes
|-

! rowspan="3" | Interoperable
| I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
! What (file) format(s) is the data available in?
|| In a structured, open standard, machine-readable format
|| In a structured, open standard, non-machine-readable format
|-
| I2. (meta)data use vocabularies that follow FAIR principles
! What best describes the types of vocabularies/ontologies/tagging schemas used to define the data elements?
|| Standardised vocabularies/ontologies/schema without global identifiers
|| Standardised vocabularies/ontologies/schema without global identifiers
|-
| I3. (meta)data include qualified references to other (meta)data
! How is the metadata linked to other data and metadata (to enhance context and clearly indicate relationships)?
|| There are no links to other metadata
|| Metadata is represented in a machine readable format, e.g. in a linked data format such as Resource Description Framework (RDF)
|-

! rowspan="3" | Reusable
| R1.1. (meta)data are released with a clear and accessible data usage license
! Which of the following best describes the license/usage rights attached to the data?
|| No license
| Standard machine-readable license (e.g. Creative Commons)
|-
| R1.2. (meta)data are associated with detailed provenance
! How much provenance information has been captured to facilitate data reuse?
|| Partially recorded
|| Partially recorded
|-
| R1.3. (meta)data meet domain-relevant community standards
|
|
|
|-
| colspan="6" style="background: #ABE" |
|-
! Total across F.A.I.R
|
! Low: < 50%; Medium: 50-60%; High: > 60%
|| ~ 45 %
|| ~ 85 %
|-
| colspan="6" style="background: #ABE" |
|-
! Remarks about database
||
||
||
|| The data is provided by the open-source platform Zenodo, not the project website itself. A very positive aspect is that information on different versions of the data as well as the number of views and downloads is available.
|-
! Identified gaps
||
||
||
* No DOI, only URL
* Extremely long URL link spanning several lines with 585 characters, that can easily become corrupted
* No links to other metadata
* No license information given
* Provenance not fully recorded

||
* Dataset identifiers should be included in the files describing the data
* Provenance details are only partially recorded with a note with contact name for further information
|}

= Metadata assessment =

== Dublin Core Elements ==
{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ The DCMI Metadata Terms lists the current set of the Dublin Core vocabulary. Source: [https://dublincore.org/specifications/dublin-core/dcmi-terms/ Dublin Core Metadata Initiative]
| 1 || abstract || 11 || contributor || 21 || extent || 31 || isReplacedBy || 41 || publisher || 51 || tableOfContents
|-
| 2 || accessRights || 12 || coverage || 22 || format || 32 || isRequiredBy || 42 || references || 52 || temporal
|-
| 3 || accrualMethod || 13 || created || 23 || hasFormat || 33 || issued || 43 || relation || 53 || title
|-
| 4 || accrualPeriodicity || 14 || creator || 24 || hasPart || 34 || isVersionOf || 44 || replaces || 54 || type
|-
| 5 || accrualPolicy || 15 || date || 25 || hasVersion || 35 || language || 45 || requires || 55 || valid
|-
| 6 || alternative || 16 || dateAccepted || 26 || identifier || 36 || license || 46 || rights || ||
|-
| 7 || audience || 17 || dateCopyrighted || 27 || instructionalMethod || 37 || mediator || 47 || rightsHolder || ||
|-
| 8 || available || 18 || dateSubmitted || 28 || isFormatOf || 38 || medium || 48 || source || ||
|-
| 9 || bibliographicCitation || 19 || description || 29 || isPartOf || 39 || modified || 49 || spatial || ||
|-
| 10 || conformsTo || 20 || educationLevel || 30 || isReferencedBy || 40 || provenance || 50 || subject || ||
|}

'''Metadata Category Types according to [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO]'''

There are three main types of metadata:
* Descriptive metadata describes a resource for purposes such as discovery and identification. It can include elements such as title, abstract, author, and keywords.
* Structural metadata indicates how compound objects are put together, for example, how pages are ordered to form chapters.
* Administrative metadata provides information to help manage a resource, such as when and how it was created, file type and other technical information, and who can access it. There are several subsets of administrative data; two that sometimes are listed as separate metadata types are:
** Rights management meta-data, which deals with intellectual property rights,and
** Preservation metadata, which contains information needed to archive and preserve a resource.
''

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Simple Dublin Core Elements. 15 DC elements with their (shortened) official definitions and examples.
! DC element name !! DC definition ([https://www.dublincore.org/specifications/dublin-core/dces/ DCMI]) !! Example (Energy Domain) !! Category (determined with the aid of [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO])
|-
! Title
| A name given to the resource || Outdoor monitoring of a hybrid micro-CPV solar panel with integrated micro-tracking and diffuse capture || Descriptive
|-
! Subject
| The topic of the resource || concentrator photovoltaics; CPV; rooftop photovoltaics; integrated tracking || Descriptive
|-
! Description
| An account of the resource || Dataset from the outdoor characterization of a B Series module from Insolight at the rooftop of the Instituto de Energía Solar - Universidad Politécnica de Madrid. …. || Descriptive
|-
! Creator
| An entity primarily responsible for making the resource || Stephen Askins; Gaël Nardin || Descriptive
|-
! Publisher
| An entity responsible for making the resource available || Stephen Askins || Descriptive
|-
! Contributor
| An entity responsible for making contributions to the resource || Stephen Askins || Descriptive
|-
! Date
| A point or period of time associated with an event in the lifecycle of the resource || 2019-07-23; Date will be associated with the creation or availability of the resource. || Administrative
|-
! Type
| The nature or genre of the resource || info:eu-repo/semantics/other; dataset || Descriptive
|-
! Format
| The file format, physical medium, or dimensions of the resource || image
|| Structural
|-
! Identifier
| An unambiguous reference to the resource within a given context || https://zenodo.org/record/3346823; 10.5281/zenodo.3346823; oai:zenodo.org:3346823 || Descriptive
|-
! Source
| A related resource from which the described resource is derived || RC607.A26W574 1996 [where "RC607.A26W574 1996" is the call number of the print version of the resource, from which the present version was scanned] || Descriptive
|-
! Language
| A language of the resource || eng || Descriptive
|-
! Relation
| A related resource || info:eu-repo/grantAgreement/EC/H2020/787289/ || Descriptive
|-
! Coverage
| The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant || 1995-1996; Madrid, Spain || Descriptive
|-
! Rights
| Information about rights held in and over the resource || info:eu-repo/semantics/openAccess; https://creativecommons.org/licenses/by/4.0/legalcode || Administrative
|}

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Non-DC elements

! Non-DC element name !! Definition !! Example !! Category
|-
! Date of availability
| Since when is the data available
||
|| Impact/Exploitation

|-
! Number of users
| Is there any information on the number of accesses or users?
||
|| Impact/Exploitation

|-
! Number of scientific publications where data is cited/used
| Is there any information on the number of citations/uses?
||
|| Impact/Exploitation

|-
! Curation activities (is the versioning ongoing)
| Update, maintainance
||
|| Impact/Exploitation
|}

Attempting to put a value on Impact/Exploitation:
* '''ENTSO-E''': Found 172 results in Advanced Search (ALL) in Web of Science ([https://apps.webofknowledge.com/ WoSc]) (16/06/2020). Citations of all these search result add up to 714. This however does not mean, that all search results really explicitly used data from ENTSO-E. Some (for sure, for some were found) only mentioned it saying they adoped workflow etc. from the site.
* '''SMARD''': Found 82 results in Advanced Search (ALL=(SMARD)) in Web of Science ([https://apps.webofknowledge.com/ WoSc]) (16/06/2020). However: SMARD is also an acronym for ''Spinal muscular atrophy with respiratory distress (SMARD)''. This somewhat complicates results and needs some further discussion. All 82 search results accumumlated 2359 citations themselves.

== Results for SMILES ==

{| class="wikitable mw-collapsible"
! DC / Non-DC element name
! DC definition ([https://www.dublincore.org/specifications/dublin-core/dces/ DCMI])
! Category (determined with the aid of [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO])
! rowspan = "25" style="background: #ABE" |
! SMILES (ED4)
! Notes (SMILES (ED4) )

|-
! Title
| A name given to the resource
|| Descriptive
|| A
||
|-
! Subject
| The topic of the resource || Descriptive || A ||
|-
! Description
| An account of the resource || Descriptive || PA || The description of the data is very limited. For all files a general description is given. This is linked to the project deliverables but it is not indicated which deliverable are exactly corresponding. This seems to be a time-consuming process for researchers.
|-
! Creator
| An entity primarily responsible for making the resource || Descriptive || A || The metadata file uses the tag "author"
|-
! Publisher
| An entity responsible for making the resource available || Descriptive || A || This information only available under the URL but not in the metadata file
|-
! Contributor
| An entity responsible for making contributions to the resource || Descriptive || NA ||
|-
! Date
| A point or period of time associated with an event in the lifecycle of the resource || Administrative || A || This information only available under the URL but not in the metadata file
|-
! Type
| The nature or genre of the resource || Descriptive || NA || No explicit mention of the type (e.g. document, dataset) although it could be derived from the context
|-
! Format
| The file format, physical medium, or dimensions of the resource || Structural || PA || The metadata element "format" is not exactly indicated but the metadata tag "file" includes the format type at the end (".csv"). Additionally, the tag "datatype" is used to specify the internal data type (e.g. integer, string) of the data file.
|-
! Identifier
| An unambiguous reference to the resource within a given context || Descriptive || NA ||
|-
! Source
| A related resource from which the described resource is derived || Descriptive || NA ||
|-
! Language
| A language of the resource || Descriptive || NA || No explicit mention of the language although it could be derived from the context
|-
! Relation
| A related resource || Descriptive || PA || This information (EU grant information) only generally available under the URL but not in the metadata file
|-
! Coverage
| The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant || Descriptive || NA ||
|-
! Rights
| Information about rights held in and over the resource || Administrative || NA ||

|-
! colspan = "6" style="background: #ABE" |

|-
! Date of availability
| Since when is the data available
|| Impact/Exploitation
|| A
|| Since 2020 (creation and modification are the same date)

|-
! Number of users
| Is there any information on the number of accesses or users?
|| Impact/Exploitation
|| NA
||

|-
! Number of scientific publications where data is cited/used
| Is there any information on the number of citations/uses?
|| Impact/Exploitation
|| NA
|| A rough check in Web of Science indicated zero citations (as of 08/2020). The reason might be the upload date (01/2020).

|-
! Curation activities (is the versioning ongoing)
| Update, maintainance
|| Impact/Exploitation
|| A
|| As the creation and modification date is the same for all data files it is not sure if data files are regularly updated.

|}

Gap analysis

2020-08-31T10:11:17Z

Christopherbs: /* Results for SMILES */

<accesscontrol>Administrators,consortium</accesscontrol>

This is the page for the gap analysis.

= Tacit knowledge "FAIRification and opening of low carbon energy research data" =
 Consortium members can add any time, they feel that something is important to note even though it is not mentioned in a standard deliverable. In other words, this page collects uncodified, tacit knowledge. It will help us later to compile suggestions and lessons learned. This kind of knowledge is collected two-fold:
#Anytime if someone feels that this should be noted. Please write down: Issue, Date, Author (could also be "anonymous"), the issue described in a few words or maximal lines>
#During the final day of workshops

=== Learning process: ===

*People are hesitant to adopt new IT technologies, this is even the case among researchers heavily relying on data, algorithms and collaborative online software (e.g., R, platforms, online conferencing, HPC, ...). The effort to encourage change is not to underestimate. Reasons are several, notably, lack of time and uncertainty about potential benefit as well as overall risk aversion preferences. EERAdata is using the online software "Only Office" to facilitate collaboration (in particular also during the Covid-19 period).
 '''FAIR and open criteria:'''
*Consortium members have a fair understanding of what FAIR/O is, but there is little knowledge and/or technical experience on how to approach the FAIRification and opening. All, however, share the view that we are at a critical point in time, where we need to implement these criteria. 
*To deepen knowledge about FAIR/O criteria, it is useful to test the criteria on a database one is familiar with. For this purpose (and to start brainstorming about the platform), AIT has developed a questionnaire for application in the use cases.
'''Metadata:'''
*A good starting point is to think about metadata and to look into existing metadata concepts in one's field. The first step is to understand that also metadata need to adhere to the FAIR/O principles.
*The next step is to increase knowledge on IT specific terms, i.e. to understand what the difference is between different '''[https://energydata.onlyoffice.eu/Products/Files/doceditor.aspx?fileid=5404340 metadata frameworks] (taxonomy, thesaurus, ontology) as well as classification of metadata (high-level, medium-level, low-level OR administrative, structural and descriptive metadata).  '''WP 2 being in charge of aligning approaches between use cases, participates in all use case kickoffs to bring everybody on the same page. The presentation is linked with "metadata frameworks".
*It is useful to supply consortium members with read aheads and watch aheads on metadata to prepare the '''[https://energydata.onlyoffice.eu/Products/Files/doceditor.aspx?fileid=5296590 first EERAdata workshop]'''. The workshop starts applications and discussions in the use cases break out sessions (and bringing insights back to the plenary), using selected databases.

= FAIR Assessment=

== FAIR Guiding Principles ==
{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ This table shows the FAIR guiding principles described by [https://www.doi.org/10.1038/sdata.2016.18 Wilkinson et al., 2016].

! To be Findable
|
* F1. (meta)data are assigned a globally unique and persistent identifier
* F2. data are described with rich metadata (defined by R1 below)
* F3. metadata clearly and explicitly include the identifier of the data it describes
* F4. (meta)data are registered or indexed in a searchable resource

|-
! To be Accessible
|
* A1. (meta)data are retrievable by their identifier using a standardized communications protocol
** A1.1 the protocol is open, free, and universally implementable
** A1.2 the protocol allows for an authentication and authorization procedure, where necessary
* A2. metadata are accessible, even when the data are no longer available

|-
! To be Interoperable
|
* I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
* I2. (meta)data use vocabularies that follow FAIR principles
* I3. (meta)data include qualified references to other (meta)data

|-
! To be Reusable
|
* R1. meta(data) are richly described with a plurality of accurate and relevant attributes
* R1.1. (meta)data are released with a clear and accessible data usage license
* R1.2. (meta)data are associated with detailed provenance
* R1.3. (meta)data meet domain-relevant community standards

|}

== Identified gaps after Workshop 1 (02/06/2020 – 04/06/2020) ==

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ This table shows a summary of the specific issues that were identified in Workshop 1. The aim was to categorise different issues to get a better understanding of how to tackle these challenges.
! FAIR/O !! Specific FAIR/O !! Gaps !! Gaps for energy domain !! Use case specific gaps !! Consequences for researchers
|-
! F
| F2
|| Metadata scope
|| Different fields require different metadata (potentially very specific)
|| UC3: lack of additional metadata for applications of materials
|| Data are less useful for the specific field
|-
! F
|| F1/F3
|| Identifiers
||
* Missing identifiers on websites
* Identifier not in downloaded data files
||
|| Makes research more difficult.
|-
! I
| I2
|| Taxonomy/ontology/common vocabulary and language issues
||
* Lack of standardisation
* Heterogeneous data makes standardisation hard
* No vocabulary documentation on websites
* Words used for same term in other languages may differ
* Databases in other languages than English
||
|| Research costs more time
|-
! I
| I3
|| References to other (meta)data
||
* No linking to source documents and related publications (for contextual knowledge)
* Possible need for links to other fields
|| UC3: link between microscopic and macroscopic materials (e.g., turbine blades)
||
* Data cannot be connected to the source
* Makes research more difficult.

|-
! R
| R1.1
|| Licensing
||
* No licenses available may mean the data is not reusable for the researcher
* Licenses not clear and accessible
* Obtaining licenses may result in more effort and costs
||
|| Data potentially not reusable

|-
! R
| R1.2
|| Reliability and provenance of data
|| When the data is collected from different and high number of sources, its reliability decreases
||
|| Data potentially not reusable

|-
! O
|
|| Privacy concerns and expected disadvantages
|| Not publishing data due to privacy concerns (sensitive data)
|| UC2: In case of distribution network data this is relevant
|| Data not accessible

|-
! Other
|
|| Conducting FAIR assessments
||
* No qualitative assessment of the data itself (which may be limited)
* Discrepancies between results conducted by humans versus machines
* Sometimes not even DC standards are met
* Metadata is not updated
* Lacking encryption of websites (https)
* Problems assessing whether metadata will be available after data is unavailable
* Interface design/layout may be unclear, incomplete or not intuitive for humans
* Authentication details (user registration login / good or bad, clarify)

||
* UC1 lack of data availability for time-series
||
* Assessment is uncertain (human assessment may need more clarification and understanding)
* Makes research more time-intensive

|}

== FAIR Self-Assessment tool ==

To conduct a human FAIR evaluation for different databases and their respective datasets, different free online tools were considered. Some seemed to be quite useful (e.g. [https://satifyd.dans.knaw.nl/ SATISFYD] ) and some were simple self-evaluation PDFs to be printed and filled out.

We decided to chose the [https://ardc.edu.au/resources/working-with-data/fair-data/fair-self-assessment-tool/ self-assessment tool] provided by the Australian Research Data Commons (ARDC). The decision was based manly upon the fact that the tools questions closely matched the FAIR categories specified by [https://www.doi.org/10.1038/sdata.2016.18 Wilkinson et al., 2016] which would make it easily comparable to the existing preferable FAIR conditions and aid with the repeatability of the results, and the half-automated scoring features that show the extent of the "FAIRness" of the analysed sample as a simple bar chart.

The result of the FAIR assessment is provided as a bar chart where an empty bar chart displays 0 % FAIR compatibility, and a full bar chart represents 100 % FAIR compatibility. The score is therefore not returned as a specific value. During the assessment below, the "Total Across FAIR" in Table ... was estimated according to the level of the bar chart from 0 % to 100 %.
Resulting scores where categorised as follows: Low: < 50%; Medium: 50-60%; High: > 60% .

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ All options for each question of the ARDCs self-assessment tool
!colspan="4"|Findable
!colspan="3"|Accessible
!colspan="3"|Interoperable
!colspan="3"|Reusable
|-
| F1. (meta)data are assigned a globally unique and persistent identifier
|| F2. data are described with rich metadata (defined by R1 below)
|| F3. metadata clearly and explicitly include the identifier of the data it describes
|| F4. (meta)data are registered or indexed in a searchable resource
|| A1. (meta)data are retrievable by their identifier using a standardized communications protocol
|| A1.1 the protocol is open, free, and universally implementable and A1.2 the protocol allows for an authentication and authorization procedure, where necessary
|| A2. metadata are accessible, even when the data are no longer available
|| I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
|| I2. (meta)data use vocabularies that follow FAIR principles
|| I3. (meta)data include qualified references to other (meta)data
|| R1.1. (meta)data are released with a clear and accessible data usage license
|| R1.2. (meta)data are associated with detailed provenance
|| R1.3. (meta)data meet domain-relevant community standards
|-
! Does the dataset have any identifiers assigned?
! Is the dataset identifier included in all metadata records/files describing the data?
! How is the data described with metadata?
! What type of repository or registry is the metadata record in?
! How accessible is the data?
! Is the data available online without requiring specialised protocols or tools once access has been approved?
! Will the metadata record be available even if the data is no longer available?
! What (file) format(s) is the data available in?
! What best describes the types of vocabularies/ontologies/tagging schemas used to define the data elements?
! How is the metadata linked to other data and metadata (to enhance context and clearly indicate relationships)?
! Which of the following best describes the license/usage rights attached to the data?
! How much provenance information has been captured to facilitate data reuse?
!
|-
| Globally unique, citable and persistant (e.g. DOI, PURL, ARK or Handle)
|| Yes
|| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
|| Data is in one place but discoverable through several registries
|| Publicly accessible
|| Standard web service API (e.g. OGC)
|| Yes
|| In a structured, open standard, machine-readable format
|| Standardised open and universal using resolvable global identifiers linking to explainations
|| Meadata is represented in a machine readable format, e.g. in a linked data format such as Resource Description Framework (RDF)
|| Standard machine-readable license (e.g. Creative Commons)
|| Fully recorded in a machine-readable format
||
|-
| Web adress (URL)
|| No
|| Comprehensively, but in a text-based, non-standard format
|| Generalist public repository
|| Fully accessible to person who meet explicitly stated conditions, e.g. ethics approval for sensitive data
|| Non-standard web service (e.g. OpenAPI/Swagger/informal API)
|| No
|| In a structured, open standard, non-machine-readable format
|| Standardised vocabularies/ontologies/schema without global identifiers
|| The metadata record includes URI links to related metadata, data and definitions
|| Standard text based license
|| Fully recorded in a text format
||
|-
| Local identifier
||
|| Brief title and description
|| Domain-specific repository
|| A de-identified / modified subset of the data is publicly accessible
|| File download from online location
|| Unsure
|| Mostly in a proprietary format
|| No standards have been applied in the description of data elements
|| There are no links to other metadata
|| Non-standard machine-readably license (clearly indicating under what conditions the data may be reused)
|| Partially recorded
||
|-
| No identifier
||
|| The data is not described
|| Local institutional repository
|| Embaged access after a specific date
|| By individual arrangement
||
||
|| Data elements not described
||
|| Non-standard text-based license
|| No provenance information is recorded
||
|-
|
||
||
|| The data is not described in any repository
|| Unspecified conditional access e.g. contact the data custodian for access
|| No access to data
||
||
||
||
|| No license
||
||
|-
|
||
||
||
|| Access to metadata only
||
||
||
||
||
||
||
||
|-
|
||
||
||
|| No access to data or metadata
||
||
||
||
||
||
||
||
|}

At this point it is important to note that the assessment of databases and/or datasets using this tool is solely considered a human assessment. This may lead to different evaluations by different users. To counteract this phenomenon, it was suggested that the evaluations should be completed using the 'four-eyes principle' to create more verifiable results. Also, it is planned to additionally evaluate the respective databases using an external tool to perform a machine-assessment.

In addition, not all questions are weighted equally and thus a worse result for one question may not have a large impact whereas only choosing the second-best option in another question will bring down the overall FAIR value considerably.

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ The ARDC tool provides detailed information about the requirements for each of the options of its tool. These descriptions are documented here.
|-
! rowspan="5" | F
|| F
|| About Findable
|| Making data Findable includes assigning a [https://www.ands.org.au/guides/persistent-identifiers-awareness persistent identifier] (like a [https://www.ands.org.au/guides/doi DOI] or [https://www.ands.org.au/guides/identify-my-data Handle] ), having rich [https://www.ands.org.au/working-with-data/metadata metadata] to describe the data and making sure it is findable through disciplinary and generalist discovery portals (local and international).

|-
| F1
|| About identifiers
|| Identifiers are essential for identifying, finding, retrieving, linking and citing datasets. A Web address (URL) can be used to specify the online location of a resource but over time URLs tend to change which leads to broken links to the data. To be useful, identifiers need to be persistent and unique. Digital Object Identifiers (DOIs) and other persistent identifiers (PIDs) provide a permanent citable reference to a particular dataset. The DOI is a permanent fixed reference to the dataset no matter where it is located online and enables citation and citation metrics. Services to create a persistent identifier are often offered by your affiliated institution or the repository you are using to describe your data. Talk to your library service for more information.

Read more: [https://www.ands.org.au/working-with-data/citation-and-identifiers Persistent identifiers]

|-
| F2
|| About identifiers
|| The identifier (preferably a persistent identifier) needs to be clearly stated in the metadata record describing the data collection, and also in any associated data files or metadata.

|-
|F3
|| About metadata descriptions
|| Comprehensive metadata records will include descriptive content that facilitates discovery, access and reuse of the data being described. While there is no 'one size fits all' list, comprehensive metadata should include:
* a globally unique persistent identifier e.g. a DOI
* a title
* related people, i.e. the data creator or custodian
* how to access the data and file formats
* a description of how the data were created and how to interpret the data subject or keywords
* citation information that clearly indicates how the data should be cited
* a machine-readable data licence
* provenance and contextual information such as:
* links to related publications, projects, services and software
* methodology and processes involved in data production
* spatial and temporal coverage (if relevant)
* object-level data description

Providing metadata in a standard schema allows it to be read and used by machines as well as humans.

Read more: [https://www.ands.org.au/working-with-data/metadata Metadata]

|-
| F4
|| About metadata repositories
|| A rich metadata description alone does not ensure a dataset’s ‘findability’ on the internet; the dataset needs to be registered or indexed in a searchable resource, such as a generalist (e.g. [https://figshare.com/ Figshare], [http://datadryad.org/ Dryad], [https://zenodo.org/ Zenodo], domain-specific (e.g. [https://pangaea.de/ PANGAEA], [https://ada.edu.au/ ADA], [https://www.icpsr.umich.edu/ ICPSR]), or institutional (e.g. [https://openresearch-repository.anu.edu.au/ ANU Research Repository] , [https://ses.library.usyd.edu.au/ Sydney eScholarship Repository], [https://data.gov.au/ data.gov.au]) data repository or registry. Ideally these repositories/registries are indexed by search engines such as Google and/or Google Scholar.

|-
! rowspan = "4" | A
| A
|| About Accessible
|| [https://www.ands.org.au/working-with-data/publishing-and-reusing-data/publishing To make data accessible] may include making the data [https://www.ands.org.au/working-with-data/articulating-the-value-of-open-data/open-data open] using a standardised protocol. However the data does not necessarily have to be open. There are sometimes good reasons why data [https://www.ands.org.au/working-with-data/sensitive-data cannot be made open], for example privacy concerns, national security or commercial interests. If it is not open there should be clarity and transparency around the conditions governing access and [https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse reuse].
|-
| A1
|| About data accessibility
|| Not all data that is discoverable can be freely accessed. Often there are embargoes, access controls, and access permissions associated with data due to a variety of issues such as privacy and commercial interests. Even with all these issues much [https://www.ands.org.au/working-with-data/sensitive-data sensitive data] can be shared. Many other issues that could be perceived as blockers to sharing data may be [http://datapub.cdlib.org/closed-data-excuses-excuses overcome].
|-
| A1.1/A1.2
|| About protocols
|| Ideally users would like to retrieve appropriate internet content directly and unhindered once they have located it. Internet protocols (e.g. http and ftp) define rules and conventions for communication between devices, and tools and services are available to facilitate this process, e.g. APIs.

Protocols:
* [https://www.lifewire.com/hypertext-transfer-protocol-817944 HTTP] (Hypertext Transfer Protocol) is the set of rules for transferring files (text, graphic images, sound, video, and other multimedia files) on the [https://whatis.techtarget.com/definition/World-Wide-Web World Wide Web]. As soon as a Web user opens their Web browser, the user is indirectly making use of HTTP.
* [https://www.lifewire.com/file-transfer-protocol-817943 FTP] (File Transfer Protocol) is a standard Internet [http://searchnetworking.techtarget.com/definition/protocol protocol] for transmitting files between computers on the Internet over TCP/IP connections. Read more: [https://www.lifewire.com/definition-of-protocol-network-817949 Web protocols]
* API (Application Programming Interface): When information is made available in any machine readable format, it becomes possible to make that information directly available to programs that request that information over the web. An API is the way this information is made directly available to other machines.
View more: [https://www.youtube.com/watch?v=s7wmiS2mSXY What is an API?]

OGC - http://www.opengeospatial.org/ogc

|-
| A2
|| About metadata availability
|| Effort is often required to maintain data resources online which can often lead to it being neglected especially over long periods of time. This often leads to broken links between the metadata and the data. Having at least a description of the data in the form of a metadata record means there is a record of the data's existence allowing the possibility of someone tracking it down.

|-
! rowspan="4" | I
|| I
|| About Interoperability
|| To be interoperable (i.e. data that is interpretable by a computer, so that they can be automatically combined with other data) the data will need to use community agreed [https://www.ands.org.au/guides/file-formats formats], language and [https://www.ands.org.au/guides/vocabularies-and-research-data vocabularies]. The [https://www.ands.org.au/working-with-data/metadata metadata] will also need to use a community agreed standards and vocabularies, and contain links to related information using [https://www.ands.org.au/working-with-data/citation-and-identifiers identifiers].
|-
|| I1
|| About file formats
|| When selecting file formats for archiving, the formats should ideally be:
* Non-proprietary
* Unencrypted
* Uncompressed
* In common usage by the research community
* Adherent to an open, documented standard, such as described by the State of California (see [http://www.leginfo.ca.gov/pub/07-08/bill/asm/ab_1651-1700/ab_1668_bill_20070524_amended_asm_v96.pdf AB 1668, 2007])
** Interoperable among diverse platforms and applications
** Fully published and available royalty-free
** Fully and independently implementable by multiple software providers on multiple platforms without any intellectual property restrictions for necessary technology
** Developed and maintained by an open standards organization with a well-defined inclusive process for evolution of the standard.

From: [https://library.stanford.edu/research/data-management-services/data-best-practices/best-practices-file-formats Stanford University Library - Best practices for file formats]

File format examples:
* Structured, open standard, machine-readable format e.g. (text) PDF/A, HTML, Plain text, (images) TIFF, JPEG 2000, GIF, (audio) MP3, AIFF, WAVE, (video) MOV, MPEG, AVI, (Tabular data) CSV
* structured, open standard, non-machine-readable format, e.g. PDF, HTML, JPG
* Proprietary format, e.g. doc (Word), .xls (Excel), .ppt (PowerPoint), .sav

Read more:
* [https://www.ands.org.au/__data/assets/pdf_file/0003/731775/File-Formats.pdf ANDS]
* [https://www.europeandataportal.eu/elearning/en/module9/#/id/co-01 European Data Portal]

|-
|| I2
|| About schema standards
|| Schema standards are schemas that have gone through a formal validation process by a standards organisation, such as the International Standards Organisation (ISO) or an equivalent body such as the Dublin Core Metadata Initiative (DCMI); or, commonly used and consistently applied metadata schemas that are well documented, endorsed, and maintained can also become ‘de-facto standards’, e.g. RIF-CS for describing data collections and services used in Research Data Australia.

Read more: [https://www.ands.org.au/guides/metadata-working Metadata schema]

Standardised open and universal schemas, e.g, [https://www.dcc.ac.uk/resources/metadata-standards/datacite-metadata-schema DataCite Metadata Schema], [https://www.dcc.ac.uk/resources/metadata-standards/prov PROV], [https://www.dcc.ac.uk/resources/metadata-standards/dublin-core Dublin Core]

Domain-specific standards, e.g. [http://human-phenotype-ontology.github.io/ HPO - Human Phenotype Ontology], [https://www.nlm.nih.gov/mesh/ MeSH - Medical Subject Headings] , [https://mcp-profile-docs.readthedocs.io/en/stable/ Marine Community Profile], [https://ddialliance.org/ DDI - Data Documentation Initiative].

For more examples of standards: [http://rd-alliance.github.io/metadata-directory/ Here].

Resolvable global identifiers - An identifier is any label used to name something uniquely (whether online or offline). URLs are an example of an identifier. So are serial numbers, and personal names. A persistent identifier is guaranteed to be managed and kept up to date over a defined time period.

Read more: [https://www.ands.org.au/guides/persistent-identifiers-awareness Persistent Identifiers]

|-
||I3
|| About metadata links
|| The goal is to create as many meaningful linkages as possible between (meta)data resources to enrich the contextual knowledge about the data, balanced against the time/energy involved in making a good data model.

'''Machine-readable (meta)data'''

(Meta)data in a format that can be automatically read and processed by a computer, such as [http://opendatahandbook.org/glossary/en/terms/csv/ CSV], [http://opendatahandbook.org/glossary/en/terms/json/ JSON], [http://opendatahandbook.org/glossary/en/terms/xml/ XML] etc. For more information: [http://opendatahandbook.org/glossary/en/terms/machine-readable/ Open Data Handbook]

'''Linked Data'''

Linked Data (also known as Linking Data) can be applied to improve the exploitation of the “Web of data.” The expression refers to the publishing of structured data in a way that typed links are created between data from different sources to provide a higher level of usability. By using Linked Data, it is possible to find other, related data. Structured data should meet four requirements to be called
Linked Data:

* URIs should be assigned to all entities of the dataset.
* HTTP URIs are required to ensure that all entities can be referenced and cited by users and user agents.
* Entities should be described using standard formats such as RDF/XML.
* Links should be created to other, related entity URIs.

All data that fulfil these requirements and are released for the public are called Linked Open Data (LOD). http://www.lesliesikos.com/semantic-web-machine-readable-structured-data-with-meaningful-annotations/

Read more under the heading (meta)data include qualified references to other (meta)data in the following resource: https://www.dtls.nl/fair-data/fair-principles-explained/

For more information on Linked Data standards - RDF - https://www.w3.org/RDF/

Other related linked data standards:
* OWL - https://www.w3.org/OWL/
* SKOS - https://www.w3.org/2004/02/skos/
* SPARQL - https://www.w3.org/TR/rdf-sparql-query/

|-
! rowspan="3" | R
|| R
|| About Reusable
|| Reusable data should maintain its initial richness. For example, it should not be abridged for the purpose of explaining the findings in one particular publication. It needs a clear machine-readable [https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse licence] and [https://www.ands.org.au/partners-and-communities/ands-communities/data-provenance-interest-group provenance] information on how the data was formed. It should also use discipline-specific data and [https://www.ands.org.au/working-with-data/metadata metadata] standards to give it rich contextual information that will allow for accurate interpretation and reuse.

|-
|| R1.1
|| About licenses
|| If data is not licensed no-one else can use it. In Australia, no licence is regarded as the same as 'all rights reserved', confining any reuse to very limited circumstances. Applying a Creative Commons licence to your data is a simple way to ensure that your data can be reused. The less restrictive the licence, the more that can be done with the data.
To make it easy for the Web to know when a work is available under a particular license, a “machine readable” version of the license provides a summary of the key freedoms and obligations written into a format that software systems, search engines, and other kinds of technology can understand.
Example from the [https://creativecommons.org/choose/ Creative Commons License Choser]:

What appears in the metadata:

This work is licensed under a [https://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International License].

Code snippet:

<code>
<a rel="license" href="http://creativecommons.org/licenses/by/4.0/">

<img alt="Creative Commons License" style="border-width:0"

src="https://i.creativecommons.org/l/by/4.0/88x31.png"/>

</a> This work is licensed under a

<a rel="license" href="http://creativecommons.org/licenses/by/4.0/">

Creative Commons Attribution 4.0 International License</a>.
<\code>

Read more: https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse

|-
||R1.2
|| About provenance
|| Data provenance is used to document where a piece of data comes from and the process and methodology by which it is produced. It is important to confirm the authenticity of data enabling trust, credibility and reproducibility. This is becoming increasingly important, especially in the eScience community where research is data intensive and often involves complex data transformations and procedures ([https://www.ands.org.au/partners-and-communities/ands-communities/data-provenance-interest-group Read more]). Provenance vocabularies such as [https://www.w3.org/2005/Incubator/prov/XGR-prov-20101214/#Recommendations Provenir Ontology (PROV-O) and others].

|}

== Results for SMILES and ShareWind ==

{| class="mw-collapsible wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Datasets from the SMILES and ShareWind platforms was analysed using the self-assessment tool provided by ARDC.
! rowspan="5" |
| rowspan="5" |
! Database Code
| rowspan="24" style="background: #ABE" |
| ED4
| ED9
|-
! Database Title
|| SMILES
| ShareWind
|-
! Database Link
|| https://www.ecria-smiles.eu/
| https://sharewind.eu/
|-
! Sample Link
|| [https://www.ecria-smiles.eu/data-files/-/document_library/W32cCAfL2jMX/view_file664466?_com_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX_redirect=https%3A%2F%2Fwww.ecria-smiles.eu%3A443%2Fdata-files%2F-%2Fdocument_library%2FW32cCAfL2jMX%2Fview%2F664423%3F_com_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX_redirect%3Dhttps%253A%252F%252Fwww.ecria-smiles.eu%253A443%252Fdata-files%253Fp_p_id%253Dcom_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX%2526p_p_lifecycle%253D0%2526p_p_state%253Dnormal%2526p_p_mode%253Dview here] (link has 585 characters)
| https://sharewind.eu/records/f9d31abbc2824284b8c1d28894d9619a (data found at https://zenodo.org/record/1162738)
|-
! Sample Size
|| 4 KB
| 37 MB
|-
| colspan="6" style="background: #ABE" |
|-
! rowspan="4" | Findable
| F1. (meta)data are assigned a globally unique and persistent identifier
! Does the dataset have any identifiers assigned?
|| Web adress (URL)
| Globally unique, citable and persistant (e.g. DOI, PURL, ARK or Handle)
|-

| F2. data are described with rich metadata (defined by R1 below)
! Is the dataset identifier included in all metadata records/files describing the data?
|| No
| No
|-

| F3. metadata clearly and explicitly include the identifier of the data it describes
! How is the data described with metadata?
|| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
|-

| F4. (meta)data are registered or indexed in a searchable resource
! What type of repository or registry is the metadata record in?
|| Domain-specific repository
| Data is in one place but discoverable through several registries
|-

! rowspan="3" | Accessible

| A1. (meta)data are retrievable by their identifier using a standardized communications protocol
! How accessible is the data?
|| Publicly accessible
| Publicly accessible
|-
| A1.1 the protocol is open, free, and universally implementable; A1.2 the protocol allows for an authentication and authorization procedure, where necessary
! Is the data available online without requiring specialised protocols or tools once access has been approved?
|| File download from online location
| Standard web service API (e.g. OGC)
|-
| A2. metadata are accessible, even when the data are no longer available
! Will the metadata record be available even if the data is no longer available?
|| Unsure
| Yes
|-

! rowspan="3" | Interoperable
| I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
! What (file) format(s) is the data available in?
|| In a structured, open standard, machine-readable format
|| In a structured, open standard, non-machine-readable format
|-
| I2. (meta)data use vocabularies that follow FAIR principles
! What best describes the types of vocabularies/ontologies/tagging schemas used to define the data elements?
|| Standardised vocabularies/ontologies/schema without global identifiers
|| Standardised vocabularies/ontologies/schema without global identifiers
|-
| I3. (meta)data include qualified references to other (meta)data
! How is the metadata linked to other data and metadata (to enhance context and clearly indicate relationships)?
|| There are no links to other metadata
|| Metadata is represented in a machine readable format, e.g. in a linked data format such as Resource Description Framework (RDF)
|-

! rowspan="3" | Reusable
| R1.1. (meta)data are released with a clear and accessible data usage license
! Which of the following best describes the license/usage rights attached to the data?
|| No license
| Standard machine-readable license (e.g. Creative Commons)
|-
| R1.2. (meta)data are associated with detailed provenance
! How much provenance information has been captured to facilitate data reuse?
|| Partially recorded
|| Partially recorded
|-
| R1.3. (meta)data meet domain-relevant community standards
|
|
|
|-
| colspan="6" style="background: #ABE" |
|-
! Total across F.A.I.R
|
! Low: < 50%; Medium: 50-60%; High: > 60%
|| ~ 45 %
|| ~ 85 %
|-
| colspan="6" style="background: #ABE" |
|-
! Remarks about database
||
||
||
|| The data is provided by the open-source platform Zenodo, not the project website itself. A very positive aspect is that information on different versions of the data as well as the number of views and downloads is available.
|-
! Identified gaps
||
||
||
* No DOI, only URL
* Extremely long URL link spanning several lines with 585 characters, that can easily become corrupted
* No links to other metadata
* No license information given
* Provenance not fully recorded

||
* Dataset identifiers should be included in the files describing the data
* Provenance details are only partially recorded with a note with contact name for further information
|}

= Metadata assessment =

== Dublin Core Elements ==
{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ The DCMI Metadata Terms lists the current set of the Dublin Core vocabulary. Source: [https://dublincore.org/specifications/dublin-core/dcmi-terms/ Dublin Core Metadata Initiative]
| 1 || abstract || 11 || contributor || 21 || extent || 31 || isReplacedBy || 41 || publisher || 51 || tableOfContents
|-
| 2 || accessRights || 12 || coverage || 22 || format || 32 || isRequiredBy || 42 || references || 52 || temporal
|-
| 3 || accrualMethod || 13 || created || 23 || hasFormat || 33 || issued || 43 || relation || 53 || title
|-
| 4 || accrualPeriodicity || 14 || creator || 24 || hasPart || 34 || isVersionOf || 44 || replaces || 54 || type
|-
| 5 || accrualPolicy || 15 || date || 25 || hasVersion || 35 || language || 45 || requires || 55 || valid
|-
| 6 || alternative || 16 || dateAccepted || 26 || identifier || 36 || license || 46 || rights || ||
|-
| 7 || audience || 17 || dateCopyrighted || 27 || instructionalMethod || 37 || mediator || 47 || rightsHolder || ||
|-
| 8 || available || 18 || dateSubmitted || 28 || isFormatOf || 38 || medium || 48 || source || ||
|-
| 9 || bibliographicCitation || 19 || description || 29 || isPartOf || 39 || modified || 49 || spatial || ||
|-
| 10 || conformsTo || 20 || educationLevel || 30 || isReferencedBy || 40 || provenance || 50 || subject || ||
|}

'''Metadata Category Types according to [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO]'''

There are three main types of metadata:
* Descriptive metadata describes a resource for purposes such as discovery and identification. It can include elements such as title, abstract, author, and keywords.
* Structural metadata indicates how compound objects are put together, for example, how pages are ordered to form chapters.
* Administrative metadata provides information to help manage a resource, such as when and how it was created, file type and other technical information, and who can access it. There are several subsets of administrative data; two that sometimes are listed as separate metadata types are:
** Rights management meta-data, which deals with intellectual property rights,and
** Preservation metadata, which contains information needed to archive and preserve a resource.
''

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Simple Dublin Core Elements. 15 DC elements with their (shortened) official definitions and examples.
! DC element name !! DC definition ([https://www.dublincore.org/specifications/dublin-core/dces/ DCMI]) !! Example (Energy Domain) !! Category (determined with the aid of [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO])
|-
! Title
| A name given to the resource || Outdoor monitoring of a hybrid micro-CPV solar panel with integrated micro-tracking and diffuse capture || Descriptive
|-
! Subject
| The topic of the resource || concentrator photovoltaics; CPV; rooftop photovoltaics; integrated tracking || Descriptive
|-
! Description
| An account of the resource || Dataset from the outdoor characterization of a B Series module from Insolight at the rooftop of the Instituto de Energía Solar - Universidad Politécnica de Madrid. …. || Descriptive
|-
! Creator
| An entity primarily responsible for making the resource || Stephen Askins; Gaël Nardin || Descriptive
|-
! Publisher
| An entity responsible for making the resource available || Stephen Askins || Descriptive
|-
! Contributor
| An entity responsible for making contributions to the resource || Stephen Askins || Descriptive
|-
! Date
| A point or period of time associated with an event in the lifecycle of the resource || 2019-07-23; Date will be associated with the creation or availability of the resource. || Administrative
|-
! Type
| The nature or genre of the resource || info:eu-repo/semantics/other; dataset || Descriptive
|-
! Format
| The file format, physical medium, or dimensions of the resource || image
|| Structural
|-
! Identifier
| An unambiguous reference to the resource within a given context || https://zenodo.org/record/3346823; 10.5281/zenodo.3346823; oai:zenodo.org:3346823 || Descriptive
|-
! Source
| A related resource from which the described resource is derived || RC607.A26W574 1996 [where "RC607.A26W574 1996" is the call number of the print version of the resource, from which the present version was scanned] || Descriptive
|-
! Language
| A language of the resource || eng || Descriptive
|-
! Relation
| A related resource || info:eu-repo/grantAgreement/EC/H2020/787289/ || Descriptive
|-
! Coverage
| The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant || 1995-1996; Madrid, Spain || Descriptive
|-
! Rights
| Information about rights held in and over the resource || info:eu-repo/semantics/openAccess; https://creativecommons.org/licenses/by/4.0/legalcode || Administrative
|}

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Non-DC elements

! Non-DC element name !! Definition !! Example !! Category
|-
! Date of availability
| Since when is the data available
||
|| Impact/Exploitation

|-
! Number of users
| Is there any information on the number of accesses or users?
||
|| Impact/Exploitation

|-
! Number of scientific publications where data is cited/used
| Is there any information on the number of citations/uses?
||
|| Impact/Exploitation

|-
! Curation activities (is the versioning ongoing)
| Update, maintainance
||
|| Impact/Exploitation
|}

Attempting to put a value on Impact/Exploitation:
* '''ENTSO-E''': Found 172 results in Advanced Search (ALL) in Web of Science ([https://apps.webofknowledge.com/ WoSc]) (16/06/2020). Citations of all these search result add up to 714. This however does not mean, that all search results really explicitly used data from ENTSO-E. Some (for sure, for some were found) only mentioned it saying they adoped workflow etc. from the site.
* '''SMARD''': Found 82 results in Advanced Search (ALL=(SMARD)) in Web of Science ([https://apps.webofknowledge.com/ WoSc]) (16/06/2020). However: SMARD is also an acronym for ''Spinal muscular atrophy with respiratory distress (SMARD)''. This somewhat complicates results and needs some further discussion. All 82 search results accumumlated 2359 citations themselves.

== Results for SMILES ==

{| class="wikitable mw-collapsible"
! DC / Non-DC element name
! DC definition ([https://www.dublincore.org/specifications/dublin-core/dces/ DCMI])
! Category (determined with the aid of [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO])
! rowspan = "25" style="background: #ABE" |
! SMILES (ED4)
! Notes (SMILES (ED4) )

|-
! Title
| A name given to the resource
|| Descriptive
|| A
||
|-
! Subject
| The topic of the resource || Descriptive || A ||
|-
! Description
| An account of the resource || Descriptive || PA || The description of the data is very limited. For all files a general description is given. This is linked to the project deliverables but it is not indicated which deliverable are exactly corresponding. This seems to be a time-consuming process for researchers.
|-
! Creator
| An entity primarily responsible for making the resource || Descriptive || A || The metadata file uses the tag "author"
|-
! Publisher
| An entity responsible for making the resource available || Descriptive || A || This information only available under the URL but not in the metadata file
|-
! Contributor
| An entity responsible for making contributions to the resource || Descriptive || NA ||
|-
! Date
| A point or period of time associated with an event in the lifecycle of the resource || Administrative || A || This information only available under the URL but not in the metadata file
|-
! Type
| The nature or genre of the resource || Descriptive || NA || No explicit mention of the type (e.g. document, dataset) although it could be derived from the context
|-
! Format
| The file format, physical medium, or dimensions of the resource || Structural || PA || The metadata element "format" is not exactly indicated but the metadata tag "file" includes the format type at the end (".csv"). Additionally, the tag "datatype" is used to specify the internal data type (e.g. integer, string) of the data file.
|-
! Identifier
| An unambiguous reference to the resource within a given context || Descriptive || NA ||
|-
! Source
| A related resource from which the described resource is derived || Descriptive || NA ||
|-
! Language
| A language of the resource || Descriptive || NA || No explicit mention of the language although it could be derived from the context
|-
! Relation
| A related resource || Descriptive || PA || This information (EU grant information) only generally available under the URL but not in the metadata file
|-
! Coverage
| The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant || Descriptive || NA ||
|-
! Rights
| Information about rights held in and over the resource || Administrative || NA ||

|-
! colspan = "6" style="background: #ABE" |

|-
! Date of availability
| Since when is the data available
|| Impact/Exploitation
|| A
|| Since 2020 (creation and modification are the same date)

|-
! Number of users
| Is there any information on the number of accesses or users?
|| Impact/Exploitation
|| NA
||

|-
! Number of scientific publications where data is cited/used
| Is there any information on the number of citations/uses?
|| Impact/Exploitation
|| NA
|| A rough check in Web of Science indicated zero citations (as of 08/2020). The reason might be the date the upload (01/2020).

|-
! Curation activities (is the versioning ongoing)
| Update, maintainance
|| Impact/Exploitation
|| A
|| As the creation and modification date is the same for all data files it is not sure if data files are regularly updated.

|}

Gap analysis

2020-08-31T09:58:48Z

Christopherbs: /* Results for SMILES */

<accesscontrol>Administrators,consortium</accesscontrol>

This is the page for the gap analysis.

= Tacit knowledge "FAIRification and opening of low carbon energy research data" =
 Consortium members can add any time, they feel that something is important to note even though it is not mentioned in a standard deliverable. In other words, this page collects uncodified, tacit knowledge. It will help us later to compile suggestions and lessons learned. This kind of knowledge is collected two-fold:
#Anytime if someone feels that this should be noted. Please write down: Issue, Date, Author (could also be "anonymous"), the issue described in a few words or maximal lines>
#During the final day of workshops

=== Learning process: ===

*People are hesitant to adopt new IT technologies, this is even the case among researchers heavily relying on data, algorithms and collaborative online software (e.g., R, platforms, online conferencing, HPC, ...). The effort to encourage change is not to underestimate. Reasons are several, notably, lack of time and uncertainty about potential benefit as well as overall risk aversion preferences. EERAdata is using the online software "Only Office" to facilitate collaboration (in particular also during the Covid-19 period).
 '''FAIR and open criteria:'''
*Consortium members have a fair understanding of what FAIR/O is, but there is little knowledge and/or technical experience on how to approach the FAIRification and opening. All, however, share the view that we are at a critical point in time, where we need to implement these criteria. 
*To deepen knowledge about FAIR/O criteria, it is useful to test the criteria on a database one is familiar with. For this purpose (and to start brainstorming about the platform), AIT has developed a questionnaire for application in the use cases.
'''Metadata:'''
*A good starting point is to think about metadata and to look into existing metadata concepts in one's field. The first step is to understand that also metadata need to adhere to the FAIR/O principles.
*The next step is to increase knowledge on IT specific terms, i.e. to understand what the difference is between different '''[https://energydata.onlyoffice.eu/Products/Files/doceditor.aspx?fileid=5404340 metadata frameworks] (taxonomy, thesaurus, ontology) as well as classification of metadata (high-level, medium-level, low-level OR administrative, structural and descriptive metadata).  '''WP 2 being in charge of aligning approaches between use cases, participates in all use case kickoffs to bring everybody on the same page. The presentation is linked with "metadata frameworks".
*It is useful to supply consortium members with read aheads and watch aheads on metadata to prepare the '''[https://energydata.onlyoffice.eu/Products/Files/doceditor.aspx?fileid=5296590 first EERAdata workshop]'''. The workshop starts applications and discussions in the use cases break out sessions (and bringing insights back to the plenary), using selected databases.

= FAIR Assessment=

== FAIR Guiding Principles ==
{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ This table shows the FAIR guiding principles described by [https://www.doi.org/10.1038/sdata.2016.18 Wilkinson et al., 2016].

! To be Findable
|
* F1. (meta)data are assigned a globally unique and persistent identifier
* F2. data are described with rich metadata (defined by R1 below)
* F3. metadata clearly and explicitly include the identifier of the data it describes
* F4. (meta)data are registered or indexed in a searchable resource

|-
! To be Accessible
|
* A1. (meta)data are retrievable by their identifier using a standardized communications protocol
** A1.1 the protocol is open, free, and universally implementable
** A1.2 the protocol allows for an authentication and authorization procedure, where necessary
* A2. metadata are accessible, even when the data are no longer available

|-
! To be Interoperable
|
* I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
* I2. (meta)data use vocabularies that follow FAIR principles
* I3. (meta)data include qualified references to other (meta)data

|-
! To be Reusable
|
* R1. meta(data) are richly described with a plurality of accurate and relevant attributes
* R1.1. (meta)data are released with a clear and accessible data usage license
* R1.2. (meta)data are associated with detailed provenance
* R1.3. (meta)data meet domain-relevant community standards

|}

== Identified gaps after Workshop 1 (02/06/2020 – 04/06/2020) ==

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ This table shows a summary of the specific issues that were identified in Workshop 1. The aim was to categorise different issues to get a better understanding of how to tackle these challenges.
! FAIR/O !! Specific FAIR/O !! Gaps !! Gaps for energy domain !! Use case specific gaps !! Consequences for researchers
|-
! F
| F2
|| Metadata scope
|| Different fields require different metadata (potentially very specific)
|| UC3: lack of additional metadata for applications of materials
|| Data are less useful for the specific field
|-
! F
|| F1/F3
|| Identifiers
||
* Missing identifiers on websites
* Identifier not in downloaded data files
||
|| Makes research more difficult.
|-
! I
| I2
|| Taxonomy/ontology/common vocabulary and language issues
||
* Lack of standardisation
* Heterogeneous data makes standardisation hard
* No vocabulary documentation on websites
* Words used for same term in other languages may differ
* Databases in other languages than English
||
|| Research costs more time
|-
! I
| I3
|| References to other (meta)data
||
* No linking to source documents and related publications (for contextual knowledge)
* Possible need for links to other fields
|| UC3: link between microscopic and macroscopic materials (e.g., turbine blades)
||
* Data cannot be connected to the source
* Makes research more difficult.

|-
! R
| R1.1
|| Licensing
||
* No licenses available may mean the data is not reusable for the researcher
* Licenses not clear and accessible
* Obtaining licenses may result in more effort and costs
||
|| Data potentially not reusable

|-
! R
| R1.2
|| Reliability and provenance of data
|| When the data is collected from different and high number of sources, its reliability decreases
||
|| Data potentially not reusable

|-
! O
|
|| Privacy concerns and expected disadvantages
|| Not publishing data due to privacy concerns (sensitive data)
|| UC2: In case of distribution network data this is relevant
|| Data not accessible

|-
! Other
|
|| Conducting FAIR assessments
||
* No qualitative assessment of the data itself (which may be limited)
* Discrepancies between results conducted by humans versus machines
* Sometimes not even DC standards are met
* Metadata is not updated
* Lacking encryption of websites (https)
* Problems assessing whether metadata will be available after data is unavailable
* Interface design/layout may be unclear, incomplete or not intuitive for humans
* Authentication details (user registration login / good or bad, clarify)

||
* UC1 lack of data availability for time-series
||
* Assessment is uncertain (human assessment may need more clarification and understanding)
* Makes research more time-intensive

|}

== FAIR Self-Assessment tool ==

To conduct a human FAIR evaluation for different databases and their respective datasets, different free online tools were considered. Some seemed to be quite useful (e.g. [https://satifyd.dans.knaw.nl/ SATISFYD] ) and some were simple self-evaluation PDFs to be printed and filled out.

We decided to chose the [https://ardc.edu.au/resources/working-with-data/fair-data/fair-self-assessment-tool/ self-assessment tool] provided by the Australian Research Data Commons (ARDC). The decision was based manly upon the fact that the tools questions closely matched the FAIR categories specified by [https://www.doi.org/10.1038/sdata.2016.18 Wilkinson et al., 2016] which would make it easily comparable to the existing preferable FAIR conditions and aid with the repeatability of the results, and the half-automated scoring features that show the extent of the "FAIRness" of the analysed sample as a simple bar chart.

The result of the FAIR assessment is provided as a bar chart where an empty bar chart displays 0 % FAIR compatibility, and a full bar chart represents 100 % FAIR compatibility. The score is therefore not returned as a specific value. During the assessment below, the "Total Across FAIR" in Table ... was estimated according to the level of the bar chart from 0 % to 100 %.
Resulting scores where categorised as follows: Low: < 50%; Medium: 50-60%; High: > 60% .

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ All options for each question of the ARDCs self-assessment tool
!colspan="4"|Findable
!colspan="3"|Accessible
!colspan="3"|Interoperable
!colspan="3"|Reusable
|-
| F1. (meta)data are assigned a globally unique and persistent identifier
|| F2. data are described with rich metadata (defined by R1 below)
|| F3. metadata clearly and explicitly include the identifier of the data it describes
|| F4. (meta)data are registered or indexed in a searchable resource
|| A1. (meta)data are retrievable by their identifier using a standardized communications protocol
|| A1.1 the protocol is open, free, and universally implementable and A1.2 the protocol allows for an authentication and authorization procedure, where necessary
|| A2. metadata are accessible, even when the data are no longer available
|| I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
|| I2. (meta)data use vocabularies that follow FAIR principles
|| I3. (meta)data include qualified references to other (meta)data
|| R1.1. (meta)data are released with a clear and accessible data usage license
|| R1.2. (meta)data are associated with detailed provenance
|| R1.3. (meta)data meet domain-relevant community standards
|-
! Does the dataset have any identifiers assigned?
! Is the dataset identifier included in all metadata records/files describing the data?
! How is the data described with metadata?
! What type of repository or registry is the metadata record in?
! How accessible is the data?
! Is the data available online without requiring specialised protocols or tools once access has been approved?
! Will the metadata record be available even if the data is no longer available?
! What (file) format(s) is the data available in?
! What best describes the types of vocabularies/ontologies/tagging schemas used to define the data elements?
! How is the metadata linked to other data and metadata (to enhance context and clearly indicate relationships)?
! Which of the following best describes the license/usage rights attached to the data?
! How much provenance information has been captured to facilitate data reuse?
!
|-
| Globally unique, citable and persistant (e.g. DOI, PURL, ARK or Handle)
|| Yes
|| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
|| Data is in one place but discoverable through several registries
|| Publicly accessible
|| Standard web service API (e.g. OGC)
|| Yes
|| In a structured, open standard, machine-readable format
|| Standardised open and universal using resolvable global identifiers linking to explainations
|| Meadata is represented in a machine readable format, e.g. in a linked data format such as Resource Description Framework (RDF)
|| Standard machine-readable license (e.g. Creative Commons)
|| Fully recorded in a machine-readable format
||
|-
| Web adress (URL)
|| No
|| Comprehensively, but in a text-based, non-standard format
|| Generalist public repository
|| Fully accessible to person who meet explicitly stated conditions, e.g. ethics approval for sensitive data
|| Non-standard web service (e.g. OpenAPI/Swagger/informal API)
|| No
|| In a structured, open standard, non-machine-readable format
|| Standardised vocabularies/ontologies/schema without global identifiers
|| The metadata record includes URI links to related metadata, data and definitions
|| Standard text based license
|| Fully recorded in a text format
||
|-
| Local identifier
||
|| Brief title and description
|| Domain-specific repository
|| A de-identified / modified subset of the data is publicly accessible
|| File download from online location
|| Unsure
|| Mostly in a proprietary format
|| No standards have been applied in the description of data elements
|| There are no links to other metadata
|| Non-standard machine-readably license (clearly indicating under what conditions the data may be reused)
|| Partially recorded
||
|-
| No identifier
||
|| The data is not described
|| Local institutional repository
|| Embaged access after a specific date
|| By individual arrangement
||
||
|| Data elements not described
||
|| Non-standard text-based license
|| No provenance information is recorded
||
|-
|
||
||
|| The data is not described in any repository
|| Unspecified conditional access e.g. contact the data custodian for access
|| No access to data
||
||
||
||
|| No license
||
||
|-
|
||
||
||
|| Access to metadata only
||
||
||
||
||
||
||
||
|-
|
||
||
||
|| No access to data or metadata
||
||
||
||
||
||
||
||
|}

At this point it is important to note that the assessment of databases and/or datasets using this tool is solely considered a human assessment. This may lead to different evaluations by different users. To counteract this phenomenon, it was suggested that the evaluations should be completed using the 'four-eyes principle' to create more verifiable results. Also, it is planned to additionally evaluate the respective databases using an external tool to perform a machine-assessment.

In addition, not all questions are weighted equally and thus a worse result for one question may not have a large impact whereas only choosing the second-best option in another question will bring down the overall FAIR value considerably.

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ The ARDC tool provides detailed information about the requirements for each of the options of its tool. These descriptions are documented here.
|-
! rowspan="5" | F
|| F
|| About Findable
|| Making data Findable includes assigning a [https://www.ands.org.au/guides/persistent-identifiers-awareness persistent identifier] (like a [https://www.ands.org.au/guides/doi DOI] or [https://www.ands.org.au/guides/identify-my-data Handle] ), having rich [https://www.ands.org.au/working-with-data/metadata metadata] to describe the data and making sure it is findable through disciplinary and generalist discovery portals (local and international).

|-
| F1
|| About identifiers
|| Identifiers are essential for identifying, finding, retrieving, linking and citing datasets. A Web address (URL) can be used to specify the online location of a resource but over time URLs tend to change which leads to broken links to the data. To be useful, identifiers need to be persistent and unique. Digital Object Identifiers (DOIs) and other persistent identifiers (PIDs) provide a permanent citable reference to a particular dataset. The DOI is a permanent fixed reference to the dataset no matter where it is located online and enables citation and citation metrics. Services to create a persistent identifier are often offered by your affiliated institution or the repository you are using to describe your data. Talk to your library service for more information.

Read more: [https://www.ands.org.au/working-with-data/citation-and-identifiers Persistent identifiers]

|-
| F2
|| About identifiers
|| The identifier (preferably a persistent identifier) needs to be clearly stated in the metadata record describing the data collection, and also in any associated data files or metadata.

|-
|F3
|| About metadata descriptions
|| Comprehensive metadata records will include descriptive content that facilitates discovery, access and reuse of the data being described. While there is no 'one size fits all' list, comprehensive metadata should include:
* a globally unique persistent identifier e.g. a DOI
* a title
* related people, i.e. the data creator or custodian
* how to access the data and file formats
* a description of how the data were created and how to interpret the data subject or keywords
* citation information that clearly indicates how the data should be cited
* a machine-readable data licence
* provenance and contextual information such as:
* links to related publications, projects, services and software
* methodology and processes involved in data production
* spatial and temporal coverage (if relevant)
* object-level data description

Providing metadata in a standard schema allows it to be read and used by machines as well as humans.

Read more: [https://www.ands.org.au/working-with-data/metadata Metadata]

|-
| F4
|| About metadata repositories
|| A rich metadata description alone does not ensure a dataset’s ‘findability’ on the internet; the dataset needs to be registered or indexed in a searchable resource, such as a generalist (e.g. [https://figshare.com/ Figshare], [http://datadryad.org/ Dryad], [https://zenodo.org/ Zenodo], domain-specific (e.g. [https://pangaea.de/ PANGAEA], [https://ada.edu.au/ ADA], [https://www.icpsr.umich.edu/ ICPSR]), or institutional (e.g. [https://openresearch-repository.anu.edu.au/ ANU Research Repository] , [https://ses.library.usyd.edu.au/ Sydney eScholarship Repository], [https://data.gov.au/ data.gov.au]) data repository or registry. Ideally these repositories/registries are indexed by search engines such as Google and/or Google Scholar.

|-
! rowspan = "4" | A
| A
|| About Accessible
|| [https://www.ands.org.au/working-with-data/publishing-and-reusing-data/publishing To make data accessible] may include making the data [https://www.ands.org.au/working-with-data/articulating-the-value-of-open-data/open-data open] using a standardised protocol. However the data does not necessarily have to be open. There are sometimes good reasons why data [https://www.ands.org.au/working-with-data/sensitive-data cannot be made open], for example privacy concerns, national security or commercial interests. If it is not open there should be clarity and transparency around the conditions governing access and [https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse reuse].
|-
| A1
|| About data accessibility
|| Not all data that is discoverable can be freely accessed. Often there are embargoes, access controls, and access permissions associated with data due to a variety of issues such as privacy and commercial interests. Even with all these issues much [https://www.ands.org.au/working-with-data/sensitive-data sensitive data] can be shared. Many other issues that could be perceived as blockers to sharing data may be [http://datapub.cdlib.org/closed-data-excuses-excuses overcome].
|-
| A1.1/A1.2
|| About protocols
|| Ideally users would like to retrieve appropriate internet content directly and unhindered once they have located it. Internet protocols (e.g. http and ftp) define rules and conventions for communication between devices, and tools and services are available to facilitate this process, e.g. APIs.

Protocols:
* [https://www.lifewire.com/hypertext-transfer-protocol-817944 HTTP] (Hypertext Transfer Protocol) is the set of rules for transferring files (text, graphic images, sound, video, and other multimedia files) on the [https://whatis.techtarget.com/definition/World-Wide-Web World Wide Web]. As soon as a Web user opens their Web browser, the user is indirectly making use of HTTP.
* [https://www.lifewire.com/file-transfer-protocol-817943 FTP] (File Transfer Protocol) is a standard Internet [http://searchnetworking.techtarget.com/definition/protocol protocol] for transmitting files between computers on the Internet over TCP/IP connections. Read more: [https://www.lifewire.com/definition-of-protocol-network-817949 Web protocols]
* API (Application Programming Interface): When information is made available in any machine readable format, it becomes possible to make that information directly available to programs that request that information over the web. An API is the way this information is made directly available to other machines.
View more: [https://www.youtube.com/watch?v=s7wmiS2mSXY What is an API?]

OGC - http://www.opengeospatial.org/ogc

|-
| A2
|| About metadata availability
|| Effort is often required to maintain data resources online which can often lead to it being neglected especially over long periods of time. This often leads to broken links between the metadata and the data. Having at least a description of the data in the form of a metadata record means there is a record of the data's existence allowing the possibility of someone tracking it down.

|-
! rowspan="4" | I
|| I
|| About Interoperability
|| To be interoperable (i.e. data that is interpretable by a computer, so that they can be automatically combined with other data) the data will need to use community agreed [https://www.ands.org.au/guides/file-formats formats], language and [https://www.ands.org.au/guides/vocabularies-and-research-data vocabularies]. The [https://www.ands.org.au/working-with-data/metadata metadata] will also need to use a community agreed standards and vocabularies, and contain links to related information using [https://www.ands.org.au/working-with-data/citation-and-identifiers identifiers].
|-
|| I1
|| About file formats
|| When selecting file formats for archiving, the formats should ideally be:
* Non-proprietary
* Unencrypted
* Uncompressed
* In common usage by the research community
* Adherent to an open, documented standard, such as described by the State of California (see [http://www.leginfo.ca.gov/pub/07-08/bill/asm/ab_1651-1700/ab_1668_bill_20070524_amended_asm_v96.pdf AB 1668, 2007])
** Interoperable among diverse platforms and applications
** Fully published and available royalty-free
** Fully and independently implementable by multiple software providers on multiple platforms without any intellectual property restrictions for necessary technology
** Developed and maintained by an open standards organization with a well-defined inclusive process for evolution of the standard.

From: [https://library.stanford.edu/research/data-management-services/data-best-practices/best-practices-file-formats Stanford University Library - Best practices for file formats]

File format examples:
* Structured, open standard, machine-readable format e.g. (text) PDF/A, HTML, Plain text, (images) TIFF, JPEG 2000, GIF, (audio) MP3, AIFF, WAVE, (video) MOV, MPEG, AVI, (Tabular data) CSV
* structured, open standard, non-machine-readable format, e.g. PDF, HTML, JPG
* Proprietary format, e.g. doc (Word), .xls (Excel), .ppt (PowerPoint), .sav

Read more:
* [https://www.ands.org.au/__data/assets/pdf_file/0003/731775/File-Formats.pdf ANDS]
* [https://www.europeandataportal.eu/elearning/en/module9/#/id/co-01 European Data Portal]

|-
|| I2
|| About schema standards
|| Schema standards are schemas that have gone through a formal validation process by a standards organisation, such as the International Standards Organisation (ISO) or an equivalent body such as the Dublin Core Metadata Initiative (DCMI); or, commonly used and consistently applied metadata schemas that are well documented, endorsed, and maintained can also become ‘de-facto standards’, e.g. RIF-CS for describing data collections and services used in Research Data Australia.

Read more: [https://www.ands.org.au/guides/metadata-working Metadata schema]

Standardised open and universal schemas, e.g, [https://www.dcc.ac.uk/resources/metadata-standards/datacite-metadata-schema DataCite Metadata Schema], [https://www.dcc.ac.uk/resources/metadata-standards/prov PROV], [https://www.dcc.ac.uk/resources/metadata-standards/dublin-core Dublin Core]

Domain-specific standards, e.g. [http://human-phenotype-ontology.github.io/ HPO - Human Phenotype Ontology], [https://www.nlm.nih.gov/mesh/ MeSH - Medical Subject Headings] , [https://mcp-profile-docs.readthedocs.io/en/stable/ Marine Community Profile], [https://ddialliance.org/ DDI - Data Documentation Initiative].

For more examples of standards: [http://rd-alliance.github.io/metadata-directory/ Here].

Resolvable global identifiers - An identifier is any label used to name something uniquely (whether online or offline). URLs are an example of an identifier. So are serial numbers, and personal names. A persistent identifier is guaranteed to be managed and kept up to date over a defined time period.

Read more: [https://www.ands.org.au/guides/persistent-identifiers-awareness Persistent Identifiers]

|-
||I3
|| About metadata links
|| The goal is to create as many meaningful linkages as possible between (meta)data resources to enrich the contextual knowledge about the data, balanced against the time/energy involved in making a good data model.

'''Machine-readable (meta)data'''

(Meta)data in a format that can be automatically read and processed by a computer, such as [http://opendatahandbook.org/glossary/en/terms/csv/ CSV], [http://opendatahandbook.org/glossary/en/terms/json/ JSON], [http://opendatahandbook.org/glossary/en/terms/xml/ XML] etc. For more information: [http://opendatahandbook.org/glossary/en/terms/machine-readable/ Open Data Handbook]

'''Linked Data'''

Linked Data (also known as Linking Data) can be applied to improve the exploitation of the “Web of data.” The expression refers to the publishing of structured data in a way that typed links are created between data from different sources to provide a higher level of usability. By using Linked Data, it is possible to find other, related data. Structured data should meet four requirements to be called
Linked Data:

* URIs should be assigned to all entities of the dataset.
* HTTP URIs are required to ensure that all entities can be referenced and cited by users and user agents.
* Entities should be described using standard formats such as RDF/XML.
* Links should be created to other, related entity URIs.

All data that fulfil these requirements and are released for the public are called Linked Open Data (LOD). http://www.lesliesikos.com/semantic-web-machine-readable-structured-data-with-meaningful-annotations/

Read more under the heading (meta)data include qualified references to other (meta)data in the following resource: https://www.dtls.nl/fair-data/fair-principles-explained/

For more information on Linked Data standards - RDF - https://www.w3.org/RDF/

Other related linked data standards:
* OWL - https://www.w3.org/OWL/
* SKOS - https://www.w3.org/2004/02/skos/
* SPARQL - https://www.w3.org/TR/rdf-sparql-query/

|-
! rowspan="3" | R
|| R
|| About Reusable
|| Reusable data should maintain its initial richness. For example, it should not be abridged for the purpose of explaining the findings in one particular publication. It needs a clear machine-readable [https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse licence] and [https://www.ands.org.au/partners-and-communities/ands-communities/data-provenance-interest-group provenance] information on how the data was formed. It should also use discipline-specific data and [https://www.ands.org.au/working-with-data/metadata metadata] standards to give it rich contextual information that will allow for accurate interpretation and reuse.

|-
|| R1.1
|| About licenses
|| If data is not licensed no-one else can use it. In Australia, no licence is regarded as the same as 'all rights reserved', confining any reuse to very limited circumstances. Applying a Creative Commons licence to your data is a simple way to ensure that your data can be reused. The less restrictive the licence, the more that can be done with the data.
To make it easy for the Web to know when a work is available under a particular license, a “machine readable” version of the license provides a summary of the key freedoms and obligations written into a format that software systems, search engines, and other kinds of technology can understand.
Example from the [https://creativecommons.org/choose/ Creative Commons License Choser]:

What appears in the metadata:

This work is licensed under a [https://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International License].

Code snippet:

<code>
<a rel="license" href="http://creativecommons.org/licenses/by/4.0/">

<img alt="Creative Commons License" style="border-width:0"

src="https://i.creativecommons.org/l/by/4.0/88x31.png"/>

</a> This work is licensed under a

<a rel="license" href="http://creativecommons.org/licenses/by/4.0/">

Creative Commons Attribution 4.0 International License</a>.
<\code>

Read more: https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse

|-
||R1.2
|| About provenance
|| Data provenance is used to document where a piece of data comes from and the process and methodology by which it is produced. It is important to confirm the authenticity of data enabling trust, credibility and reproducibility. This is becoming increasingly important, especially in the eScience community where research is data intensive and often involves complex data transformations and procedures ([https://www.ands.org.au/partners-and-communities/ands-communities/data-provenance-interest-group Read more]). Provenance vocabularies such as [https://www.w3.org/2005/Incubator/prov/XGR-prov-20101214/#Recommendations Provenir Ontology (PROV-O) and others].

|}

== Results for SMILES and ShareWind ==

{| class="mw-collapsible wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Datasets from the SMILES and ShareWind platforms was analysed using the self-assessment tool provided by ARDC.
! rowspan="5" |
| rowspan="5" |
! Database Code
| rowspan="24" style="background: #ABE" |
| ED4
| ED9
|-
! Database Title
|| SMILES
| ShareWind
|-
! Database Link
|| https://www.ecria-smiles.eu/
| https://sharewind.eu/
|-
! Sample Link
|| [https://www.ecria-smiles.eu/data-files/-/document_library/W32cCAfL2jMX/view_file664466?_com_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX_redirect=https%3A%2F%2Fwww.ecria-smiles.eu%3A443%2Fdata-files%2F-%2Fdocument_library%2FW32cCAfL2jMX%2Fview%2F664423%3F_com_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX_redirect%3Dhttps%253A%252F%252Fwww.ecria-smiles.eu%253A443%252Fdata-files%253Fp_p_id%253Dcom_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX%2526p_p_lifecycle%253D0%2526p_p_state%253Dnormal%2526p_p_mode%253Dview here] (link has 585 characters)
| https://sharewind.eu/records/f9d31abbc2824284b8c1d28894d9619a (data found at https://zenodo.org/record/1162738)
|-
! Sample Size
|| 4 KB
| 37 MB
|-
| colspan="6" style="background: #ABE" |
|-
! rowspan="4" | Findable
| F1. (meta)data are assigned a globally unique and persistent identifier
! Does the dataset have any identifiers assigned?
|| Web adress (URL)
| Globally unique, citable and persistant (e.g. DOI, PURL, ARK or Handle)
|-

| F2. data are described with rich metadata (defined by R1 below)
! Is the dataset identifier included in all metadata records/files describing the data?
|| No
| No
|-

| F3. metadata clearly and explicitly include the identifier of the data it describes
! How is the data described with metadata?
|| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
|-

| F4. (meta)data are registered or indexed in a searchable resource
! What type of repository or registry is the metadata record in?
|| Domain-specific repository
| Data is in one place but discoverable through several registries
|-

! rowspan="3" | Accessible

| A1. (meta)data are retrievable by their identifier using a standardized communications protocol
! How accessible is the data?
|| Publicly accessible
| Publicly accessible
|-
| A1.1 the protocol is open, free, and universally implementable; A1.2 the protocol allows for an authentication and authorization procedure, where necessary
! Is the data available online without requiring specialised protocols or tools once access has been approved?
|| File download from online location
| Standard web service API (e.g. OGC)
|-
| A2. metadata are accessible, even when the data are no longer available
! Will the metadata record be available even if the data is no longer available?
|| Unsure
| Yes
|-

! rowspan="3" | Interoperable
| I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
! What (file) format(s) is the data available in?
|| In a structured, open standard, machine-readable format
|| In a structured, open standard, non-machine-readable format
|-
| I2. (meta)data use vocabularies that follow FAIR principles
! What best describes the types of vocabularies/ontologies/tagging schemas used to define the data elements?
|| Standardised vocabularies/ontologies/schema without global identifiers
|| Standardised vocabularies/ontologies/schema without global identifiers
|-
| I3. (meta)data include qualified references to other (meta)data
! How is the metadata linked to other data and metadata (to enhance context and clearly indicate relationships)?
|| There are no links to other metadata
|| Metadata is represented in a machine readable format, e.g. in a linked data format such as Resource Description Framework (RDF)
|-

! rowspan="3" | Reusable
| R1.1. (meta)data are released with a clear and accessible data usage license
! Which of the following best describes the license/usage rights attached to the data?
|| No license
| Standard machine-readable license (e.g. Creative Commons)
|-
| R1.2. (meta)data are associated with detailed provenance
! How much provenance information has been captured to facilitate data reuse?
|| Partially recorded
|| Partially recorded
|-
| R1.3. (meta)data meet domain-relevant community standards
|
|
|
|-
| colspan="6" style="background: #ABE" |
|-
! Total across F.A.I.R
|
! Low: < 50%; Medium: 50-60%; High: > 60%
|| ~ 45 %
|| ~ 85 %
|-
| colspan="6" style="background: #ABE" |
|-
! Remarks about database
||
||
||
|| The data is provided by the open-source platform Zenodo, not the project website itself. A very positive aspect is that information on different versions of the data as well as the number of views and downloads is available.
|-
! Identified gaps
||
||
||
* No DOI, only URL
* Extremely long URL link spanning several lines with 585 characters, that can easily become corrupted
* No links to other metadata
* No license information given
* Provenance not fully recorded

||
* Dataset identifiers should be included in the files describing the data
* Provenance details are only partially recorded with a note with contact name for further information
|}

= Metadata assessment =

== Dublin Core Elements ==
{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ The DCMI Metadata Terms lists the current set of the Dublin Core vocabulary. Source: [https://dublincore.org/specifications/dublin-core/dcmi-terms/ Dublin Core Metadata Initiative]
| 1 || abstract || 11 || contributor || 21 || extent || 31 || isReplacedBy || 41 || publisher || 51 || tableOfContents
|-
| 2 || accessRights || 12 || coverage || 22 || format || 32 || isRequiredBy || 42 || references || 52 || temporal
|-
| 3 || accrualMethod || 13 || created || 23 || hasFormat || 33 || issued || 43 || relation || 53 || title
|-
| 4 || accrualPeriodicity || 14 || creator || 24 || hasPart || 34 || isVersionOf || 44 || replaces || 54 || type
|-
| 5 || accrualPolicy || 15 || date || 25 || hasVersion || 35 || language || 45 || requires || 55 || valid
|-
| 6 || alternative || 16 || dateAccepted || 26 || identifier || 36 || license || 46 || rights || ||
|-
| 7 || audience || 17 || dateCopyrighted || 27 || instructionalMethod || 37 || mediator || 47 || rightsHolder || ||
|-
| 8 || available || 18 || dateSubmitted || 28 || isFormatOf || 38 || medium || 48 || source || ||
|-
| 9 || bibliographicCitation || 19 || description || 29 || isPartOf || 39 || modified || 49 || spatial || ||
|-
| 10 || conformsTo || 20 || educationLevel || 30 || isReferencedBy || 40 || provenance || 50 || subject || ||
|}

'''Metadata Category Types according to [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO]'''

There are three main types of metadata:
* Descriptive metadata describes a resource for purposes such as discovery and identification. It can include elements such as title, abstract, author, and keywords.
* Structural metadata indicates how compound objects are put together, for example, how pages are ordered to form chapters.
* Administrative metadata provides information to help manage a resource, such as when and how it was created, file type and other technical information, and who can access it. There are several subsets of administrative data; two that sometimes are listed as separate metadata types are:
** Rights management meta-data, which deals with intellectual property rights,and
** Preservation metadata, which contains information needed to archive and preserve a resource.
''

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Simple Dublin Core Elements. 15 DC elements with their (shortened) official definitions and examples.
! DC element name !! DC definition ([https://www.dublincore.org/specifications/dublin-core/dces/ DCMI]) !! Example (Energy Domain) !! Category (determined with the aid of [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO])
|-
! Title
| A name given to the resource || Outdoor monitoring of a hybrid micro-CPV solar panel with integrated micro-tracking and diffuse capture || Descriptive
|-
! Subject
| The topic of the resource || concentrator photovoltaics; CPV; rooftop photovoltaics; integrated tracking || Descriptive
|-
! Description
| An account of the resource || Dataset from the outdoor characterization of a B Series module from Insolight at the rooftop of the Instituto de Energía Solar - Universidad Politécnica de Madrid. …. || Descriptive
|-
! Creator
| An entity primarily responsible for making the resource || Stephen Askins; Gaël Nardin || Descriptive
|-
! Publisher
| An entity responsible for making the resource available || Stephen Askins || Descriptive
|-
! Contributor
| An entity responsible for making contributions to the resource || Stephen Askins || Descriptive
|-
! Date
| A point or period of time associated with an event in the lifecycle of the resource || 2019-07-23; Date will be associated with the creation or availability of the resource. || Administrative
|-
! Type
| The nature or genre of the resource || info:eu-repo/semantics/other; dataset || Descriptive
|-
! Format
| The file format, physical medium, or dimensions of the resource || image
|| Structural
|-
! Identifier
| An unambiguous reference to the resource within a given context || https://zenodo.org/record/3346823; 10.5281/zenodo.3346823; oai:zenodo.org:3346823 || Descriptive
|-
! Source
| A related resource from which the described resource is derived || RC607.A26W574 1996 [where "RC607.A26W574 1996" is the call number of the print version of the resource, from which the present version was scanned] || Descriptive
|-
! Language
| A language of the resource || eng || Descriptive
|-
! Relation
| A related resource || info:eu-repo/grantAgreement/EC/H2020/787289/ || Descriptive
|-
! Coverage
| The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant || 1995-1996; Madrid, Spain || Descriptive
|-
! Rights
| Information about rights held in and over the resource || info:eu-repo/semantics/openAccess; https://creativecommons.org/licenses/by/4.0/legalcode || Administrative
|}

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Non-DC elements

! Non-DC element name !! Definition !! Example !! Category
|-
! Date of availability
| Since when is the data available
||
|| Impact/Exploitation

|-
! Number of users
| Is there any information on the number of accesses or users?
||
|| Impact/Exploitation

|-
! Number of scientific publications where data is cited/used
| Is there any information on the number of citations/uses?
||
|| Impact/Exploitation

|-
! Curation activities (is the versioning ongoing)
| Update, maintainance
||
|| Impact/Exploitation
|}

Attempting to put a value on Impact/Exploitation:
* '''ENTSO-E''': Found 172 results in Advanced Search (ALL) in Web of Science ([https://apps.webofknowledge.com/ WoSc]) (16/06/2020). Citations of all these search result add up to 714. This however does not mean, that all search results really explicitly used data from ENTSO-E. Some (for sure, for some were found) only mentioned it saying they adoped workflow etc. from the site.
* '''SMARD''': Found 82 results in Advanced Search (ALL=(SMARD)) in Web of Science ([https://apps.webofknowledge.com/ WoSc]) (16/06/2020). However: SMARD is also an acronym for ''Spinal muscular atrophy with respiratory distress (SMARD)''. This somewhat complicates results and needs some further discussion. All 82 search results accumumlated 2359 citations themselves.

== Results for SMILES ==

{| class="wikitable mw-collapsible"
! DC element name
! DC definition ([https://www.dublincore.org/specifications/dublin-core/dces/ DCMI])
! Category (determined with the aid of [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO])
! rowspan = "16" style="background: #ABE" |
! SMILES (ED4)
! Notes (SMILES (ED4) )

|-
! Title
| A name given to the resource
|| Descriptive
|| A
||
|-
! Subject
| The topic of the resource || Descriptive || A ||
|-
! Description
| An account of the resource || Descriptive || PA || The description of the data is very limited. For all files a general description is given. This is linked to the project deliverables but it is not indicated which deliverable are exactly corresponding. This seems to be a time-consuming process for researchers.
|-
! Creator
| An entity primarily responsible for making the resource || Descriptive || A || The metadata file uses the tag "author"
|-
! Publisher
| An entity responsible for making the resource available || Descriptive || A || This information only available under the URL but not in the metadata file
|-
! Contributor
| An entity responsible for making contributions to the resource || Descriptive || NA ||
|-
! Date
| A point or period of time associated with an event in the lifecycle of the resource || Administrative || A || This information only available under the URL but not in the metadata file
|-
! Type
| The nature or genre of the resource || Descriptive || NA || No explicit mention of the type (e.g. document, dataset) although it could be derived from the context
|-
! Format
| The file format, physical medium, or dimensions of the resource || Structural || PA || The metadata element "format" is not exactly indicated but the metadata tag "file" includes the format type at the end (".csv"). Additionally, the tag "datatype" is used to specify the internal data type (e.g. integer, string) of the data file.
|-
! Identifier
| An unambiguous reference to the resource within a given context || Descriptive || NA ||
|-
! Source
| A related resource from which the described resource is derived || Descriptive || NA ||
|-
! Language
| A language of the resource || Descriptive || NA || No explicit mention of the language although it could be derived from the context
|-
! Relation
| A related resource || Descriptive || PA || This information (EU grant information) only generally available under the URL but not in the metadata file
|-
! Coverage
| The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant || Descriptive || NA ||
|-
! Rights
| Information about rights held in and over the resource || Administrative || NA ||
|-
! colspan = "6" style="background: #ABE" |
|-
!
|}

Gap analysis

2020-08-31T09:58:11Z

Christopherbs: /* Results for SMILES */

<accesscontrol>Administrators,consortium</accesscontrol>

This is the page for the gap analysis.

= Tacit knowledge "FAIRification and opening of low carbon energy research data" =
 Consortium members can add any time, they feel that something is important to note even though it is not mentioned in a standard deliverable. In other words, this page collects uncodified, tacit knowledge. It will help us later to compile suggestions and lessons learned. This kind of knowledge is collected two-fold:
#Anytime if someone feels that this should be noted. Please write down: Issue, Date, Author (could also be "anonymous"), the issue described in a few words or maximal lines>
#During the final day of workshops

=== Learning process: ===

*People are hesitant to adopt new IT technologies, this is even the case among researchers heavily relying on data, algorithms and collaborative online software (e.g., R, platforms, online conferencing, HPC, ...). The effort to encourage change is not to underestimate. Reasons are several, notably, lack of time and uncertainty about potential benefit as well as overall risk aversion preferences. EERAdata is using the online software "Only Office" to facilitate collaboration (in particular also during the Covid-19 period).
 '''FAIR and open criteria:'''
*Consortium members have a fair understanding of what FAIR/O is, but there is little knowledge and/or technical experience on how to approach the FAIRification and opening. All, however, share the view that we are at a critical point in time, where we need to implement these criteria. 
*To deepen knowledge about FAIR/O criteria, it is useful to test the criteria on a database one is familiar with. For this purpose (and to start brainstorming about the platform), AIT has developed a questionnaire for application in the use cases.
'''Metadata:'''
*A good starting point is to think about metadata and to look into existing metadata concepts in one's field. The first step is to understand that also metadata need to adhere to the FAIR/O principles.
*The next step is to increase knowledge on IT specific terms, i.e. to understand what the difference is between different '''[https://energydata.onlyoffice.eu/Products/Files/doceditor.aspx?fileid=5404340 metadata frameworks] (taxonomy, thesaurus, ontology) as well as classification of metadata (high-level, medium-level, low-level OR administrative, structural and descriptive metadata).  '''WP 2 being in charge of aligning approaches between use cases, participates in all use case kickoffs to bring everybody on the same page. The presentation is linked with "metadata frameworks".
*It is useful to supply consortium members with read aheads and watch aheads on metadata to prepare the '''[https://energydata.onlyoffice.eu/Products/Files/doceditor.aspx?fileid=5296590 first EERAdata workshop]'''. The workshop starts applications and discussions in the use cases break out sessions (and bringing insights back to the plenary), using selected databases.

= FAIR Assessment=

== FAIR Guiding Principles ==
{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ This table shows the FAIR guiding principles described by [https://www.doi.org/10.1038/sdata.2016.18 Wilkinson et al., 2016].

! To be Findable
|
* F1. (meta)data are assigned a globally unique and persistent identifier
* F2. data are described with rich metadata (defined by R1 below)
* F3. metadata clearly and explicitly include the identifier of the data it describes
* F4. (meta)data are registered or indexed in a searchable resource

|-
! To be Accessible
|
* A1. (meta)data are retrievable by their identifier using a standardized communications protocol
** A1.1 the protocol is open, free, and universally implementable
** A1.2 the protocol allows for an authentication and authorization procedure, where necessary
* A2. metadata are accessible, even when the data are no longer available

|-
! To be Interoperable
|
* I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
* I2. (meta)data use vocabularies that follow FAIR principles
* I3. (meta)data include qualified references to other (meta)data

|-
! To be Reusable
|
* R1. meta(data) are richly described with a plurality of accurate and relevant attributes
* R1.1. (meta)data are released with a clear and accessible data usage license
* R1.2. (meta)data are associated with detailed provenance
* R1.3. (meta)data meet domain-relevant community standards

|}

== Identified gaps after Workshop 1 (02/06/2020 – 04/06/2020) ==

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ This table shows a summary of the specific issues that were identified in Workshop 1. The aim was to categorise different issues to get a better understanding of how to tackle these challenges.
! FAIR/O !! Specific FAIR/O !! Gaps !! Gaps for energy domain !! Use case specific gaps !! Consequences for researchers
|-
! F
| F2
|| Metadata scope
|| Different fields require different metadata (potentially very specific)
|| UC3: lack of additional metadata for applications of materials
|| Data are less useful for the specific field
|-
! F
|| F1/F3
|| Identifiers
||
* Missing identifiers on websites
* Identifier not in downloaded data files
||
|| Makes research more difficult.
|-
! I
| I2
|| Taxonomy/ontology/common vocabulary and language issues
||
* Lack of standardisation
* Heterogeneous data makes standardisation hard
* No vocabulary documentation on websites
* Words used for same term in other languages may differ
* Databases in other languages than English
||
|| Research costs more time
|-
! I
| I3
|| References to other (meta)data
||
* No linking to source documents and related publications (for contextual knowledge)
* Possible need for links to other fields
|| UC3: link between microscopic and macroscopic materials (e.g., turbine blades)
||
* Data cannot be connected to the source
* Makes research more difficult.

|-
! R
| R1.1
|| Licensing
||
* No licenses available may mean the data is not reusable for the researcher
* Licenses not clear and accessible
* Obtaining licenses may result in more effort and costs
||
|| Data potentially not reusable

|-
! R
| R1.2
|| Reliability and provenance of data
|| When the data is collected from different and high number of sources, its reliability decreases
||
|| Data potentially not reusable

|-
! O
|
|| Privacy concerns and expected disadvantages
|| Not publishing data due to privacy concerns (sensitive data)
|| UC2: In case of distribution network data this is relevant
|| Data not accessible

|-
! Other
|
|| Conducting FAIR assessments
||
* No qualitative assessment of the data itself (which may be limited)
* Discrepancies between results conducted by humans versus machines
* Sometimes not even DC standards are met
* Metadata is not updated
* Lacking encryption of websites (https)
* Problems assessing whether metadata will be available after data is unavailable
* Interface design/layout may be unclear, incomplete or not intuitive for humans
* Authentication details (user registration login / good or bad, clarify)

||
* UC1 lack of data availability for time-series
||
* Assessment is uncertain (human assessment may need more clarification and understanding)
* Makes research more time-intensive

|}

== FAIR Self-Assessment tool ==

To conduct a human FAIR evaluation for different databases and their respective datasets, different free online tools were considered. Some seemed to be quite useful (e.g. [https://satifyd.dans.knaw.nl/ SATISFYD] ) and some were simple self-evaluation PDFs to be printed and filled out.

We decided to chose the [https://ardc.edu.au/resources/working-with-data/fair-data/fair-self-assessment-tool/ self-assessment tool] provided by the Australian Research Data Commons (ARDC). The decision was based manly upon the fact that the tools questions closely matched the FAIR categories specified by [https://www.doi.org/10.1038/sdata.2016.18 Wilkinson et al., 2016] which would make it easily comparable to the existing preferable FAIR conditions and aid with the repeatability of the results, and the half-automated scoring features that show the extent of the "FAIRness" of the analysed sample as a simple bar chart.

The result of the FAIR assessment is provided as a bar chart where an empty bar chart displays 0 % FAIR compatibility, and a full bar chart represents 100 % FAIR compatibility. The score is therefore not returned as a specific value. During the assessment below, the "Total Across FAIR" in Table ... was estimated according to the level of the bar chart from 0 % to 100 %.
Resulting scores where categorised as follows: Low: < 50%; Medium: 50-60%; High: > 60% .

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ All options for each question of the ARDCs self-assessment tool
!colspan="4"|Findable
!colspan="3"|Accessible
!colspan="3"|Interoperable
!colspan="3"|Reusable
|-
| F1. (meta)data are assigned a globally unique and persistent identifier
|| F2. data are described with rich metadata (defined by R1 below)
|| F3. metadata clearly and explicitly include the identifier of the data it describes
|| F4. (meta)data are registered or indexed in a searchable resource
|| A1. (meta)data are retrievable by their identifier using a standardized communications protocol
|| A1.1 the protocol is open, free, and universally implementable and A1.2 the protocol allows for an authentication and authorization procedure, where necessary
|| A2. metadata are accessible, even when the data are no longer available
|| I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
|| I2. (meta)data use vocabularies that follow FAIR principles
|| I3. (meta)data include qualified references to other (meta)data
|| R1.1. (meta)data are released with a clear and accessible data usage license
|| R1.2. (meta)data are associated with detailed provenance
|| R1.3. (meta)data meet domain-relevant community standards
|-
! Does the dataset have any identifiers assigned?
! Is the dataset identifier included in all metadata records/files describing the data?
! How is the data described with metadata?
! What type of repository or registry is the metadata record in?
! How accessible is the data?
! Is the data available online without requiring specialised protocols or tools once access has been approved?
! Will the metadata record be available even if the data is no longer available?
! What (file) format(s) is the data available in?
! What best describes the types of vocabularies/ontologies/tagging schemas used to define the data elements?
! How is the metadata linked to other data and metadata (to enhance context and clearly indicate relationships)?
! Which of the following best describes the license/usage rights attached to the data?
! How much provenance information has been captured to facilitate data reuse?
!
|-
| Globally unique, citable and persistant (e.g. DOI, PURL, ARK or Handle)
|| Yes
|| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
|| Data is in one place but discoverable through several registries
|| Publicly accessible
|| Standard web service API (e.g. OGC)
|| Yes
|| In a structured, open standard, machine-readable format
|| Standardised open and universal using resolvable global identifiers linking to explainations
|| Meadata is represented in a machine readable format, e.g. in a linked data format such as Resource Description Framework (RDF)
|| Standard machine-readable license (e.g. Creative Commons)
|| Fully recorded in a machine-readable format
||
|-
| Web adress (URL)
|| No
|| Comprehensively, but in a text-based, non-standard format
|| Generalist public repository
|| Fully accessible to person who meet explicitly stated conditions, e.g. ethics approval for sensitive data
|| Non-standard web service (e.g. OpenAPI/Swagger/informal API)
|| No
|| In a structured, open standard, non-machine-readable format
|| Standardised vocabularies/ontologies/schema without global identifiers
|| The metadata record includes URI links to related metadata, data and definitions
|| Standard text based license
|| Fully recorded in a text format
||
|-
| Local identifier
||
|| Brief title and description
|| Domain-specific repository
|| A de-identified / modified subset of the data is publicly accessible
|| File download from online location
|| Unsure
|| Mostly in a proprietary format
|| No standards have been applied in the description of data elements
|| There are no links to other metadata
|| Non-standard machine-readably license (clearly indicating under what conditions the data may be reused)
|| Partially recorded
||
|-
| No identifier
||
|| The data is not described
|| Local institutional repository
|| Embaged access after a specific date
|| By individual arrangement
||
||
|| Data elements not described
||
|| Non-standard text-based license
|| No provenance information is recorded
||
|-
|
||
||
|| The data is not described in any repository
|| Unspecified conditional access e.g. contact the data custodian for access
|| No access to data
||
||
||
||
|| No license
||
||
|-
|
||
||
||
|| Access to metadata only
||
||
||
||
||
||
||
||
|-
|
||
||
||
|| No access to data or metadata
||
||
||
||
||
||
||
||
|}

At this point it is important to note that the assessment of databases and/or datasets using this tool is solely considered a human assessment. This may lead to different evaluations by different users. To counteract this phenomenon, it was suggested that the evaluations should be completed using the 'four-eyes principle' to create more verifiable results. Also, it is planned to additionally evaluate the respective databases using an external tool to perform a machine-assessment.

In addition, not all questions are weighted equally and thus a worse result for one question may not have a large impact whereas only choosing the second-best option in another question will bring down the overall FAIR value considerably.

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ The ARDC tool provides detailed information about the requirements for each of the options of its tool. These descriptions are documented here.
|-
! rowspan="5" | F
|| F
|| About Findable
|| Making data Findable includes assigning a [https://www.ands.org.au/guides/persistent-identifiers-awareness persistent identifier] (like a [https://www.ands.org.au/guides/doi DOI] or [https://www.ands.org.au/guides/identify-my-data Handle] ), having rich [https://www.ands.org.au/working-with-data/metadata metadata] to describe the data and making sure it is findable through disciplinary and generalist discovery portals (local and international).

|-
| F1
|| About identifiers
|| Identifiers are essential for identifying, finding, retrieving, linking and citing datasets. A Web address (URL) can be used to specify the online location of a resource but over time URLs tend to change which leads to broken links to the data. To be useful, identifiers need to be persistent and unique. Digital Object Identifiers (DOIs) and other persistent identifiers (PIDs) provide a permanent citable reference to a particular dataset. The DOI is a permanent fixed reference to the dataset no matter where it is located online and enables citation and citation metrics. Services to create a persistent identifier are often offered by your affiliated institution or the repository you are using to describe your data. Talk to your library service for more information.

Read more: [https://www.ands.org.au/working-with-data/citation-and-identifiers Persistent identifiers]

|-
| F2
|| About identifiers
|| The identifier (preferably a persistent identifier) needs to be clearly stated in the metadata record describing the data collection, and also in any associated data files or metadata.

|-
|F3
|| About metadata descriptions
|| Comprehensive metadata records will include descriptive content that facilitates discovery, access and reuse of the data being described. While there is no 'one size fits all' list, comprehensive metadata should include:
* a globally unique persistent identifier e.g. a DOI
* a title
* related people, i.e. the data creator or custodian
* how to access the data and file formats
* a description of how the data were created and how to interpret the data subject or keywords
* citation information that clearly indicates how the data should be cited
* a machine-readable data licence
* provenance and contextual information such as:
* links to related publications, projects, services and software
* methodology and processes involved in data production
* spatial and temporal coverage (if relevant)
* object-level data description

Providing metadata in a standard schema allows it to be read and used by machines as well as humans.

Read more: [https://www.ands.org.au/working-with-data/metadata Metadata]

|-
| F4
|| About metadata repositories
|| A rich metadata description alone does not ensure a dataset’s ‘findability’ on the internet; the dataset needs to be registered or indexed in a searchable resource, such as a generalist (e.g. [https://figshare.com/ Figshare], [http://datadryad.org/ Dryad], [https://zenodo.org/ Zenodo], domain-specific (e.g. [https://pangaea.de/ PANGAEA], [https://ada.edu.au/ ADA], [https://www.icpsr.umich.edu/ ICPSR]), or institutional (e.g. [https://openresearch-repository.anu.edu.au/ ANU Research Repository] , [https://ses.library.usyd.edu.au/ Sydney eScholarship Repository], [https://data.gov.au/ data.gov.au]) data repository or registry. Ideally these repositories/registries are indexed by search engines such as Google and/or Google Scholar.

|-
! rowspan = "4" | A
| A
|| About Accessible
|| [https://www.ands.org.au/working-with-data/publishing-and-reusing-data/publishing To make data accessible] may include making the data [https://www.ands.org.au/working-with-data/articulating-the-value-of-open-data/open-data open] using a standardised protocol. However the data does not necessarily have to be open. There are sometimes good reasons why data [https://www.ands.org.au/working-with-data/sensitive-data cannot be made open], for example privacy concerns, national security or commercial interests. If it is not open there should be clarity and transparency around the conditions governing access and [https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse reuse].
|-
| A1
|| About data accessibility
|| Not all data that is discoverable can be freely accessed. Often there are embargoes, access controls, and access permissions associated with data due to a variety of issues such as privacy and commercial interests. Even with all these issues much [https://www.ands.org.au/working-with-data/sensitive-data sensitive data] can be shared. Many other issues that could be perceived as blockers to sharing data may be [http://datapub.cdlib.org/closed-data-excuses-excuses overcome].
|-
| A1.1/A1.2
|| About protocols
|| Ideally users would like to retrieve appropriate internet content directly and unhindered once they have located it. Internet protocols (e.g. http and ftp) define rules and conventions for communication between devices, and tools and services are available to facilitate this process, e.g. APIs.

Protocols:
* [https://www.lifewire.com/hypertext-transfer-protocol-817944 HTTP] (Hypertext Transfer Protocol) is the set of rules for transferring files (text, graphic images, sound, video, and other multimedia files) on the [https://whatis.techtarget.com/definition/World-Wide-Web World Wide Web]. As soon as a Web user opens their Web browser, the user is indirectly making use of HTTP.
* [https://www.lifewire.com/file-transfer-protocol-817943 FTP] (File Transfer Protocol) is a standard Internet [http://searchnetworking.techtarget.com/definition/protocol protocol] for transmitting files between computers on the Internet over TCP/IP connections. Read more: [https://www.lifewire.com/definition-of-protocol-network-817949 Web protocols]
* API (Application Programming Interface): When information is made available in any machine readable format, it becomes possible to make that information directly available to programs that request that information over the web. An API is the way this information is made directly available to other machines.
View more: [https://www.youtube.com/watch?v=s7wmiS2mSXY What is an API?]

OGC - http://www.opengeospatial.org/ogc

|-
| A2
|| About metadata availability
|| Effort is often required to maintain data resources online which can often lead to it being neglected especially over long periods of time. This often leads to broken links between the metadata and the data. Having at least a description of the data in the form of a metadata record means there is a record of the data's existence allowing the possibility of someone tracking it down.

|-
! rowspan="4" | I
|| I
|| About Interoperability
|| To be interoperable (i.e. data that is interpretable by a computer, so that they can be automatically combined with other data) the data will need to use community agreed [https://www.ands.org.au/guides/file-formats formats], language and [https://www.ands.org.au/guides/vocabularies-and-research-data vocabularies]. The [https://www.ands.org.au/working-with-data/metadata metadata] will also need to use a community agreed standards and vocabularies, and contain links to related information using [https://www.ands.org.au/working-with-data/citation-and-identifiers identifiers].
|-
|| I1
|| About file formats
|| When selecting file formats for archiving, the formats should ideally be:
* Non-proprietary
* Unencrypted
* Uncompressed
* In common usage by the research community
* Adherent to an open, documented standard, such as described by the State of California (see [http://www.leginfo.ca.gov/pub/07-08/bill/asm/ab_1651-1700/ab_1668_bill_20070524_amended_asm_v96.pdf AB 1668, 2007])
** Interoperable among diverse platforms and applications
** Fully published and available royalty-free
** Fully and independently implementable by multiple software providers on multiple platforms without any intellectual property restrictions for necessary technology
** Developed and maintained by an open standards organization with a well-defined inclusive process for evolution of the standard.

From: [https://library.stanford.edu/research/data-management-services/data-best-practices/best-practices-file-formats Stanford University Library - Best practices for file formats]

File format examples:
* Structured, open standard, machine-readable format e.g. (text) PDF/A, HTML, Plain text, (images) TIFF, JPEG 2000, GIF, (audio) MP3, AIFF, WAVE, (video) MOV, MPEG, AVI, (Tabular data) CSV
* structured, open standard, non-machine-readable format, e.g. PDF, HTML, JPG
* Proprietary format, e.g. doc (Word), .xls (Excel), .ppt (PowerPoint), .sav

Read more:
* [https://www.ands.org.au/__data/assets/pdf_file/0003/731775/File-Formats.pdf ANDS]
* [https://www.europeandataportal.eu/elearning/en/module9/#/id/co-01 European Data Portal]

|-
|| I2
|| About schema standards
|| Schema standards are schemas that have gone through a formal validation process by a standards organisation, such as the International Standards Organisation (ISO) or an equivalent body such as the Dublin Core Metadata Initiative (DCMI); or, commonly used and consistently applied metadata schemas that are well documented, endorsed, and maintained can also become ‘de-facto standards’, e.g. RIF-CS for describing data collections and services used in Research Data Australia.

Read more: [https://www.ands.org.au/guides/metadata-working Metadata schema]

Standardised open and universal schemas, e.g, [https://www.dcc.ac.uk/resources/metadata-standards/datacite-metadata-schema DataCite Metadata Schema], [https://www.dcc.ac.uk/resources/metadata-standards/prov PROV], [https://www.dcc.ac.uk/resources/metadata-standards/dublin-core Dublin Core]

Domain-specific standards, e.g. [http://human-phenotype-ontology.github.io/ HPO - Human Phenotype Ontology], [https://www.nlm.nih.gov/mesh/ MeSH - Medical Subject Headings] , [https://mcp-profile-docs.readthedocs.io/en/stable/ Marine Community Profile], [https://ddialliance.org/ DDI - Data Documentation Initiative].

For more examples of standards: [http://rd-alliance.github.io/metadata-directory/ Here].

Resolvable global identifiers - An identifier is any label used to name something uniquely (whether online or offline). URLs are an example of an identifier. So are serial numbers, and personal names. A persistent identifier is guaranteed to be managed and kept up to date over a defined time period.

Read more: [https://www.ands.org.au/guides/persistent-identifiers-awareness Persistent Identifiers]

|-
||I3
|| About metadata links
|| The goal is to create as many meaningful linkages as possible between (meta)data resources to enrich the contextual knowledge about the data, balanced against the time/energy involved in making a good data model.

'''Machine-readable (meta)data'''

(Meta)data in a format that can be automatically read and processed by a computer, such as [http://opendatahandbook.org/glossary/en/terms/csv/ CSV], [http://opendatahandbook.org/glossary/en/terms/json/ JSON], [http://opendatahandbook.org/glossary/en/terms/xml/ XML] etc. For more information: [http://opendatahandbook.org/glossary/en/terms/machine-readable/ Open Data Handbook]

'''Linked Data'''

Linked Data (also known as Linking Data) can be applied to improve the exploitation of the “Web of data.” The expression refers to the publishing of structured data in a way that typed links are created between data from different sources to provide a higher level of usability. By using Linked Data, it is possible to find other, related data. Structured data should meet four requirements to be called
Linked Data:

* URIs should be assigned to all entities of the dataset.
* HTTP URIs are required to ensure that all entities can be referenced and cited by users and user agents.
* Entities should be described using standard formats such as RDF/XML.
* Links should be created to other, related entity URIs.

All data that fulfil these requirements and are released for the public are called Linked Open Data (LOD). http://www.lesliesikos.com/semantic-web-machine-readable-structured-data-with-meaningful-annotations/

Read more under the heading (meta)data include qualified references to other (meta)data in the following resource: https://www.dtls.nl/fair-data/fair-principles-explained/

For more information on Linked Data standards - RDF - https://www.w3.org/RDF/

Other related linked data standards:
* OWL - https://www.w3.org/OWL/
* SKOS - https://www.w3.org/2004/02/skos/
* SPARQL - https://www.w3.org/TR/rdf-sparql-query/

|-
! rowspan="3" | R
|| R
|| About Reusable
|| Reusable data should maintain its initial richness. For example, it should not be abridged for the purpose of explaining the findings in one particular publication. It needs a clear machine-readable [https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse licence] and [https://www.ands.org.au/partners-and-communities/ands-communities/data-provenance-interest-group provenance] information on how the data was formed. It should also use discipline-specific data and [https://www.ands.org.au/working-with-data/metadata metadata] standards to give it rich contextual information that will allow for accurate interpretation and reuse.

|-
|| R1.1
|| About licenses
|| If data is not licensed no-one else can use it. In Australia, no licence is regarded as the same as 'all rights reserved', confining any reuse to very limited circumstances. Applying a Creative Commons licence to your data is a simple way to ensure that your data can be reused. The less restrictive the licence, the more that can be done with the data.
To make it easy for the Web to know when a work is available under a particular license, a “machine readable” version of the license provides a summary of the key freedoms and obligations written into a format that software systems, search engines, and other kinds of technology can understand.
Example from the [https://creativecommons.org/choose/ Creative Commons License Choser]:

What appears in the metadata:

This work is licensed under a [https://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International License].

Code snippet:

<code>
<a rel="license" href="http://creativecommons.org/licenses/by/4.0/">

<img alt="Creative Commons License" style="border-width:0"

src="https://i.creativecommons.org/l/by/4.0/88x31.png"/>

</a> This work is licensed under a

<a rel="license" href="http://creativecommons.org/licenses/by/4.0/">

Creative Commons Attribution 4.0 International License</a>.
<\code>

Read more: https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse

|-
||R1.2
|| About provenance
|| Data provenance is used to document where a piece of data comes from and the process and methodology by which it is produced. It is important to confirm the authenticity of data enabling trust, credibility and reproducibility. This is becoming increasingly important, especially in the eScience community where research is data intensive and often involves complex data transformations and procedures ([https://www.ands.org.au/partners-and-communities/ands-communities/data-provenance-interest-group Read more]). Provenance vocabularies such as [https://www.w3.org/2005/Incubator/prov/XGR-prov-20101214/#Recommendations Provenir Ontology (PROV-O) and others].

|}

== Results for SMILES and ShareWind ==

{| class="mw-collapsible wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Datasets from the SMILES and ShareWind platforms was analysed using the self-assessment tool provided by ARDC.
! rowspan="5" |
| rowspan="5" |
! Database Code
| rowspan="24" style="background: #ABE" |
| ED4
| ED9
|-
! Database Title
|| SMILES
| ShareWind
|-
! Database Link
|| https://www.ecria-smiles.eu/
| https://sharewind.eu/
|-
! Sample Link
|| [https://www.ecria-smiles.eu/data-files/-/document_library/W32cCAfL2jMX/view_file664466?_com_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX_redirect=https%3A%2F%2Fwww.ecria-smiles.eu%3A443%2Fdata-files%2F-%2Fdocument_library%2FW32cCAfL2jMX%2Fview%2F664423%3F_com_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX_redirect%3Dhttps%253A%252F%252Fwww.ecria-smiles.eu%253A443%252Fdata-files%253Fp_p_id%253Dcom_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX%2526p_p_lifecycle%253D0%2526p_p_state%253Dnormal%2526p_p_mode%253Dview here] (link has 585 characters)
| https://sharewind.eu/records/f9d31abbc2824284b8c1d28894d9619a (data found at https://zenodo.org/record/1162738)
|-
! Sample Size
|| 4 KB
| 37 MB
|-
| colspan="6" style="background: #ABE" |
|-
! rowspan="4" | Findable
| F1. (meta)data are assigned a globally unique and persistent identifier
! Does the dataset have any identifiers assigned?
|| Web adress (URL)
| Globally unique, citable and persistant (e.g. DOI, PURL, ARK or Handle)
|-

| F2. data are described with rich metadata (defined by R1 below)
! Is the dataset identifier included in all metadata records/files describing the data?
|| No
| No
|-

| F3. metadata clearly and explicitly include the identifier of the data it describes
! How is the data described with metadata?
|| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
|-

| F4. (meta)data are registered or indexed in a searchable resource
! What type of repository or registry is the metadata record in?
|| Domain-specific repository
| Data is in one place but discoverable through several registries
|-

! rowspan="3" | Accessible

| A1. (meta)data are retrievable by their identifier using a standardized communications protocol
! How accessible is the data?
|| Publicly accessible
| Publicly accessible
|-
| A1.1 the protocol is open, free, and universally implementable; A1.2 the protocol allows for an authentication and authorization procedure, where necessary
! Is the data available online without requiring specialised protocols or tools once access has been approved?
|| File download from online location
| Standard web service API (e.g. OGC)
|-
| A2. metadata are accessible, even when the data are no longer available
! Will the metadata record be available even if the data is no longer available?
|| Unsure
| Yes
|-

! rowspan="3" | Interoperable
| I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
! What (file) format(s) is the data available in?
|| In a structured, open standard, machine-readable format
|| In a structured, open standard, non-machine-readable format
|-
| I2. (meta)data use vocabularies that follow FAIR principles
! What best describes the types of vocabularies/ontologies/tagging schemas used to define the data elements?
|| Standardised vocabularies/ontologies/schema without global identifiers
|| Standardised vocabularies/ontologies/schema without global identifiers
|-
| I3. (meta)data include qualified references to other (meta)data
! How is the metadata linked to other data and metadata (to enhance context and clearly indicate relationships)?
|| There are no links to other metadata
|| Metadata is represented in a machine readable format, e.g. in a linked data format such as Resource Description Framework (RDF)
|-

! rowspan="3" | Reusable
| R1.1. (meta)data are released with a clear and accessible data usage license
! Which of the following best describes the license/usage rights attached to the data?
|| No license
| Standard machine-readable license (e.g. Creative Commons)
|-
| R1.2. (meta)data are associated with detailed provenance
! How much provenance information has been captured to facilitate data reuse?
|| Partially recorded
|| Partially recorded
|-
| R1.3. (meta)data meet domain-relevant community standards
|
|
|
|-
| colspan="6" style="background: #ABE" |
|-
! Total across F.A.I.R
|
! Low: < 50%; Medium: 50-60%; High: > 60%
|| ~ 45 %
|| ~ 85 %
|-
| colspan="6" style="background: #ABE" |
|-
! Remarks about database
||
||
||
|| The data is provided by the open-source platform Zenodo, not the project website itself. A very positive aspect is that information on different versions of the data as well as the number of views and downloads is available.
|-
! Identified gaps
||
||
||
* No DOI, only URL
* Extremely long URL link spanning several lines with 585 characters, that can easily become corrupted
* No links to other metadata
* No license information given
* Provenance not fully recorded

||
* Dataset identifiers should be included in the files describing the data
* Provenance details are only partially recorded with a note with contact name for further information
|}

= Metadata assessment =

== Dublin Core Elements ==
{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ The DCMI Metadata Terms lists the current set of the Dublin Core vocabulary. Source: [https://dublincore.org/specifications/dublin-core/dcmi-terms/ Dublin Core Metadata Initiative]
| 1 || abstract || 11 || contributor || 21 || extent || 31 || isReplacedBy || 41 || publisher || 51 || tableOfContents
|-
| 2 || accessRights || 12 || coverage || 22 || format || 32 || isRequiredBy || 42 || references || 52 || temporal
|-
| 3 || accrualMethod || 13 || created || 23 || hasFormat || 33 || issued || 43 || relation || 53 || title
|-
| 4 || accrualPeriodicity || 14 || creator || 24 || hasPart || 34 || isVersionOf || 44 || replaces || 54 || type
|-
| 5 || accrualPolicy || 15 || date || 25 || hasVersion || 35 || language || 45 || requires || 55 || valid
|-
| 6 || alternative || 16 || dateAccepted || 26 || identifier || 36 || license || 46 || rights || ||
|-
| 7 || audience || 17 || dateCopyrighted || 27 || instructionalMethod || 37 || mediator || 47 || rightsHolder || ||
|-
| 8 || available || 18 || dateSubmitted || 28 || isFormatOf || 38 || medium || 48 || source || ||
|-
| 9 || bibliographicCitation || 19 || description || 29 || isPartOf || 39 || modified || 49 || spatial || ||
|-
| 10 || conformsTo || 20 || educationLevel || 30 || isReferencedBy || 40 || provenance || 50 || subject || ||
|}

'''Metadata Category Types according to [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO]'''

There are three main types of metadata:
* Descriptive metadata describes a resource for purposes such as discovery and identification. It can include elements such as title, abstract, author, and keywords.
* Structural metadata indicates how compound objects are put together, for example, how pages are ordered to form chapters.
* Administrative metadata provides information to help manage a resource, such as when and how it was created, file type and other technical information, and who can access it. There are several subsets of administrative data; two that sometimes are listed as separate metadata types are:
** Rights management meta-data, which deals with intellectual property rights,and
** Preservation metadata, which contains information needed to archive and preserve a resource.
''

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Simple Dublin Core Elements. 15 DC elements with their (shortened) official definitions and examples.
! DC element name !! DC definition ([https://www.dublincore.org/specifications/dublin-core/dces/ DCMI]) !! Example (Energy Domain) !! Category (determined with the aid of [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO])
|-
! Title
| A name given to the resource || Outdoor monitoring of a hybrid micro-CPV solar panel with integrated micro-tracking and diffuse capture || Descriptive
|-
! Subject
| The topic of the resource || concentrator photovoltaics; CPV; rooftop photovoltaics; integrated tracking || Descriptive
|-
! Description
| An account of the resource || Dataset from the outdoor characterization of a B Series module from Insolight at the rooftop of the Instituto de Energía Solar - Universidad Politécnica de Madrid. …. || Descriptive
|-
! Creator
| An entity primarily responsible for making the resource || Stephen Askins; Gaël Nardin || Descriptive
|-
! Publisher
| An entity responsible for making the resource available || Stephen Askins || Descriptive
|-
! Contributor
| An entity responsible for making contributions to the resource || Stephen Askins || Descriptive
|-
! Date
| A point or period of time associated with an event in the lifecycle of the resource || 2019-07-23; Date will be associated with the creation or availability of the resource. || Administrative
|-
! Type
| The nature or genre of the resource || info:eu-repo/semantics/other; dataset || Descriptive
|-
! Format
| The file format, physical medium, or dimensions of the resource || image
|| Structural
|-
! Identifier
| An unambiguous reference to the resource within a given context || https://zenodo.org/record/3346823; 10.5281/zenodo.3346823; oai:zenodo.org:3346823 || Descriptive
|-
! Source
| A related resource from which the described resource is derived || RC607.A26W574 1996 [where "RC607.A26W574 1996" is the call number of the print version of the resource, from which the present version was scanned] || Descriptive
|-
! Language
| A language of the resource || eng || Descriptive
|-
! Relation
| A related resource || info:eu-repo/grantAgreement/EC/H2020/787289/ || Descriptive
|-
! Coverage
| The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant || 1995-1996; Madrid, Spain || Descriptive
|-
! Rights
| Information about rights held in and over the resource || info:eu-repo/semantics/openAccess; https://creativecommons.org/licenses/by/4.0/legalcode || Administrative
|}

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Non-DC elements

! Non-DC element name !! Definition !! Example !! Category
|-
! Date of availability
| Since when is the data available
||
|| Impact/Exploitation

|-
! Number of users
| Is there any information on the number of accesses or users?
||
|| Impact/Exploitation

|-
! Number of scientific publications where data is cited/used
| Is there any information on the number of citations/uses?
||
|| Impact/Exploitation

|-
! Curation activities (is the versioning ongoing)
| Update, maintainance
||
|| Impact/Exploitation
|}

Attempting to put a value on Impact/Exploitation:
* '''ENTSO-E''': Found 172 results in Advanced Search (ALL) in Web of Science ([https://apps.webofknowledge.com/ WoSc]) (16/06/2020). Citations of all these search result add up to 714. This however does not mean, that all search results really explicitly used data from ENTSO-E. Some (for sure, for some were found) only mentioned it saying they adoped workflow etc. from the site.
* '''SMARD''': Found 82 results in Advanced Search (ALL=(SMARD)) in Web of Science ([https://apps.webofknowledge.com/ WoSc]) (16/06/2020). However: SMARD is also an acronym for ''Spinal muscular atrophy with respiratory distress (SMARD)''. This somewhat complicates results and needs some further discussion. All 82 search results accumumlated 2359 citations themselves.

== Results for SMILES ==

{| class="wikitable mw-collapsible"
! DC element name
! DC definition ([https://www.dublincore.org/specifications/dublin-core/dces/ DCMI])
! Category (determined with the aid of [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO])
! rowspan = "16" style="background: #ABE" |
! SMILES (ED4)
! Notes (SMILES (ED4) )

|-
! Title
| A name given to the resource
|| Descriptive
|| A
||
|-
! Subject
| The topic of the resource || Descriptive || A ||
|-
! Description
| An account of the resource || Descriptive || PA || The description of the data is very limited. For all files a general description is given. This is linked to the project deliverables but it is not indicated which deliverable are exactly corresponding. This seems to be a time-consuming process for researchers.
|-
! Creator
| An entity primarily responsible for making the resource || Descriptive || A || The metadata file uses the tag "author"
|-
! Publisher
| An entity responsible for making the resource available || Descriptive || A || This information only available under the URL but not in the metadata file
|-
! Contributor
| An entity responsible for making contributions to the resource || Descriptive || NA ||
|-
! Date
| A point or period of time associated with an event in the lifecycle of the resource || Administrative || A || This information only available under the URL but not in the metadata file
|-
! Type
| The nature or genre of the resource || Descriptive || NA || No explicit mention of the type (e.g. document, dataset) although it could be derived from the context
|-
! Format
| The file format, physical medium, or dimensions of the resource || Structural || PA || The metadata element "format" is not exactly indicated but the metadata tag "file" includes the format type at the end (".csv"). Additionally, the tag "datatype" is used to specify the internal data type (e.g. integer, string) of the data file.
|-
! Identifier
| An unambiguous reference to the resource within a given context || Descriptive || NA ||
|-
! Source
| A related resource from which the described resource is derived || Descriptive || NA ||
|-
! Language
| A language of the resource || Descriptive || NA || No explicit mention of the language although it could be derived from the context
|-
! Relation
| A related resource || Descriptive || PA || This information (EU grant information) only generally available under the URL but not in the metadata file
|-
! Coverage
| The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant || Descriptive || NA ||
|-
! Rights
| Information about rights held in and over the resource || Administrative || NA ||
|-
! colspan = "5" style="background: #ABE" |
|-
!
|}

Gap analysis

2020-08-31T09:53:08Z

Christopherbs: /* Results for SMILES */

<accesscontrol>Administrators,consortium</accesscontrol>

This is the page for the gap analysis.

= Tacit knowledge "FAIRification and opening of low carbon energy research data" =
 Consortium members can add any time, they feel that something is important to note even though it is not mentioned in a standard deliverable. In other words, this page collects uncodified, tacit knowledge. It will help us later to compile suggestions and lessons learned. This kind of knowledge is collected two-fold:
#Anytime if someone feels that this should be noted. Please write down: Issue, Date, Author (could also be "anonymous"), the issue described in a few words or maximal lines>
#During the final day of workshops

=== Learning process: ===

*People are hesitant to adopt new IT technologies, this is even the case among researchers heavily relying on data, algorithms and collaborative online software (e.g., R, platforms, online conferencing, HPC, ...). The effort to encourage change is not to underestimate. Reasons are several, notably, lack of time and uncertainty about potential benefit as well as overall risk aversion preferences. EERAdata is using the online software "Only Office" to facilitate collaboration (in particular also during the Covid-19 period).
 '''FAIR and open criteria:'''
*Consortium members have a fair understanding of what FAIR/O is, but there is little knowledge and/or technical experience on how to approach the FAIRification and opening. All, however, share the view that we are at a critical point in time, where we need to implement these criteria. 
*To deepen knowledge about FAIR/O criteria, it is useful to test the criteria on a database one is familiar with. For this purpose (and to start brainstorming about the platform), AIT has developed a questionnaire for application in the use cases.
'''Metadata:'''
*A good starting point is to think about metadata and to look into existing metadata concepts in one's field. The first step is to understand that also metadata need to adhere to the FAIR/O principles.
*The next step is to increase knowledge on IT specific terms, i.e. to understand what the difference is between different '''[https://energydata.onlyoffice.eu/Products/Files/doceditor.aspx?fileid=5404340 metadata frameworks] (taxonomy, thesaurus, ontology) as well as classification of metadata (high-level, medium-level, low-level OR administrative, structural and descriptive metadata).  '''WP 2 being in charge of aligning approaches between use cases, participates in all use case kickoffs to bring everybody on the same page. The presentation is linked with "metadata frameworks".
*It is useful to supply consortium members with read aheads and watch aheads on metadata to prepare the '''[https://energydata.onlyoffice.eu/Products/Files/doceditor.aspx?fileid=5296590 first EERAdata workshop]'''. The workshop starts applications and discussions in the use cases break out sessions (and bringing insights back to the plenary), using selected databases.

= FAIR Assessment=

== FAIR Guiding Principles ==
{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ This table shows the FAIR guiding principles described by [https://www.doi.org/10.1038/sdata.2016.18 Wilkinson et al., 2016].

! To be Findable
|
* F1. (meta)data are assigned a globally unique and persistent identifier
* F2. data are described with rich metadata (defined by R1 below)
* F3. metadata clearly and explicitly include the identifier of the data it describes
* F4. (meta)data are registered or indexed in a searchable resource

|-
! To be Accessible
|
* A1. (meta)data are retrievable by their identifier using a standardized communications protocol
** A1.1 the protocol is open, free, and universally implementable
** A1.2 the protocol allows for an authentication and authorization procedure, where necessary
* A2. metadata are accessible, even when the data are no longer available

|-
! To be Interoperable
|
* I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
* I2. (meta)data use vocabularies that follow FAIR principles
* I3. (meta)data include qualified references to other (meta)data

|-
! To be Reusable
|
* R1. meta(data) are richly described with a plurality of accurate and relevant attributes
* R1.1. (meta)data are released with a clear and accessible data usage license
* R1.2. (meta)data are associated with detailed provenance
* R1.3. (meta)data meet domain-relevant community standards

|}

== Identified gaps after Workshop 1 (02/06/2020 – 04/06/2020) ==

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ This table shows a summary of the specific issues that were identified in Workshop 1. The aim was to categorise different issues to get a better understanding of how to tackle these challenges.
! FAIR/O !! Specific FAIR/O !! Gaps !! Gaps for energy domain !! Use case specific gaps !! Consequences for researchers
|-
! F
| F2
|| Metadata scope
|| Different fields require different metadata (potentially very specific)
|| UC3: lack of additional metadata for applications of materials
|| Data are less useful for the specific field
|-
! F
|| F1/F3
|| Identifiers
||
* Missing identifiers on websites
* Identifier not in downloaded data files
||
|| Makes research more difficult.
|-
! I
| I2
|| Taxonomy/ontology/common vocabulary and language issues
||
* Lack of standardisation
* Heterogeneous data makes standardisation hard
* No vocabulary documentation on websites
* Words used for same term in other languages may differ
* Databases in other languages than English
||
|| Research costs more time
|-
! I
| I3
|| References to other (meta)data
||
* No linking to source documents and related publications (for contextual knowledge)
* Possible need for links to other fields
|| UC3: link between microscopic and macroscopic materials (e.g., turbine blades)
||
* Data cannot be connected to the source
* Makes research more difficult.

|-
! R
| R1.1
|| Licensing
||
* No licenses available may mean the data is not reusable for the researcher
* Licenses not clear and accessible
* Obtaining licenses may result in more effort and costs
||
|| Data potentially not reusable

|-
! R
| R1.2
|| Reliability and provenance of data
|| When the data is collected from different and high number of sources, its reliability decreases
||
|| Data potentially not reusable

|-
! O
|
|| Privacy concerns and expected disadvantages
|| Not publishing data due to privacy concerns (sensitive data)
|| UC2: In case of distribution network data this is relevant
|| Data not accessible

|-
! Other
|
|| Conducting FAIR assessments
||
* No qualitative assessment of the data itself (which may be limited)
* Discrepancies between results conducted by humans versus machines
* Sometimes not even DC standards are met
* Metadata is not updated
* Lacking encryption of websites (https)
* Problems assessing whether metadata will be available after data is unavailable
* Interface design/layout may be unclear, incomplete or not intuitive for humans
* Authentication details (user registration login / good or bad, clarify)

||
* UC1 lack of data availability for time-series
||
* Assessment is uncertain (human assessment may need more clarification and understanding)
* Makes research more time-intensive

|}

== FAIR Self-Assessment tool ==

To conduct a human FAIR evaluation for different databases and their respective datasets, different free online tools were considered. Some seemed to be quite useful (e.g. [https://satifyd.dans.knaw.nl/ SATISFYD] ) and some were simple self-evaluation PDFs to be printed and filled out.

We decided to chose the [https://ardc.edu.au/resources/working-with-data/fair-data/fair-self-assessment-tool/ self-assessment tool] provided by the Australian Research Data Commons (ARDC). The decision was based manly upon the fact that the tools questions closely matched the FAIR categories specified by [https://www.doi.org/10.1038/sdata.2016.18 Wilkinson et al., 2016] which would make it easily comparable to the existing preferable FAIR conditions and aid with the repeatability of the results, and the half-automated scoring features that show the extent of the "FAIRness" of the analysed sample as a simple bar chart.

The result of the FAIR assessment is provided as a bar chart where an empty bar chart displays 0 % FAIR compatibility, and a full bar chart represents 100 % FAIR compatibility. The score is therefore not returned as a specific value. During the assessment below, the "Total Across FAIR" in Table ... was estimated according to the level of the bar chart from 0 % to 100 %.
Resulting scores where categorised as follows: Low: < 50%; Medium: 50-60%; High: > 60% .

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ All options for each question of the ARDCs self-assessment tool
!colspan="4"|Findable
!colspan="3"|Accessible
!colspan="3"|Interoperable
!colspan="3"|Reusable
|-
| F1. (meta)data are assigned a globally unique and persistent identifier
|| F2. data are described with rich metadata (defined by R1 below)
|| F3. metadata clearly and explicitly include the identifier of the data it describes
|| F4. (meta)data are registered or indexed in a searchable resource
|| A1. (meta)data are retrievable by their identifier using a standardized communications protocol
|| A1.1 the protocol is open, free, and universally implementable and A1.2 the protocol allows for an authentication and authorization procedure, where necessary
|| A2. metadata are accessible, even when the data are no longer available
|| I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
|| I2. (meta)data use vocabularies that follow FAIR principles
|| I3. (meta)data include qualified references to other (meta)data
|| R1.1. (meta)data are released with a clear and accessible data usage license
|| R1.2. (meta)data are associated with detailed provenance
|| R1.3. (meta)data meet domain-relevant community standards
|-
! Does the dataset have any identifiers assigned?
! Is the dataset identifier included in all metadata records/files describing the data?
! How is the data described with metadata?
! What type of repository or registry is the metadata record in?
! How accessible is the data?
! Is the data available online without requiring specialised protocols or tools once access has been approved?
! Will the metadata record be available even if the data is no longer available?
! What (file) format(s) is the data available in?
! What best describes the types of vocabularies/ontologies/tagging schemas used to define the data elements?
! How is the metadata linked to other data and metadata (to enhance context and clearly indicate relationships)?
! Which of the following best describes the license/usage rights attached to the data?
! How much provenance information has been captured to facilitate data reuse?
!
|-
| Globally unique, citable and persistant (e.g. DOI, PURL, ARK or Handle)
|| Yes
|| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
|| Data is in one place but discoverable through several registries
|| Publicly accessible
|| Standard web service API (e.g. OGC)
|| Yes
|| In a structured, open standard, machine-readable format
|| Standardised open and universal using resolvable global identifiers linking to explainations
|| Meadata is represented in a machine readable format, e.g. in a linked data format such as Resource Description Framework (RDF)
|| Standard machine-readable license (e.g. Creative Commons)
|| Fully recorded in a machine-readable format
||
|-
| Web adress (URL)
|| No
|| Comprehensively, but in a text-based, non-standard format
|| Generalist public repository
|| Fully accessible to person who meet explicitly stated conditions, e.g. ethics approval for sensitive data
|| Non-standard web service (e.g. OpenAPI/Swagger/informal API)
|| No
|| In a structured, open standard, non-machine-readable format
|| Standardised vocabularies/ontologies/schema without global identifiers
|| The metadata record includes URI links to related metadata, data and definitions
|| Standard text based license
|| Fully recorded in a text format
||
|-
| Local identifier
||
|| Brief title and description
|| Domain-specific repository
|| A de-identified / modified subset of the data is publicly accessible
|| File download from online location
|| Unsure
|| Mostly in a proprietary format
|| No standards have been applied in the description of data elements
|| There are no links to other metadata
|| Non-standard machine-readably license (clearly indicating under what conditions the data may be reused)
|| Partially recorded
||
|-
| No identifier
||
|| The data is not described
|| Local institutional repository
|| Embaged access after a specific date
|| By individual arrangement
||
||
|| Data elements not described
||
|| Non-standard text-based license
|| No provenance information is recorded
||
|-
|
||
||
|| The data is not described in any repository
|| Unspecified conditional access e.g. contact the data custodian for access
|| No access to data
||
||
||
||
|| No license
||
||
|-
|
||
||
||
|| Access to metadata only
||
||
||
||
||
||
||
||
|-
|
||
||
||
|| No access to data or metadata
||
||
||
||
||
||
||
||
|}

At this point it is important to note that the assessment of databases and/or datasets using this tool is solely considered a human assessment. This may lead to different evaluations by different users. To counteract this phenomenon, it was suggested that the evaluations should be completed using the 'four-eyes principle' to create more verifiable results. Also, it is planned to additionally evaluate the respective databases using an external tool to perform a machine-assessment.

In addition, not all questions are weighted equally and thus a worse result for one question may not have a large impact whereas only choosing the second-best option in another question will bring down the overall FAIR value considerably.

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ The ARDC tool provides detailed information about the requirements for each of the options of its tool. These descriptions are documented here.
|-
! rowspan="5" | F
|| F
|| About Findable
|| Making data Findable includes assigning a [https://www.ands.org.au/guides/persistent-identifiers-awareness persistent identifier] (like a [https://www.ands.org.au/guides/doi DOI] or [https://www.ands.org.au/guides/identify-my-data Handle] ), having rich [https://www.ands.org.au/working-with-data/metadata metadata] to describe the data and making sure it is findable through disciplinary and generalist discovery portals (local and international).

|-
| F1
|| About identifiers
|| Identifiers are essential for identifying, finding, retrieving, linking and citing datasets. A Web address (URL) can be used to specify the online location of a resource but over time URLs tend to change which leads to broken links to the data. To be useful, identifiers need to be persistent and unique. Digital Object Identifiers (DOIs) and other persistent identifiers (PIDs) provide a permanent citable reference to a particular dataset. The DOI is a permanent fixed reference to the dataset no matter where it is located online and enables citation and citation metrics. Services to create a persistent identifier are often offered by your affiliated institution or the repository you are using to describe your data. Talk to your library service for more information.

Read more: [https://www.ands.org.au/working-with-data/citation-and-identifiers Persistent identifiers]

|-
| F2
|| About identifiers
|| The identifier (preferably a persistent identifier) needs to be clearly stated in the metadata record describing the data collection, and also in any associated data files or metadata.

|-
|F3
|| About metadata descriptions
|| Comprehensive metadata records will include descriptive content that facilitates discovery, access and reuse of the data being described. While there is no 'one size fits all' list, comprehensive metadata should include:
* a globally unique persistent identifier e.g. a DOI
* a title
* related people, i.e. the data creator or custodian
* how to access the data and file formats
* a description of how the data were created and how to interpret the data subject or keywords
* citation information that clearly indicates how the data should be cited
* a machine-readable data licence
* provenance and contextual information such as:
* links to related publications, projects, services and software
* methodology and processes involved in data production
* spatial and temporal coverage (if relevant)
* object-level data description

Providing metadata in a standard schema allows it to be read and used by machines as well as humans.

Read more: [https://www.ands.org.au/working-with-data/metadata Metadata]

|-
| F4
|| About metadata repositories
|| A rich metadata description alone does not ensure a dataset’s ‘findability’ on the internet; the dataset needs to be registered or indexed in a searchable resource, such as a generalist (e.g. [https://figshare.com/ Figshare], [http://datadryad.org/ Dryad], [https://zenodo.org/ Zenodo], domain-specific (e.g. [https://pangaea.de/ PANGAEA], [https://ada.edu.au/ ADA], [https://www.icpsr.umich.edu/ ICPSR]), or institutional (e.g. [https://openresearch-repository.anu.edu.au/ ANU Research Repository] , [https://ses.library.usyd.edu.au/ Sydney eScholarship Repository], [https://data.gov.au/ data.gov.au]) data repository or registry. Ideally these repositories/registries are indexed by search engines such as Google and/or Google Scholar.

|-
! rowspan = "4" | A
| A
|| About Accessible
|| [https://www.ands.org.au/working-with-data/publishing-and-reusing-data/publishing To make data accessible] may include making the data [https://www.ands.org.au/working-with-data/articulating-the-value-of-open-data/open-data open] using a standardised protocol. However the data does not necessarily have to be open. There are sometimes good reasons why data [https://www.ands.org.au/working-with-data/sensitive-data cannot be made open], for example privacy concerns, national security or commercial interests. If it is not open there should be clarity and transparency around the conditions governing access and [https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse reuse].
|-
| A1
|| About data accessibility
|| Not all data that is discoverable can be freely accessed. Often there are embargoes, access controls, and access permissions associated with data due to a variety of issues such as privacy and commercial interests. Even with all these issues much [https://www.ands.org.au/working-with-data/sensitive-data sensitive data] can be shared. Many other issues that could be perceived as blockers to sharing data may be [http://datapub.cdlib.org/closed-data-excuses-excuses overcome].
|-
| A1.1/A1.2
|| About protocols
|| Ideally users would like to retrieve appropriate internet content directly and unhindered once they have located it. Internet protocols (e.g. http and ftp) define rules and conventions for communication between devices, and tools and services are available to facilitate this process, e.g. APIs.

Protocols:
* [https://www.lifewire.com/hypertext-transfer-protocol-817944 HTTP] (Hypertext Transfer Protocol) is the set of rules for transferring files (text, graphic images, sound, video, and other multimedia files) on the [https://whatis.techtarget.com/definition/World-Wide-Web World Wide Web]. As soon as a Web user opens their Web browser, the user is indirectly making use of HTTP.
* [https://www.lifewire.com/file-transfer-protocol-817943 FTP] (File Transfer Protocol) is a standard Internet [http://searchnetworking.techtarget.com/definition/protocol protocol] for transmitting files between computers on the Internet over TCP/IP connections. Read more: [https://www.lifewire.com/definition-of-protocol-network-817949 Web protocols]
* API (Application Programming Interface): When information is made available in any machine readable format, it becomes possible to make that information directly available to programs that request that information over the web. An API is the way this information is made directly available to other machines.
View more: [https://www.youtube.com/watch?v=s7wmiS2mSXY What is an API?]

OGC - http://www.opengeospatial.org/ogc

|-
| A2
|| About metadata availability
|| Effort is often required to maintain data resources online which can often lead to it being neglected especially over long periods of time. This often leads to broken links between the metadata and the data. Having at least a description of the data in the form of a metadata record means there is a record of the data's existence allowing the possibility of someone tracking it down.

|-
! rowspan="4" | I
|| I
|| About Interoperability
|| To be interoperable (i.e. data that is interpretable by a computer, so that they can be automatically combined with other data) the data will need to use community agreed [https://www.ands.org.au/guides/file-formats formats], language and [https://www.ands.org.au/guides/vocabularies-and-research-data vocabularies]. The [https://www.ands.org.au/working-with-data/metadata metadata] will also need to use a community agreed standards and vocabularies, and contain links to related information using [https://www.ands.org.au/working-with-data/citation-and-identifiers identifiers].
|-
|| I1
|| About file formats
|| When selecting file formats for archiving, the formats should ideally be:
* Non-proprietary
* Unencrypted
* Uncompressed
* In common usage by the research community
* Adherent to an open, documented standard, such as described by the State of California (see [http://www.leginfo.ca.gov/pub/07-08/bill/asm/ab_1651-1700/ab_1668_bill_20070524_amended_asm_v96.pdf AB 1668, 2007])
** Interoperable among diverse platforms and applications
** Fully published and available royalty-free
** Fully and independently implementable by multiple software providers on multiple platforms without any intellectual property restrictions for necessary technology
** Developed and maintained by an open standards organization with a well-defined inclusive process for evolution of the standard.

From: [https://library.stanford.edu/research/data-management-services/data-best-practices/best-practices-file-formats Stanford University Library - Best practices for file formats]

File format examples:
* Structured, open standard, machine-readable format e.g. (text) PDF/A, HTML, Plain text, (images) TIFF, JPEG 2000, GIF, (audio) MP3, AIFF, WAVE, (video) MOV, MPEG, AVI, (Tabular data) CSV
* structured, open standard, non-machine-readable format, e.g. PDF, HTML, JPG
* Proprietary format, e.g. doc (Word), .xls (Excel), .ppt (PowerPoint), .sav

Read more:
* [https://www.ands.org.au/__data/assets/pdf_file/0003/731775/File-Formats.pdf ANDS]
* [https://www.europeandataportal.eu/elearning/en/module9/#/id/co-01 European Data Portal]

|-
|| I2
|| About schema standards
|| Schema standards are schemas that have gone through a formal validation process by a standards organisation, such as the International Standards Organisation (ISO) or an equivalent body such as the Dublin Core Metadata Initiative (DCMI); or, commonly used and consistently applied metadata schemas that are well documented, endorsed, and maintained can also become ‘de-facto standards’, e.g. RIF-CS for describing data collections and services used in Research Data Australia.

Read more: [https://www.ands.org.au/guides/metadata-working Metadata schema]

Standardised open and universal schemas, e.g, [https://www.dcc.ac.uk/resources/metadata-standards/datacite-metadata-schema DataCite Metadata Schema], [https://www.dcc.ac.uk/resources/metadata-standards/prov PROV], [https://www.dcc.ac.uk/resources/metadata-standards/dublin-core Dublin Core]

Domain-specific standards, e.g. [http://human-phenotype-ontology.github.io/ HPO - Human Phenotype Ontology], [https://www.nlm.nih.gov/mesh/ MeSH - Medical Subject Headings] , [https://mcp-profile-docs.readthedocs.io/en/stable/ Marine Community Profile], [https://ddialliance.org/ DDI - Data Documentation Initiative].

For more examples of standards: [http://rd-alliance.github.io/metadata-directory/ Here].

Resolvable global identifiers - An identifier is any label used to name something uniquely (whether online or offline). URLs are an example of an identifier. So are serial numbers, and personal names. A persistent identifier is guaranteed to be managed and kept up to date over a defined time period.

Read more: [https://www.ands.org.au/guides/persistent-identifiers-awareness Persistent Identifiers]

|-
||I3
|| About metadata links
|| The goal is to create as many meaningful linkages as possible between (meta)data resources to enrich the contextual knowledge about the data, balanced against the time/energy involved in making a good data model.

'''Machine-readable (meta)data'''

(Meta)data in a format that can be automatically read and processed by a computer, such as [http://opendatahandbook.org/glossary/en/terms/csv/ CSV], [http://opendatahandbook.org/glossary/en/terms/json/ JSON], [http://opendatahandbook.org/glossary/en/terms/xml/ XML] etc. For more information: [http://opendatahandbook.org/glossary/en/terms/machine-readable/ Open Data Handbook]

'''Linked Data'''

Linked Data (also known as Linking Data) can be applied to improve the exploitation of the “Web of data.” The expression refers to the publishing of structured data in a way that typed links are created between data from different sources to provide a higher level of usability. By using Linked Data, it is possible to find other, related data. Structured data should meet four requirements to be called
Linked Data:

* URIs should be assigned to all entities of the dataset.
* HTTP URIs are required to ensure that all entities can be referenced and cited by users and user agents.
* Entities should be described using standard formats such as RDF/XML.
* Links should be created to other, related entity URIs.

All data that fulfil these requirements and are released for the public are called Linked Open Data (LOD). http://www.lesliesikos.com/semantic-web-machine-readable-structured-data-with-meaningful-annotations/

Read more under the heading (meta)data include qualified references to other (meta)data in the following resource: https://www.dtls.nl/fair-data/fair-principles-explained/

For more information on Linked Data standards - RDF - https://www.w3.org/RDF/

Other related linked data standards:
* OWL - https://www.w3.org/OWL/
* SKOS - https://www.w3.org/2004/02/skos/
* SPARQL - https://www.w3.org/TR/rdf-sparql-query/

|-
! rowspan="3" | R
|| R
|| About Reusable
|| Reusable data should maintain its initial richness. For example, it should not be abridged for the purpose of explaining the findings in one particular publication. It needs a clear machine-readable [https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse licence] and [https://www.ands.org.au/partners-and-communities/ands-communities/data-provenance-interest-group provenance] information on how the data was formed. It should also use discipline-specific data and [https://www.ands.org.au/working-with-data/metadata metadata] standards to give it rich contextual information that will allow for accurate interpretation and reuse.

|-
|| R1.1
|| About licenses
|| If data is not licensed no-one else can use it. In Australia, no licence is regarded as the same as 'all rights reserved', confining any reuse to very limited circumstances. Applying a Creative Commons licence to your data is a simple way to ensure that your data can be reused. The less restrictive the licence, the more that can be done with the data.
To make it easy for the Web to know when a work is available under a particular license, a “machine readable” version of the license provides a summary of the key freedoms and obligations written into a format that software systems, search engines, and other kinds of technology can understand.
Example from the [https://creativecommons.org/choose/ Creative Commons License Choser]:

What appears in the metadata:

This work is licensed under a [https://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International License].

Code snippet:

<code>
<a rel="license" href="http://creativecommons.org/licenses/by/4.0/">

<img alt="Creative Commons License" style="border-width:0"

src="https://i.creativecommons.org/l/by/4.0/88x31.png"/>

</a> This work is licensed under a

<a rel="license" href="http://creativecommons.org/licenses/by/4.0/">

Creative Commons Attribution 4.0 International License</a>.
<\code>

Read more: https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse

|-
||R1.2
|| About provenance
|| Data provenance is used to document where a piece of data comes from and the process and methodology by which it is produced. It is important to confirm the authenticity of data enabling trust, credibility and reproducibility. This is becoming increasingly important, especially in the eScience community where research is data intensive and often involves complex data transformations and procedures ([https://www.ands.org.au/partners-and-communities/ands-communities/data-provenance-interest-group Read more]). Provenance vocabularies such as [https://www.w3.org/2005/Incubator/prov/XGR-prov-20101214/#Recommendations Provenir Ontology (PROV-O) and others].

|}

== Results for SMILES and ShareWind ==

{| class="mw-collapsible wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Datasets from the SMILES and ShareWind platforms was analysed using the self-assessment tool provided by ARDC.
! rowspan="5" |
| rowspan="5" |
! Database Code
| rowspan="24" style="background: #ABE" |
| ED4
| ED9
|-
! Database Title
|| SMILES
| ShareWind
|-
! Database Link
|| https://www.ecria-smiles.eu/
| https://sharewind.eu/
|-
! Sample Link
|| [https://www.ecria-smiles.eu/data-files/-/document_library/W32cCAfL2jMX/view_file664466?_com_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX_redirect=https%3A%2F%2Fwww.ecria-smiles.eu%3A443%2Fdata-files%2F-%2Fdocument_library%2FW32cCAfL2jMX%2Fview%2F664423%3F_com_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX_redirect%3Dhttps%253A%252F%252Fwww.ecria-smiles.eu%253A443%252Fdata-files%253Fp_p_id%253Dcom_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX%2526p_p_lifecycle%253D0%2526p_p_state%253Dnormal%2526p_p_mode%253Dview here] (link has 585 characters)
| https://sharewind.eu/records/f9d31abbc2824284b8c1d28894d9619a (data found at https://zenodo.org/record/1162738)
|-
! Sample Size
|| 4 KB
| 37 MB
|-
| colspan="6" style="background: #ABE" |
|-
! rowspan="4" | Findable
| F1. (meta)data are assigned a globally unique and persistent identifier
! Does the dataset have any identifiers assigned?
|| Web adress (URL)
| Globally unique, citable and persistant (e.g. DOI, PURL, ARK or Handle)
|-

| F2. data are described with rich metadata (defined by R1 below)
! Is the dataset identifier included in all metadata records/files describing the data?
|| No
| No
|-

| F3. metadata clearly and explicitly include the identifier of the data it describes
! How is the data described with metadata?
|| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
|-

| F4. (meta)data are registered or indexed in a searchable resource
! What type of repository or registry is the metadata record in?
|| Domain-specific repository
| Data is in one place but discoverable through several registries
|-

! rowspan="3" | Accessible

| A1. (meta)data are retrievable by their identifier using a standardized communications protocol
! How accessible is the data?
|| Publicly accessible
| Publicly accessible
|-
| A1.1 the protocol is open, free, and universally implementable; A1.2 the protocol allows for an authentication and authorization procedure, where necessary
! Is the data available online without requiring specialised protocols or tools once access has been approved?
|| File download from online location
| Standard web service API (e.g. OGC)
|-
| A2. metadata are accessible, even when the data are no longer available
! Will the metadata record be available even if the data is no longer available?
|| Unsure
| Yes
|-

! rowspan="3" | Interoperable
| I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
! What (file) format(s) is the data available in?
|| In a structured, open standard, machine-readable format
|| In a structured, open standard, non-machine-readable format
|-
| I2. (meta)data use vocabularies that follow FAIR principles
! What best describes the types of vocabularies/ontologies/tagging schemas used to define the data elements?
|| Standardised vocabularies/ontologies/schema without global identifiers
|| Standardised vocabularies/ontologies/schema without global identifiers
|-
| I3. (meta)data include qualified references to other (meta)data
! How is the metadata linked to other data and metadata (to enhance context and clearly indicate relationships)?
|| There are no links to other metadata
|| Metadata is represented in a machine readable format, e.g. in a linked data format such as Resource Description Framework (RDF)
|-

! rowspan="3" | Reusable
| R1.1. (meta)data are released with a clear and accessible data usage license
! Which of the following best describes the license/usage rights attached to the data?
|| No license
| Standard machine-readable license (e.g. Creative Commons)
|-
| R1.2. (meta)data are associated with detailed provenance
! How much provenance information has been captured to facilitate data reuse?
|| Partially recorded
|| Partially recorded
|-
| R1.3. (meta)data meet domain-relevant community standards
|
|
|
|-
| colspan="6" style="background: #ABE" |
|-
! Total across F.A.I.R
|
! Low: < 50%; Medium: 50-60%; High: > 60%
|| ~ 45 %
|| ~ 85 %
|-
| colspan="6" style="background: #ABE" |
|-
! Remarks about database
||
||
||
|| The data is provided by the open-source platform Zenodo, not the project website itself. A very positive aspect is that information on different versions of the data as well as the number of views and downloads is available.
|-
! Identified gaps
||
||
||
* No DOI, only URL
* Extremely long URL link spanning several lines with 585 characters, that can easily become corrupted
* No links to other metadata
* No license information given
* Provenance not fully recorded

||
* Dataset identifiers should be included in the files describing the data
* Provenance details are only partially recorded with a note with contact name for further information
|}

= Metadata assessment =

== Dublin Core Elements ==
{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ The DCMI Metadata Terms lists the current set of the Dublin Core vocabulary. Source: [https://dublincore.org/specifications/dublin-core/dcmi-terms/ Dublin Core Metadata Initiative]
| 1 || abstract || 11 || contributor || 21 || extent || 31 || isReplacedBy || 41 || publisher || 51 || tableOfContents
|-
| 2 || accessRights || 12 || coverage || 22 || format || 32 || isRequiredBy || 42 || references || 52 || temporal
|-
| 3 || accrualMethod || 13 || created || 23 || hasFormat || 33 || issued || 43 || relation || 53 || title
|-
| 4 || accrualPeriodicity || 14 || creator || 24 || hasPart || 34 || isVersionOf || 44 || replaces || 54 || type
|-
| 5 || accrualPolicy || 15 || date || 25 || hasVersion || 35 || language || 45 || requires || 55 || valid
|-
| 6 || alternative || 16 || dateAccepted || 26 || identifier || 36 || license || 46 || rights || ||
|-
| 7 || audience || 17 || dateCopyrighted || 27 || instructionalMethod || 37 || mediator || 47 || rightsHolder || ||
|-
| 8 || available || 18 || dateSubmitted || 28 || isFormatOf || 38 || medium || 48 || source || ||
|-
| 9 || bibliographicCitation || 19 || description || 29 || isPartOf || 39 || modified || 49 || spatial || ||
|-
| 10 || conformsTo || 20 || educationLevel || 30 || isReferencedBy || 40 || provenance || 50 || subject || ||
|}

'''Metadata Category Types according to [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO]'''

There are three main types of metadata:
* Descriptive metadata describes a resource for purposes such as discovery and identification. It can include elements such as title, abstract, author, and keywords.
* Structural metadata indicates how compound objects are put together, for example, how pages are ordered to form chapters.
* Administrative metadata provides information to help manage a resource, such as when and how it was created, file type and other technical information, and who can access it. There are several subsets of administrative data; two that sometimes are listed as separate metadata types are:
** Rights management meta-data, which deals with intellectual property rights,and
** Preservation metadata, which contains information needed to archive and preserve a resource.
''

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Simple Dublin Core Elements. 15 DC elements with their (shortened) official definitions and examples.
! DC element name !! DC definition ([https://www.dublincore.org/specifications/dublin-core/dces/ DCMI]) !! Example (Energy Domain) !! Category (determined with the aid of [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO])
|-
! Title
| A name given to the resource || Outdoor monitoring of a hybrid micro-CPV solar panel with integrated micro-tracking and diffuse capture || Descriptive
|-
! Subject
| The topic of the resource || concentrator photovoltaics; CPV; rooftop photovoltaics; integrated tracking || Descriptive
|-
! Description
| An account of the resource || Dataset from the outdoor characterization of a B Series module from Insolight at the rooftop of the Instituto de Energía Solar - Universidad Politécnica de Madrid. …. || Descriptive
|-
! Creator
| An entity primarily responsible for making the resource || Stephen Askins; Gaël Nardin || Descriptive
|-
! Publisher
| An entity responsible for making the resource available || Stephen Askins || Descriptive
|-
! Contributor
| An entity responsible for making contributions to the resource || Stephen Askins || Descriptive
|-
! Date
| A point or period of time associated with an event in the lifecycle of the resource || 2019-07-23; Date will be associated with the creation or availability of the resource. || Administrative
|-
! Type
| The nature or genre of the resource || info:eu-repo/semantics/other; dataset || Descriptive
|-
! Format
| The file format, physical medium, or dimensions of the resource || image
|| Structural
|-
! Identifier
| An unambiguous reference to the resource within a given context || https://zenodo.org/record/3346823; 10.5281/zenodo.3346823; oai:zenodo.org:3346823 || Descriptive
|-
! Source
| A related resource from which the described resource is derived || RC607.A26W574 1996 [where "RC607.A26W574 1996" is the call number of the print version of the resource, from which the present version was scanned] || Descriptive
|-
! Language
| A language of the resource || eng || Descriptive
|-
! Relation
| A related resource || info:eu-repo/grantAgreement/EC/H2020/787289/ || Descriptive
|-
! Coverage
| The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant || 1995-1996; Madrid, Spain || Descriptive
|-
! Rights
| Information about rights held in and over the resource || info:eu-repo/semantics/openAccess; https://creativecommons.org/licenses/by/4.0/legalcode || Administrative
|}

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Non-DC elements

! Non-DC element name !! Definition !! Example !! Category
|-
! Date of availability
| Since when is the data available
||
|| Impact/Exploitation

|-
! Number of users
| Is there any information on the number of accesses or users?
||
|| Impact/Exploitation

|-
! Number of scientific publications where data is cited/used
| Is there any information on the number of citations/uses?
||
|| Impact/Exploitation

|-
! Curation activities (is the versioning ongoing)
| Update, maintainance
||
|| Impact/Exploitation
|}

Attempting to put a value on Impact/Exploitation:
* '''ENTSO-E''': Found 172 results in Advanced Search (ALL) in Web of Science ([https://apps.webofknowledge.com/ WoSc]) (16/06/2020). Citations of all these search result add up to 714. This however does not mean, that all search results really explicitly used data from ENTSO-E. Some (for sure, for some were found) only mentioned it saying they adoped workflow etc. from the site.
* '''SMARD''': Found 82 results in Advanced Search (ALL=(SMARD)) in Web of Science ([https://apps.webofknowledge.com/ WoSc]) (16/06/2020). However: SMARD is also an acronym for ''Spinal muscular atrophy with respiratory distress (SMARD)''. This somewhat complicates results and needs some further discussion. All 82 search results accumumlated 2359 citations themselves.

== Results for SMILES ==

{| class="wikitable mw-collapsible"
! DC element name
! DC definition ([https://www.dublincore.org/specifications/dublin-core/dces/ DCMI])
! Category (determined with the aid of [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO])
! rowspan = "16" style="background: #ABE" |
! SMILES (ED4)
! Notes (SMILES (ED4) )

|-
! Title
| A name given to the resource
|| Descriptive
|| A
||
|-
! Subject
| The topic of the resource || Descriptive || A ||
|-
! Description
| An account of the resource || Descriptive || PA || The description of the data is very limited. For all files a general description is given. This is linked to the project deliverables but it is not indicated which deliverable are exactly corresponding. This seems to be a time-consuming process for researchers.
|-
! Creator
| An entity primarily responsible for making the resource || Descriptive || A || The metadata file uses the tag "author"
|-
! Publisher
| An entity responsible for making the resource available || Descriptive || A || This information only available under the URL but not in the metadata file
|-
! Contributor
| An entity responsible for making contributions to the resource || Descriptive || NA ||
|-
! Date
| A point or period of time associated with an event in the lifecycle of the resource || Administrative || A || This information only available under the URL but not in the metadata file
|-
! Type
| The nature or genre of the resource || Descriptive || NA || No explicit mention of the type (e.g. document, dataset) although it could be derived from the context
|-
! Format
| The file format, physical medium, or dimensions of the resource || Structural || PA || The metadata element "format" is not exactly indicated but the metadata tag "file" includes the format type at the end (".csv"). Additionally, the tag "datatype" is used to specify the internal data type (e.g. integer, string) of the data file.
|-
! Identifier
| An unambiguous reference to the resource within a given context || Descriptive || NA ||
|-
! Source
| A related resource from which the described resource is derived || Descriptive || NA ||
|-
! Language
| A language of the resource || Descriptive || NA || No explicit mention of the language although it could be derived from the context
|-
! Relation
| A related resource || Descriptive || PA || This information (EU grant information) only generally available under the URL but not in the metadata file
|-
! Coverage
| The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant || Descriptive || NA ||
|-
! Rights
| Information about rights held in and over the resource || Administrative || NA ||
|}

Gap analysis

2020-08-31T09:49:09Z

Christopherbs: /* Results for SMILES */

<accesscontrol>Administrators,consortium</accesscontrol>

This is the page for the gap analysis.

= Tacit knowledge "FAIRification and opening of low carbon energy research data" =
 Consortium members can add any time, they feel that something is important to note even though it is not mentioned in a standard deliverable. In other words, this page collects uncodified, tacit knowledge. It will help us later to compile suggestions and lessons learned. This kind of knowledge is collected two-fold:
#Anytime if someone feels that this should be noted. Please write down: Issue, Date, Author (could also be "anonymous"), the issue described in a few words or maximal lines>
#During the final day of workshops

=== Learning process: ===

*People are hesitant to adopt new IT technologies, this is even the case among researchers heavily relying on data, algorithms and collaborative online software (e.g., R, platforms, online conferencing, HPC, ...). The effort to encourage change is not to underestimate. Reasons are several, notably, lack of time and uncertainty about potential benefit as well as overall risk aversion preferences. EERAdata is using the online software "Only Office" to facilitate collaboration (in particular also during the Covid-19 period).
 '''FAIR and open criteria:'''
*Consortium members have a fair understanding of what FAIR/O is, but there is little knowledge and/or technical experience on how to approach the FAIRification and opening. All, however, share the view that we are at a critical point in time, where we need to implement these criteria. 
*To deepen knowledge about FAIR/O criteria, it is useful to test the criteria on a database one is familiar with. For this purpose (and to start brainstorming about the platform), AIT has developed a questionnaire for application in the use cases.
'''Metadata:'''
*A good starting point is to think about metadata and to look into existing metadata concepts in one's field. The first step is to understand that also metadata need to adhere to the FAIR/O principles.
*The next step is to increase knowledge on IT specific terms, i.e. to understand what the difference is between different '''[https://energydata.onlyoffice.eu/Products/Files/doceditor.aspx?fileid=5404340 metadata frameworks] (taxonomy, thesaurus, ontology) as well as classification of metadata (high-level, medium-level, low-level OR administrative, structural and descriptive metadata).  '''WP 2 being in charge of aligning approaches between use cases, participates in all use case kickoffs to bring everybody on the same page. The presentation is linked with "metadata frameworks".
*It is useful to supply consortium members with read aheads and watch aheads on metadata to prepare the '''[https://energydata.onlyoffice.eu/Products/Files/doceditor.aspx?fileid=5296590 first EERAdata workshop]'''. The workshop starts applications and discussions in the use cases break out sessions (and bringing insights back to the plenary), using selected databases.

= FAIR Assessment=

== FAIR Guiding Principles ==
{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ This table shows the FAIR guiding principles described by [https://www.doi.org/10.1038/sdata.2016.18 Wilkinson et al., 2016].

! To be Findable
|
* F1. (meta)data are assigned a globally unique and persistent identifier
* F2. data are described with rich metadata (defined by R1 below)
* F3. metadata clearly and explicitly include the identifier of the data it describes
* F4. (meta)data are registered or indexed in a searchable resource

|-
! To be Accessible
|
* A1. (meta)data are retrievable by their identifier using a standardized communications protocol
** A1.1 the protocol is open, free, and universally implementable
** A1.2 the protocol allows for an authentication and authorization procedure, where necessary
* A2. metadata are accessible, even when the data are no longer available

|-
! To be Interoperable
|
* I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
* I2. (meta)data use vocabularies that follow FAIR principles
* I3. (meta)data include qualified references to other (meta)data

|-
! To be Reusable
|
* R1. meta(data) are richly described with a plurality of accurate and relevant attributes
* R1.1. (meta)data are released with a clear and accessible data usage license
* R1.2. (meta)data are associated with detailed provenance
* R1.3. (meta)data meet domain-relevant community standards

|}

== Identified gaps after Workshop 1 (02/06/2020 – 04/06/2020) ==

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ This table shows a summary of the specific issues that were identified in Workshop 1. The aim was to categorise different issues to get a better understanding of how to tackle these challenges.
! FAIR/O !! Specific FAIR/O !! Gaps !! Gaps for energy domain !! Use case specific gaps !! Consequences for researchers
|-
! F
| F2
|| Metadata scope
|| Different fields require different metadata (potentially very specific)
|| UC3: lack of additional metadata for applications of materials
|| Data are less useful for the specific field
|-
! F
|| F1/F3
|| Identifiers
||
* Missing identifiers on websites
* Identifier not in downloaded data files
||
|| Makes research more difficult.
|-
! I
| I2
|| Taxonomy/ontology/common vocabulary and language issues
||
* Lack of standardisation
* Heterogeneous data makes standardisation hard
* No vocabulary documentation on websites
* Words used for same term in other languages may differ
* Databases in other languages than English
||
|| Research costs more time
|-
! I
| I3
|| References to other (meta)data
||
* No linking to source documents and related publications (for contextual knowledge)
* Possible need for links to other fields
|| UC3: link between microscopic and macroscopic materials (e.g., turbine blades)
||
* Data cannot be connected to the source
* Makes research more difficult.

|-
! R
| R1.1
|| Licensing
||
* No licenses available may mean the data is not reusable for the researcher
* Licenses not clear and accessible
* Obtaining licenses may result in more effort and costs
||
|| Data potentially not reusable

|-
! R
| R1.2
|| Reliability and provenance of data
|| When the data is collected from different and high number of sources, its reliability decreases
||
|| Data potentially not reusable

|-
! O
|
|| Privacy concerns and expected disadvantages
|| Not publishing data due to privacy concerns (sensitive data)
|| UC2: In case of distribution network data this is relevant
|| Data not accessible

|-
! Other
|
|| Conducting FAIR assessments
||
* No qualitative assessment of the data itself (which may be limited)
* Discrepancies between results conducted by humans versus machines
* Sometimes not even DC standards are met
* Metadata is not updated
* Lacking encryption of websites (https)
* Problems assessing whether metadata will be available after data is unavailable
* Interface design/layout may be unclear, incomplete or not intuitive for humans
* Authentication details (user registration login / good or bad, clarify)

||
* UC1 lack of data availability for time-series
||
* Assessment is uncertain (human assessment may need more clarification and understanding)
* Makes research more time-intensive

|}

== FAIR Self-Assessment tool ==

To conduct a human FAIR evaluation for different databases and their respective datasets, different free online tools were considered. Some seemed to be quite useful (e.g. [https://satifyd.dans.knaw.nl/ SATISFYD] ) and some were simple self-evaluation PDFs to be printed and filled out.

We decided to chose the [https://ardc.edu.au/resources/working-with-data/fair-data/fair-self-assessment-tool/ self-assessment tool] provided by the Australian Research Data Commons (ARDC). The decision was based manly upon the fact that the tools questions closely matched the FAIR categories specified by [https://www.doi.org/10.1038/sdata.2016.18 Wilkinson et al., 2016] which would make it easily comparable to the existing preferable FAIR conditions and aid with the repeatability of the results, and the half-automated scoring features that show the extent of the "FAIRness" of the analysed sample as a simple bar chart.

The result of the FAIR assessment is provided as a bar chart where an empty bar chart displays 0 % FAIR compatibility, and a full bar chart represents 100 % FAIR compatibility. The score is therefore not returned as a specific value. During the assessment below, the "Total Across FAIR" in Table ... was estimated according to the level of the bar chart from 0 % to 100 %.
Resulting scores where categorised as follows: Low: < 50%; Medium: 50-60%; High: > 60% .

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ All options for each question of the ARDCs self-assessment tool
!colspan="4"|Findable
!colspan="3"|Accessible
!colspan="3"|Interoperable
!colspan="3"|Reusable
|-
| F1. (meta)data are assigned a globally unique and persistent identifier
|| F2. data are described with rich metadata (defined by R1 below)
|| F3. metadata clearly and explicitly include the identifier of the data it describes
|| F4. (meta)data are registered or indexed in a searchable resource
|| A1. (meta)data are retrievable by their identifier using a standardized communications protocol
|| A1.1 the protocol is open, free, and universally implementable and A1.2 the protocol allows for an authentication and authorization procedure, where necessary
|| A2. metadata are accessible, even when the data are no longer available
|| I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
|| I2. (meta)data use vocabularies that follow FAIR principles
|| I3. (meta)data include qualified references to other (meta)data
|| R1.1. (meta)data are released with a clear and accessible data usage license
|| R1.2. (meta)data are associated with detailed provenance
|| R1.3. (meta)data meet domain-relevant community standards
|-
! Does the dataset have any identifiers assigned?
! Is the dataset identifier included in all metadata records/files describing the data?
! How is the data described with metadata?
! What type of repository or registry is the metadata record in?
! How accessible is the data?
! Is the data available online without requiring specialised protocols or tools once access has been approved?
! Will the metadata record be available even if the data is no longer available?
! What (file) format(s) is the data available in?
! What best describes the types of vocabularies/ontologies/tagging schemas used to define the data elements?
! How is the metadata linked to other data and metadata (to enhance context and clearly indicate relationships)?
! Which of the following best describes the license/usage rights attached to the data?
! How much provenance information has been captured to facilitate data reuse?
!
|-
| Globally unique, citable and persistant (e.g. DOI, PURL, ARK or Handle)
|| Yes
|| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
|| Data is in one place but discoverable through several registries
|| Publicly accessible
|| Standard web service API (e.g. OGC)
|| Yes
|| In a structured, open standard, machine-readable format
|| Standardised open and universal using resolvable global identifiers linking to explainations
|| Meadata is represented in a machine readable format, e.g. in a linked data format such as Resource Description Framework (RDF)
|| Standard machine-readable license (e.g. Creative Commons)
|| Fully recorded in a machine-readable format
||
|-
| Web adress (URL)
|| No
|| Comprehensively, but in a text-based, non-standard format
|| Generalist public repository
|| Fully accessible to person who meet explicitly stated conditions, e.g. ethics approval for sensitive data
|| Non-standard web service (e.g. OpenAPI/Swagger/informal API)
|| No
|| In a structured, open standard, non-machine-readable format
|| Standardised vocabularies/ontologies/schema without global identifiers
|| The metadata record includes URI links to related metadata, data and definitions
|| Standard text based license
|| Fully recorded in a text format
||
|-
| Local identifier
||
|| Brief title and description
|| Domain-specific repository
|| A de-identified / modified subset of the data is publicly accessible
|| File download from online location
|| Unsure
|| Mostly in a proprietary format
|| No standards have been applied in the description of data elements
|| There are no links to other metadata
|| Non-standard machine-readably license (clearly indicating under what conditions the data may be reused)
|| Partially recorded
||
|-
| No identifier
||
|| The data is not described
|| Local institutional repository
|| Embaged access after a specific date
|| By individual arrangement
||
||
|| Data elements not described
||
|| Non-standard text-based license
|| No provenance information is recorded
||
|-
|
||
||
|| The data is not described in any repository
|| Unspecified conditional access e.g. contact the data custodian for access
|| No access to data
||
||
||
||
|| No license
||
||
|-
|
||
||
||
|| Access to metadata only
||
||
||
||
||
||
||
||
|-
|
||
||
||
|| No access to data or metadata
||
||
||
||
||
||
||
||
|}

At this point it is important to note that the assessment of databases and/or datasets using this tool is solely considered a human assessment. This may lead to different evaluations by different users. To counteract this phenomenon, it was suggested that the evaluations should be completed using the 'four-eyes principle' to create more verifiable results. Also, it is planned to additionally evaluate the respective databases using an external tool to perform a machine-assessment.

In addition, not all questions are weighted equally and thus a worse result for one question may not have a large impact whereas only choosing the second-best option in another question will bring down the overall FAIR value considerably.

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ The ARDC tool provides detailed information about the requirements for each of the options of its tool. These descriptions are documented here.
|-
! rowspan="5" | F
|| F
|| About Findable
|| Making data Findable includes assigning a [https://www.ands.org.au/guides/persistent-identifiers-awareness persistent identifier] (like a [https://www.ands.org.au/guides/doi DOI] or [https://www.ands.org.au/guides/identify-my-data Handle] ), having rich [https://www.ands.org.au/working-with-data/metadata metadata] to describe the data and making sure it is findable through disciplinary and generalist discovery portals (local and international).

|-
| F1
|| About identifiers
|| Identifiers are essential for identifying, finding, retrieving, linking and citing datasets. A Web address (URL) can be used to specify the online location of a resource but over time URLs tend to change which leads to broken links to the data. To be useful, identifiers need to be persistent and unique. Digital Object Identifiers (DOIs) and other persistent identifiers (PIDs) provide a permanent citable reference to a particular dataset. The DOI is a permanent fixed reference to the dataset no matter where it is located online and enables citation and citation metrics. Services to create a persistent identifier are often offered by your affiliated institution or the repository you are using to describe your data. Talk to your library service for more information.

Read more: [https://www.ands.org.au/working-with-data/citation-and-identifiers Persistent identifiers]

|-
| F2
|| About identifiers
|| The identifier (preferably a persistent identifier) needs to be clearly stated in the metadata record describing the data collection, and also in any associated data files or metadata.

|-
|F3
|| About metadata descriptions
|| Comprehensive metadata records will include descriptive content that facilitates discovery, access and reuse of the data being described. While there is no 'one size fits all' list, comprehensive metadata should include:
* a globally unique persistent identifier e.g. a DOI
* a title
* related people, i.e. the data creator or custodian
* how to access the data and file formats
* a description of how the data were created and how to interpret the data subject or keywords
* citation information that clearly indicates how the data should be cited
* a machine-readable data licence
* provenance and contextual information such as:
* links to related publications, projects, services and software
* methodology and processes involved in data production
* spatial and temporal coverage (if relevant)
* object-level data description

Providing metadata in a standard schema allows it to be read and used by machines as well as humans.

Read more: [https://www.ands.org.au/working-with-data/metadata Metadata]

|-
| F4
|| About metadata repositories
|| A rich metadata description alone does not ensure a dataset’s ‘findability’ on the internet; the dataset needs to be registered or indexed in a searchable resource, such as a generalist (e.g. [https://figshare.com/ Figshare], [http://datadryad.org/ Dryad], [https://zenodo.org/ Zenodo], domain-specific (e.g. [https://pangaea.de/ PANGAEA], [https://ada.edu.au/ ADA], [https://www.icpsr.umich.edu/ ICPSR]), or institutional (e.g. [https://openresearch-repository.anu.edu.au/ ANU Research Repository] , [https://ses.library.usyd.edu.au/ Sydney eScholarship Repository], [https://data.gov.au/ data.gov.au]) data repository or registry. Ideally these repositories/registries are indexed by search engines such as Google and/or Google Scholar.

|-
! rowspan = "4" | A
| A
|| About Accessible
|| [https://www.ands.org.au/working-with-data/publishing-and-reusing-data/publishing To make data accessible] may include making the data [https://www.ands.org.au/working-with-data/articulating-the-value-of-open-data/open-data open] using a standardised protocol. However the data does not necessarily have to be open. There are sometimes good reasons why data [https://www.ands.org.au/working-with-data/sensitive-data cannot be made open], for example privacy concerns, national security or commercial interests. If it is not open there should be clarity and transparency around the conditions governing access and [https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse reuse].
|-
| A1
|| About data accessibility
|| Not all data that is discoverable can be freely accessed. Often there are embargoes, access controls, and access permissions associated with data due to a variety of issues such as privacy and commercial interests. Even with all these issues much [https://www.ands.org.au/working-with-data/sensitive-data sensitive data] can be shared. Many other issues that could be perceived as blockers to sharing data may be [http://datapub.cdlib.org/closed-data-excuses-excuses overcome].
|-
| A1.1/A1.2
|| About protocols
|| Ideally users would like to retrieve appropriate internet content directly and unhindered once they have located it. Internet protocols (e.g. http and ftp) define rules and conventions for communication between devices, and tools and services are available to facilitate this process, e.g. APIs.

Protocols:
* [https://www.lifewire.com/hypertext-transfer-protocol-817944 HTTP] (Hypertext Transfer Protocol) is the set of rules for transferring files (text, graphic images, sound, video, and other multimedia files) on the [https://whatis.techtarget.com/definition/World-Wide-Web World Wide Web]. As soon as a Web user opens their Web browser, the user is indirectly making use of HTTP.
* [https://www.lifewire.com/file-transfer-protocol-817943 FTP] (File Transfer Protocol) is a standard Internet [http://searchnetworking.techtarget.com/definition/protocol protocol] for transmitting files between computers on the Internet over TCP/IP connections. Read more: [https://www.lifewire.com/definition-of-protocol-network-817949 Web protocols]
* API (Application Programming Interface): When information is made available in any machine readable format, it becomes possible to make that information directly available to programs that request that information over the web. An API is the way this information is made directly available to other machines.
View more: [https://www.youtube.com/watch?v=s7wmiS2mSXY What is an API?]

OGC - http://www.opengeospatial.org/ogc

|-
| A2
|| About metadata availability
|| Effort is often required to maintain data resources online which can often lead to it being neglected especially over long periods of time. This often leads to broken links between the metadata and the data. Having at least a description of the data in the form of a metadata record means there is a record of the data's existence allowing the possibility of someone tracking it down.

|-
! rowspan="4" | I
|| I
|| About Interoperability
|| To be interoperable (i.e. data that is interpretable by a computer, so that they can be automatically combined with other data) the data will need to use community agreed [https://www.ands.org.au/guides/file-formats formats], language and [https://www.ands.org.au/guides/vocabularies-and-research-data vocabularies]. The [https://www.ands.org.au/working-with-data/metadata metadata] will also need to use a community agreed standards and vocabularies, and contain links to related information using [https://www.ands.org.au/working-with-data/citation-and-identifiers identifiers].
|-
|| I1
|| About file formats
|| When selecting file formats for archiving, the formats should ideally be:
* Non-proprietary
* Unencrypted
* Uncompressed
* In common usage by the research community
* Adherent to an open, documented standard, such as described by the State of California (see [http://www.leginfo.ca.gov/pub/07-08/bill/asm/ab_1651-1700/ab_1668_bill_20070524_amended_asm_v96.pdf AB 1668, 2007])
** Interoperable among diverse platforms and applications
** Fully published and available royalty-free
** Fully and independently implementable by multiple software providers on multiple platforms without any intellectual property restrictions for necessary technology
** Developed and maintained by an open standards organization with a well-defined inclusive process for evolution of the standard.

From: [https://library.stanford.edu/research/data-management-services/data-best-practices/best-practices-file-formats Stanford University Library - Best practices for file formats]

File format examples:
* Structured, open standard, machine-readable format e.g. (text) PDF/A, HTML, Plain text, (images) TIFF, JPEG 2000, GIF, (audio) MP3, AIFF, WAVE, (video) MOV, MPEG, AVI, (Tabular data) CSV
* structured, open standard, non-machine-readable format, e.g. PDF, HTML, JPG
* Proprietary format, e.g. doc (Word), .xls (Excel), .ppt (PowerPoint), .sav

Read more:
* [https://www.ands.org.au/__data/assets/pdf_file/0003/731775/File-Formats.pdf ANDS]
* [https://www.europeandataportal.eu/elearning/en/module9/#/id/co-01 European Data Portal]

|-
|| I2
|| About schema standards
|| Schema standards are schemas that have gone through a formal validation process by a standards organisation, such as the International Standards Organisation (ISO) or an equivalent body such as the Dublin Core Metadata Initiative (DCMI); or, commonly used and consistently applied metadata schemas that are well documented, endorsed, and maintained can also become ‘de-facto standards’, e.g. RIF-CS for describing data collections and services used in Research Data Australia.

Read more: [https://www.ands.org.au/guides/metadata-working Metadata schema]

Standardised open and universal schemas, e.g, [https://www.dcc.ac.uk/resources/metadata-standards/datacite-metadata-schema DataCite Metadata Schema], [https://www.dcc.ac.uk/resources/metadata-standards/prov PROV], [https://www.dcc.ac.uk/resources/metadata-standards/dublin-core Dublin Core]

Domain-specific standards, e.g. [http://human-phenotype-ontology.github.io/ HPO - Human Phenotype Ontology], [https://www.nlm.nih.gov/mesh/ MeSH - Medical Subject Headings] , [https://mcp-profile-docs.readthedocs.io/en/stable/ Marine Community Profile], [https://ddialliance.org/ DDI - Data Documentation Initiative].

For more examples of standards: [http://rd-alliance.github.io/metadata-directory/ Here].

Resolvable global identifiers - An identifier is any label used to name something uniquely (whether online or offline). URLs are an example of an identifier. So are serial numbers, and personal names. A persistent identifier is guaranteed to be managed and kept up to date over a defined time period.

Read more: [https://www.ands.org.au/guides/persistent-identifiers-awareness Persistent Identifiers]

|-
||I3
|| About metadata links
|| The goal is to create as many meaningful linkages as possible between (meta)data resources to enrich the contextual knowledge about the data, balanced against the time/energy involved in making a good data model.

'''Machine-readable (meta)data'''

(Meta)data in a format that can be automatically read and processed by a computer, such as [http://opendatahandbook.org/glossary/en/terms/csv/ CSV], [http://opendatahandbook.org/glossary/en/terms/json/ JSON], [http://opendatahandbook.org/glossary/en/terms/xml/ XML] etc. For more information: [http://opendatahandbook.org/glossary/en/terms/machine-readable/ Open Data Handbook]

'''Linked Data'''

Linked Data (also known as Linking Data) can be applied to improve the exploitation of the “Web of data.” The expression refers to the publishing of structured data in a way that typed links are created between data from different sources to provide a higher level of usability. By using Linked Data, it is possible to find other, related data. Structured data should meet four requirements to be called
Linked Data:

* URIs should be assigned to all entities of the dataset.
* HTTP URIs are required to ensure that all entities can be referenced and cited by users and user agents.
* Entities should be described using standard formats such as RDF/XML.
* Links should be created to other, related entity URIs.

All data that fulfil these requirements and are released for the public are called Linked Open Data (LOD). http://www.lesliesikos.com/semantic-web-machine-readable-structured-data-with-meaningful-annotations/

Read more under the heading (meta)data include qualified references to other (meta)data in the following resource: https://www.dtls.nl/fair-data/fair-principles-explained/

For more information on Linked Data standards - RDF - https://www.w3.org/RDF/

Other related linked data standards:
* OWL - https://www.w3.org/OWL/
* SKOS - https://www.w3.org/2004/02/skos/
* SPARQL - https://www.w3.org/TR/rdf-sparql-query/

|-
! rowspan="3" | R
|| R
|| About Reusable
|| Reusable data should maintain its initial richness. For example, it should not be abridged for the purpose of explaining the findings in one particular publication. It needs a clear machine-readable [https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse licence] and [https://www.ands.org.au/partners-and-communities/ands-communities/data-provenance-interest-group provenance] information on how the data was formed. It should also use discipline-specific data and [https://www.ands.org.au/working-with-data/metadata metadata] standards to give it rich contextual information that will allow for accurate interpretation and reuse.

|-
|| R1.1
|| About licenses
|| If data is not licensed no-one else can use it. In Australia, no licence is regarded as the same as 'all rights reserved', confining any reuse to very limited circumstances. Applying a Creative Commons licence to your data is a simple way to ensure that your data can be reused. The less restrictive the licence, the more that can be done with the data.
To make it easy for the Web to know when a work is available under a particular license, a “machine readable” version of the license provides a summary of the key freedoms and obligations written into a format that software systems, search engines, and other kinds of technology can understand.
Example from the [https://creativecommons.org/choose/ Creative Commons License Choser]:

What appears in the metadata:

This work is licensed under a [https://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International License].

Code snippet:

<code>
<a rel="license" href="http://creativecommons.org/licenses/by/4.0/">

<img alt="Creative Commons License" style="border-width:0"

src="https://i.creativecommons.org/l/by/4.0/88x31.png"/>

</a> This work is licensed under a

<a rel="license" href="http://creativecommons.org/licenses/by/4.0/">

Creative Commons Attribution 4.0 International License</a>.
<\code>

Read more: https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse

|-
||R1.2
|| About provenance
|| Data provenance is used to document where a piece of data comes from and the process and methodology by which it is produced. It is important to confirm the authenticity of data enabling trust, credibility and reproducibility. This is becoming increasingly important, especially in the eScience community where research is data intensive and often involves complex data transformations and procedures ([https://www.ands.org.au/partners-and-communities/ands-communities/data-provenance-interest-group Read more]). Provenance vocabularies such as [https://www.w3.org/2005/Incubator/prov/XGR-prov-20101214/#Recommendations Provenir Ontology (PROV-O) and others].

|}

== Results for SMILES and ShareWind ==

{| class="mw-collapsible wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Datasets from the SMILES and ShareWind platforms was analysed using the self-assessment tool provided by ARDC.
! rowspan="5" |
| rowspan="5" |
! Database Code
| rowspan="24" style="background: #ABE" |
| ED4
| ED9
|-
! Database Title
|| SMILES
| ShareWind
|-
! Database Link
|| https://www.ecria-smiles.eu/
| https://sharewind.eu/
|-
! Sample Link
|| [https://www.ecria-smiles.eu/data-files/-/document_library/W32cCAfL2jMX/view_file664466?_com_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX_redirect=https%3A%2F%2Fwww.ecria-smiles.eu%3A443%2Fdata-files%2F-%2Fdocument_library%2FW32cCAfL2jMX%2Fview%2F664423%3F_com_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX_redirect%3Dhttps%253A%252F%252Fwww.ecria-smiles.eu%253A443%252Fdata-files%253Fp_p_id%253Dcom_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX%2526p_p_lifecycle%253D0%2526p_p_state%253Dnormal%2526p_p_mode%253Dview here] (link has 585 characters)
| https://sharewind.eu/records/f9d31abbc2824284b8c1d28894d9619a (data found at https://zenodo.org/record/1162738)
|-
! Sample Size
|| 4 KB
| 37 MB
|-
| colspan="6" style="background: #ABE" |
|-
! rowspan="4" | Findable
| F1. (meta)data are assigned a globally unique and persistent identifier
! Does the dataset have any identifiers assigned?
|| Web adress (URL)
| Globally unique, citable and persistant (e.g. DOI, PURL, ARK or Handle)
|-

| F2. data are described with rich metadata (defined by R1 below)
! Is the dataset identifier included in all metadata records/files describing the data?
|| No
| No
|-

| F3. metadata clearly and explicitly include the identifier of the data it describes
! How is the data described with metadata?
|| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
|-

| F4. (meta)data are registered or indexed in a searchable resource
! What type of repository or registry is the metadata record in?
|| Domain-specific repository
| Data is in one place but discoverable through several registries
|-

! rowspan="3" | Accessible

| A1. (meta)data are retrievable by their identifier using a standardized communications protocol
! How accessible is the data?
|| Publicly accessible
| Publicly accessible
|-
| A1.1 the protocol is open, free, and universally implementable; A1.2 the protocol allows for an authentication and authorization procedure, where necessary
! Is the data available online without requiring specialised protocols or tools once access has been approved?
|| File download from online location
| Standard web service API (e.g. OGC)
|-
| A2. metadata are accessible, even when the data are no longer available
! Will the metadata record be available even if the data is no longer available?
|| Unsure
| Yes
|-

! rowspan="3" | Interoperable
| I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
! What (file) format(s) is the data available in?
|| In a structured, open standard, machine-readable format
|| In a structured, open standard, non-machine-readable format
|-
| I2. (meta)data use vocabularies that follow FAIR principles
! What best describes the types of vocabularies/ontologies/tagging schemas used to define the data elements?
|| Standardised vocabularies/ontologies/schema without global identifiers
|| Standardised vocabularies/ontologies/schema without global identifiers
|-
| I3. (meta)data include qualified references to other (meta)data
! How is the metadata linked to other data and metadata (to enhance context and clearly indicate relationships)?
|| There are no links to other metadata
|| Metadata is represented in a machine readable format, e.g. in a linked data format such as Resource Description Framework (RDF)
|-

! rowspan="3" | Reusable
| R1.1. (meta)data are released with a clear and accessible data usage license
! Which of the following best describes the license/usage rights attached to the data?
|| No license
| Standard machine-readable license (e.g. Creative Commons)
|-
| R1.2. (meta)data are associated with detailed provenance
! How much provenance information has been captured to facilitate data reuse?
|| Partially recorded
|| Partially recorded
|-
| R1.3. (meta)data meet domain-relevant community standards
|
|
|
|-
| colspan="6" style="background: #ABE" |
|-
! Total across F.A.I.R
|
! Low: < 50%; Medium: 50-60%; High: > 60%
|| ~ 45 %
|| ~ 85 %
|-
| colspan="6" style="background: #ABE" |
|-
! Remarks about database
||
||
||
|| The data is provided by the open-source platform Zenodo, not the project website itself. A very positive aspect is that information on different versions of the data as well as the number of views and downloads is available.
|-
! Identified gaps
||
||
||
* No DOI, only URL
* Extremely long URL link spanning several lines with 585 characters, that can easily become corrupted
* No links to other metadata
* No license information given
* Provenance not fully recorded

||
* Dataset identifiers should be included in the files describing the data
* Provenance details are only partially recorded with a note with contact name for further information
|}

= Metadata assessment =

== Dublin Core Elements ==
{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ The DCMI Metadata Terms lists the current set of the Dublin Core vocabulary. Source: [https://dublincore.org/specifications/dublin-core/dcmi-terms/ Dublin Core Metadata Initiative]
| 1 || abstract || 11 || contributor || 21 || extent || 31 || isReplacedBy || 41 || publisher || 51 || tableOfContents
|-
| 2 || accessRights || 12 || coverage || 22 || format || 32 || isRequiredBy || 42 || references || 52 || temporal
|-
| 3 || accrualMethod || 13 || created || 23 || hasFormat || 33 || issued || 43 || relation || 53 || title
|-
| 4 || accrualPeriodicity || 14 || creator || 24 || hasPart || 34 || isVersionOf || 44 || replaces || 54 || type
|-
| 5 || accrualPolicy || 15 || date || 25 || hasVersion || 35 || language || 45 || requires || 55 || valid
|-
| 6 || alternative || 16 || dateAccepted || 26 || identifier || 36 || license || 46 || rights || ||
|-
| 7 || audience || 17 || dateCopyrighted || 27 || instructionalMethod || 37 || mediator || 47 || rightsHolder || ||
|-
| 8 || available || 18 || dateSubmitted || 28 || isFormatOf || 38 || medium || 48 || source || ||
|-
| 9 || bibliographicCitation || 19 || description || 29 || isPartOf || 39 || modified || 49 || spatial || ||
|-
| 10 || conformsTo || 20 || educationLevel || 30 || isReferencedBy || 40 || provenance || 50 || subject || ||
|}

'''Metadata Category Types according to [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO]'''

There are three main types of metadata:
* Descriptive metadata describes a resource for purposes such as discovery and identification. It can include elements such as title, abstract, author, and keywords.
* Structural metadata indicates how compound objects are put together, for example, how pages are ordered to form chapters.
* Administrative metadata provides information to help manage a resource, such as when and how it was created, file type and other technical information, and who can access it. There are several subsets of administrative data; two that sometimes are listed as separate metadata types are:
** Rights management meta-data, which deals with intellectual property rights,and
** Preservation metadata, which contains information needed to archive and preserve a resource.
''

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Simple Dublin Core Elements. 15 DC elements with their (shortened) official definitions and examples.
! DC element name !! DC definition ([https://www.dublincore.org/specifications/dublin-core/dces/ DCMI]) !! Example (Energy Domain) !! Category (determined with the aid of [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO])
|-
! Title
| A name given to the resource || Outdoor monitoring of a hybrid micro-CPV solar panel with integrated micro-tracking and diffuse capture || Descriptive
|-
! Subject
| The topic of the resource || concentrator photovoltaics; CPV; rooftop photovoltaics; integrated tracking || Descriptive
|-
! Description
| An account of the resource || Dataset from the outdoor characterization of a B Series module from Insolight at the rooftop of the Instituto de Energía Solar - Universidad Politécnica de Madrid. …. || Descriptive
|-
! Creator
| An entity primarily responsible for making the resource || Stephen Askins; Gaël Nardin || Descriptive
|-
! Publisher
| An entity responsible for making the resource available || Stephen Askins || Descriptive
|-
! Contributor
| An entity responsible for making contributions to the resource || Stephen Askins || Descriptive
|-
! Date
| A point or period of time associated with an event in the lifecycle of the resource || 2019-07-23; Date will be associated with the creation or availability of the resource. || Administrative
|-
! Type
| The nature or genre of the resource || info:eu-repo/semantics/other; dataset || Descriptive
|-
! Format
| The file format, physical medium, or dimensions of the resource || image
|| Structural
|-
! Identifier
| An unambiguous reference to the resource within a given context || https://zenodo.org/record/3346823; 10.5281/zenodo.3346823; oai:zenodo.org:3346823 || Descriptive
|-
! Source
| A related resource from which the described resource is derived || RC607.A26W574 1996 [where "RC607.A26W574 1996" is the call number of the print version of the resource, from which the present version was scanned] || Descriptive
|-
! Language
| A language of the resource || eng || Descriptive
|-
! Relation
| A related resource || info:eu-repo/grantAgreement/EC/H2020/787289/ || Descriptive
|-
! Coverage
| The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant || 1995-1996; Madrid, Spain || Descriptive
|-
! Rights
| Information about rights held in and over the resource || info:eu-repo/semantics/openAccess; https://creativecommons.org/licenses/by/4.0/legalcode || Administrative
|}

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Non-DC elements

! Non-DC element name !! Definition !! Example !! Category
|-
! Date of availability
| Since when is the data available
||
|| Impact/Exploitation

|-
! Number of users
| Is there any information on the number of accesses or users?
||
|| Impact/Exploitation

|-
! Number of scientific publications where data is cited/used
| Is there any information on the number of citations/uses?
||
|| Impact/Exploitation

|-
! Curation activities (is the versioning ongoing)
| Update, maintainance
||
|| Impact/Exploitation
|}

Attempting to put a value on Impact/Exploitation:
* '''ENTSO-E''': Found 172 results in Advanced Search (ALL) in Web of Science ([https://apps.webofknowledge.com/ WoSc]) (16/06/2020). Citations of all these search result add up to 714. This however does not mean, that all search results really explicitly used data from ENTSO-E. Some (for sure, for some were found) only mentioned it saying they adoped workflow etc. from the site.
* '''SMARD''': Found 82 results in Advanced Search (ALL=(SMARD)) in Web of Science ([https://apps.webofknowledge.com/ WoSc]) (16/06/2020). However: SMARD is also an acronym for ''Spinal muscular atrophy with respiratory distress (SMARD)''. This somewhat complicates results and needs some further discussion. All 82 search results accumumlated 2359 citations themselves.

== Results for SMILES ==

{| class="wikitable mw-collapsible"
! DC element name
! DC definition ([https://www.dublincore.org/specifications/dublin-core/dces/ DCMI])
! Category (determined with the aid of [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO])
! rowspan = "16" style="background: #ABE" |
! SMILES (ED4)
! Notes (SMILES (ED4) )

|-
! Title
| A name given to the resource
|| Descriptive
|| A
||
|-
! Subject
| The topic of the resource || Descriptive || A ||
|-
! Description
| An account of the resource || Descriptive || PA || The description of the data is very limited. For all files a general description is given. This is linked to the project deliverables but it is not indicated which deliverable are exactly corresponding. This seems to be a time-consuming process for researchers.
|-
! Creator
| An entity primarily responsible for making the resource || Descriptive || A || The metadata file uses the tag "author"
|-
! Publisher
| An entity responsible for making the resource available || Descriptive || A || This information only available under the URL but not in the metadata file
|-
! Contributor
| An entity responsible for making contributions to the resource || Descriptive || NA ||
|-
! Date
| A point or period of time associated with an event in the lifecycle of the resource || Administrative || A || This information only available under the URL but not in the metadata file
|-
! Type
| The nature or genre of the resource || Descriptive || NA || No explicit mention of the type (e.g. document, dataset) although it could be derived from the context
|-
! Format
| The file format, physical medium, or dimensions of the resource || Structural || PA || The metadata element "format" is not exactly indicated but the metadata tag "file" includes the format type at the end (".csv"). Additionally, the tag "datatype" is used to specify the internal data type (e.g. integer, string) of the data file.
|-
! Identifier
| An unambiguous reference to the resource within a given context || Descriptive || NA ||
|-
! Source
| A related resource from which the described resource is derived || Descriptive || NA ||
|-
! Language
| A language of the resource || Descriptive || NA || No explicit mention of the language although it could be derived from the context
|-
! Relation
| A related resource || Descriptive || NA || This information (EU grant information) only generally available under the URL but not in the metadata file
|-
! Coverage
| The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant || Descriptive || NA ||
|-
! Rights
| Information about rights held in and over the resource || Administrative || NA ||
|}

Gap analysis

2020-08-31T09:34:38Z

Christopherbs: /* Results for SMILES */

<accesscontrol>Administrators,consortium</accesscontrol>

This is the page for the gap analysis.

= Tacit knowledge "FAIRification and opening of low carbon energy research data" =
 Consortium members can add any time, they feel that something is important to note even though it is not mentioned in a standard deliverable. In other words, this page collects uncodified, tacit knowledge. It will help us later to compile suggestions and lessons learned. This kind of knowledge is collected two-fold:
#Anytime if someone feels that this should be noted. Please write down: Issue, Date, Author (could also be "anonymous"), the issue described in a few words or maximal lines>
#During the final day of workshops

=== Learning process: ===

*People are hesitant to adopt new IT technologies, this is even the case among researchers heavily relying on data, algorithms and collaborative online software (e.g., R, platforms, online conferencing, HPC, ...). The effort to encourage change is not to underestimate. Reasons are several, notably, lack of time and uncertainty about potential benefit as well as overall risk aversion preferences. EERAdata is using the online software "Only Office" to facilitate collaboration (in particular also during the Covid-19 period).
 '''FAIR and open criteria:'''
*Consortium members have a fair understanding of what FAIR/O is, but there is little knowledge and/or technical experience on how to approach the FAIRification and opening. All, however, share the view that we are at a critical point in time, where we need to implement these criteria. 
*To deepen knowledge about FAIR/O criteria, it is useful to test the criteria on a database one is familiar with. For this purpose (and to start brainstorming about the platform), AIT has developed a questionnaire for application in the use cases.
'''Metadata:'''
*A good starting point is to think about metadata and to look into existing metadata concepts in one's field. The first step is to understand that also metadata need to adhere to the FAIR/O principles.
*The next step is to increase knowledge on IT specific terms, i.e. to understand what the difference is between different '''[https://energydata.onlyoffice.eu/Products/Files/doceditor.aspx?fileid=5404340 metadata frameworks] (taxonomy, thesaurus, ontology) as well as classification of metadata (high-level, medium-level, low-level OR administrative, structural and descriptive metadata).  '''WP 2 being in charge of aligning approaches between use cases, participates in all use case kickoffs to bring everybody on the same page. The presentation is linked with "metadata frameworks".
*It is useful to supply consortium members with read aheads and watch aheads on metadata to prepare the '''[https://energydata.onlyoffice.eu/Products/Files/doceditor.aspx?fileid=5296590 first EERAdata workshop]'''. The workshop starts applications and discussions in the use cases break out sessions (and bringing insights back to the plenary), using selected databases.

= FAIR Assessment=

== FAIR Guiding Principles ==
{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ This table shows the FAIR guiding principles described by [https://www.doi.org/10.1038/sdata.2016.18 Wilkinson et al., 2016].

! To be Findable
|
* F1. (meta)data are assigned a globally unique and persistent identifier
* F2. data are described with rich metadata (defined by R1 below)
* F3. metadata clearly and explicitly include the identifier of the data it describes
* F4. (meta)data are registered or indexed in a searchable resource

|-
! To be Accessible
|
* A1. (meta)data are retrievable by their identifier using a standardized communications protocol
** A1.1 the protocol is open, free, and universally implementable
** A1.2 the protocol allows for an authentication and authorization procedure, where necessary
* A2. metadata are accessible, even when the data are no longer available

|-
! To be Interoperable
|
* I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
* I2. (meta)data use vocabularies that follow FAIR principles
* I3. (meta)data include qualified references to other (meta)data

|-
! To be Reusable
|
* R1. meta(data) are richly described with a plurality of accurate and relevant attributes
* R1.1. (meta)data are released with a clear and accessible data usage license
* R1.2. (meta)data are associated with detailed provenance
* R1.3. (meta)data meet domain-relevant community standards

|}

== Identified gaps after Workshop 1 (02/06/2020 – 04/06/2020) ==

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ This table shows a summary of the specific issues that were identified in Workshop 1. The aim was to categorise different issues to get a better understanding of how to tackle these challenges.
! FAIR/O !! Specific FAIR/O !! Gaps !! Gaps for energy domain !! Use case specific gaps !! Consequences for researchers
|-
! F
| F2
|| Metadata scope
|| Different fields require different metadata (potentially very specific)
|| UC3: lack of additional metadata for applications of materials
|| Data are less useful for the specific field
|-
! F
|| F1/F3
|| Identifiers
||
* Missing identifiers on websites
* Identifier not in downloaded data files
||
|| Makes research more difficult.
|-
! I
| I2
|| Taxonomy/ontology/common vocabulary and language issues
||
* Lack of standardisation
* Heterogeneous data makes standardisation hard
* No vocabulary documentation on websites
* Words used for same term in other languages may differ
* Databases in other languages than English
||
|| Research costs more time
|-
! I
| I3
|| References to other (meta)data
||
* No linking to source documents and related publications (for contextual knowledge)
* Possible need for links to other fields
|| UC3: link between microscopic and macroscopic materials (e.g., turbine blades)
||
* Data cannot be connected to the source
* Makes research more difficult.

|-
! R
| R1.1
|| Licensing
||
* No licenses available may mean the data is not reusable for the researcher
* Licenses not clear and accessible
* Obtaining licenses may result in more effort and costs
||
|| Data potentially not reusable

|-
! R
| R1.2
|| Reliability and provenance of data
|| When the data is collected from different and high number of sources, its reliability decreases
||
|| Data potentially not reusable

|-
! O
|
|| Privacy concerns and expected disadvantages
|| Not publishing data due to privacy concerns (sensitive data)
|| UC2: In case of distribution network data this is relevant
|| Data not accessible

|-
! Other
|
|| Conducting FAIR assessments
||
* No qualitative assessment of the data itself (which may be limited)
* Discrepancies between results conducted by humans versus machines
* Sometimes not even DC standards are met
* Metadata is not updated
* Lacking encryption of websites (https)
* Problems assessing whether metadata will be available after data is unavailable
* Interface design/layout may be unclear, incomplete or not intuitive for humans
* Authentication details (user registration login / good or bad, clarify)

||
* UC1 lack of data availability for time-series
||
* Assessment is uncertain (human assessment may need more clarification and understanding)
* Makes research more time-intensive

|}

== FAIR Self-Assessment tool ==

To conduct a human FAIR evaluation for different databases and their respective datasets, different free online tools were considered. Some seemed to be quite useful (e.g. [https://satifyd.dans.knaw.nl/ SATISFYD] ) and some were simple self-evaluation PDFs to be printed and filled out.

We decided to chose the [https://ardc.edu.au/resources/working-with-data/fair-data/fair-self-assessment-tool/ self-assessment tool] provided by the Australian Research Data Commons (ARDC). The decision was based manly upon the fact that the tools questions closely matched the FAIR categories specified by [https://www.doi.org/10.1038/sdata.2016.18 Wilkinson et al., 2016] which would make it easily comparable to the existing preferable FAIR conditions and aid with the repeatability of the results, and the half-automated scoring features that show the extent of the "FAIRness" of the analysed sample as a simple bar chart.

The result of the FAIR assessment is provided as a bar chart where an empty bar chart displays 0 % FAIR compatibility, and a full bar chart represents 100 % FAIR compatibility. The score is therefore not returned as a specific value. During the assessment below, the "Total Across FAIR" in Table ... was estimated according to the level of the bar chart from 0 % to 100 %.
Resulting scores where categorised as follows: Low: < 50%; Medium: 50-60%; High: > 60% .

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ All options for each question of the ARDCs self-assessment tool
!colspan="4"|Findable
!colspan="3"|Accessible
!colspan="3"|Interoperable
!colspan="3"|Reusable
|-
| F1. (meta)data are assigned a globally unique and persistent identifier
|| F2. data are described with rich metadata (defined by R1 below)
|| F3. metadata clearly and explicitly include the identifier of the data it describes
|| F4. (meta)data are registered or indexed in a searchable resource
|| A1. (meta)data are retrievable by their identifier using a standardized communications protocol
|| A1.1 the protocol is open, free, and universally implementable and A1.2 the protocol allows for an authentication and authorization procedure, where necessary
|| A2. metadata are accessible, even when the data are no longer available
|| I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
|| I2. (meta)data use vocabularies that follow FAIR principles
|| I3. (meta)data include qualified references to other (meta)data
|| R1.1. (meta)data are released with a clear and accessible data usage license
|| R1.2. (meta)data are associated with detailed provenance
|| R1.3. (meta)data meet domain-relevant community standards
|-
! Does the dataset have any identifiers assigned?
! Is the dataset identifier included in all metadata records/files describing the data?
! How is the data described with metadata?
! What type of repository or registry is the metadata record in?
! How accessible is the data?
! Is the data available online without requiring specialised protocols or tools once access has been approved?
! Will the metadata record be available even if the data is no longer available?
! What (file) format(s) is the data available in?
! What best describes the types of vocabularies/ontologies/tagging schemas used to define the data elements?
! How is the metadata linked to other data and metadata (to enhance context and clearly indicate relationships)?
! Which of the following best describes the license/usage rights attached to the data?
! How much provenance information has been captured to facilitate data reuse?
!
|-
| Globally unique, citable and persistant (e.g. DOI, PURL, ARK or Handle)
|| Yes
|| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
|| Data is in one place but discoverable through several registries
|| Publicly accessible
|| Standard web service API (e.g. OGC)
|| Yes
|| In a structured, open standard, machine-readable format
|| Standardised open and universal using resolvable global identifiers linking to explainations
|| Meadata is represented in a machine readable format, e.g. in a linked data format such as Resource Description Framework (RDF)
|| Standard machine-readable license (e.g. Creative Commons)
|| Fully recorded in a machine-readable format
||
|-
| Web adress (URL)
|| No
|| Comprehensively, but in a text-based, non-standard format
|| Generalist public repository
|| Fully accessible to person who meet explicitly stated conditions, e.g. ethics approval for sensitive data
|| Non-standard web service (e.g. OpenAPI/Swagger/informal API)
|| No
|| In a structured, open standard, non-machine-readable format
|| Standardised vocabularies/ontologies/schema without global identifiers
|| The metadata record includes URI links to related metadata, data and definitions
|| Standard text based license
|| Fully recorded in a text format
||
|-
| Local identifier
||
|| Brief title and description
|| Domain-specific repository
|| A de-identified / modified subset of the data is publicly accessible
|| File download from online location
|| Unsure
|| Mostly in a proprietary format
|| No standards have been applied in the description of data elements
|| There are no links to other metadata
|| Non-standard machine-readably license (clearly indicating under what conditions the data may be reused)
|| Partially recorded
||
|-
| No identifier
||
|| The data is not described
|| Local institutional repository
|| Embaged access after a specific date
|| By individual arrangement
||
||
|| Data elements not described
||
|| Non-standard text-based license
|| No provenance information is recorded
||
|-
|
||
||
|| The data is not described in any repository
|| Unspecified conditional access e.g. contact the data custodian for access
|| No access to data
||
||
||
||
|| No license
||
||
|-
|
||
||
||
|| Access to metadata only
||
||
||
||
||
||
||
||
|-
|
||
||
||
|| No access to data or metadata
||
||
||
||
||
||
||
||
|}

At this point it is important to note that the assessment of databases and/or datasets using this tool is solely considered a human assessment. This may lead to different evaluations by different users. To counteract this phenomenon, it was suggested that the evaluations should be completed using the 'four-eyes principle' to create more verifiable results. Also, it is planned to additionally evaluate the respective databases using an external tool to perform a machine-assessment.

In addition, not all questions are weighted equally and thus a worse result for one question may not have a large impact whereas only choosing the second-best option in another question will bring down the overall FAIR value considerably.

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ The ARDC tool provides detailed information about the requirements for each of the options of its tool. These descriptions are documented here.
|-
! rowspan="5" | F
|| F
|| About Findable
|| Making data Findable includes assigning a [https://www.ands.org.au/guides/persistent-identifiers-awareness persistent identifier] (like a [https://www.ands.org.au/guides/doi DOI] or [https://www.ands.org.au/guides/identify-my-data Handle] ), having rich [https://www.ands.org.au/working-with-data/metadata metadata] to describe the data and making sure it is findable through disciplinary and generalist discovery portals (local and international).

|-
| F1
|| About identifiers
|| Identifiers are essential for identifying, finding, retrieving, linking and citing datasets. A Web address (URL) can be used to specify the online location of a resource but over time URLs tend to change which leads to broken links to the data. To be useful, identifiers need to be persistent and unique. Digital Object Identifiers (DOIs) and other persistent identifiers (PIDs) provide a permanent citable reference to a particular dataset. The DOI is a permanent fixed reference to the dataset no matter where it is located online and enables citation and citation metrics. Services to create a persistent identifier are often offered by your affiliated institution or the repository you are using to describe your data. Talk to your library service for more information.

Read more: [https://www.ands.org.au/working-with-data/citation-and-identifiers Persistent identifiers]

|-
| F2
|| About identifiers
|| The identifier (preferably a persistent identifier) needs to be clearly stated in the metadata record describing the data collection, and also in any associated data files or metadata.

|-
|F3
|| About metadata descriptions
|| Comprehensive metadata records will include descriptive content that facilitates discovery, access and reuse of the data being described. While there is no 'one size fits all' list, comprehensive metadata should include:
* a globally unique persistent identifier e.g. a DOI
* a title
* related people, i.e. the data creator or custodian
* how to access the data and file formats
* a description of how the data were created and how to interpret the data subject or keywords
* citation information that clearly indicates how the data should be cited
* a machine-readable data licence
* provenance and contextual information such as:
* links to related publications, projects, services and software
* methodology and processes involved in data production
* spatial and temporal coverage (if relevant)
* object-level data description

Providing metadata in a standard schema allows it to be read and used by machines as well as humans.

Read more: [https://www.ands.org.au/working-with-data/metadata Metadata]

|-
| F4
|| About metadata repositories
|| A rich metadata description alone does not ensure a dataset’s ‘findability’ on the internet; the dataset needs to be registered or indexed in a searchable resource, such as a generalist (e.g. [https://figshare.com/ Figshare], [http://datadryad.org/ Dryad], [https://zenodo.org/ Zenodo], domain-specific (e.g. [https://pangaea.de/ PANGAEA], [https://ada.edu.au/ ADA], [https://www.icpsr.umich.edu/ ICPSR]), or institutional (e.g. [https://openresearch-repository.anu.edu.au/ ANU Research Repository] , [https://ses.library.usyd.edu.au/ Sydney eScholarship Repository], [https://data.gov.au/ data.gov.au]) data repository or registry. Ideally these repositories/registries are indexed by search engines such as Google and/or Google Scholar.

|-
! rowspan = "4" | A
| A
|| About Accessible
|| [https://www.ands.org.au/working-with-data/publishing-and-reusing-data/publishing To make data accessible] may include making the data [https://www.ands.org.au/working-with-data/articulating-the-value-of-open-data/open-data open] using a standardised protocol. However the data does not necessarily have to be open. There are sometimes good reasons why data [https://www.ands.org.au/working-with-data/sensitive-data cannot be made open], for example privacy concerns, national security or commercial interests. If it is not open there should be clarity and transparency around the conditions governing access and [https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse reuse].
|-
| A1
|| About data accessibility
|| Not all data that is discoverable can be freely accessed. Often there are embargoes, access controls, and access permissions associated with data due to a variety of issues such as privacy and commercial interests. Even with all these issues much [https://www.ands.org.au/working-with-data/sensitive-data sensitive data] can be shared. Many other issues that could be perceived as blockers to sharing data may be [http://datapub.cdlib.org/closed-data-excuses-excuses overcome].
|-
| A1.1/A1.2
|| About protocols
|| Ideally users would like to retrieve appropriate internet content directly and unhindered once they have located it. Internet protocols (e.g. http and ftp) define rules and conventions for communication between devices, and tools and services are available to facilitate this process, e.g. APIs.

Protocols:
* [https://www.lifewire.com/hypertext-transfer-protocol-817944 HTTP] (Hypertext Transfer Protocol) is the set of rules for transferring files (text, graphic images, sound, video, and other multimedia files) on the [https://whatis.techtarget.com/definition/World-Wide-Web World Wide Web]. As soon as a Web user opens their Web browser, the user is indirectly making use of HTTP.
* [https://www.lifewire.com/file-transfer-protocol-817943 FTP] (File Transfer Protocol) is a standard Internet [http://searchnetworking.techtarget.com/definition/protocol protocol] for transmitting files between computers on the Internet over TCP/IP connections. Read more: [https://www.lifewire.com/definition-of-protocol-network-817949 Web protocols]
* API (Application Programming Interface): When information is made available in any machine readable format, it becomes possible to make that information directly available to programs that request that information over the web. An API is the way this information is made directly available to other machines.
View more: [https://www.youtube.com/watch?v=s7wmiS2mSXY What is an API?]

OGC - http://www.opengeospatial.org/ogc

|-
| A2
|| About metadata availability
|| Effort is often required to maintain data resources online which can often lead to it being neglected especially over long periods of time. This often leads to broken links between the metadata and the data. Having at least a description of the data in the form of a metadata record means there is a record of the data's existence allowing the possibility of someone tracking it down.

|-
! rowspan="4" | I
|| I
|| About Interoperability
|| To be interoperable (i.e. data that is interpretable by a computer, so that they can be automatically combined with other data) the data will need to use community agreed [https://www.ands.org.au/guides/file-formats formats], language and [https://www.ands.org.au/guides/vocabularies-and-research-data vocabularies]. The [https://www.ands.org.au/working-with-data/metadata metadata] will also need to use a community agreed standards and vocabularies, and contain links to related information using [https://www.ands.org.au/working-with-data/citation-and-identifiers identifiers].
|-
|| I1
|| About file formats
|| When selecting file formats for archiving, the formats should ideally be:
* Non-proprietary
* Unencrypted
* Uncompressed
* In common usage by the research community
* Adherent to an open, documented standard, such as described by the State of California (see [http://www.leginfo.ca.gov/pub/07-08/bill/asm/ab_1651-1700/ab_1668_bill_20070524_amended_asm_v96.pdf AB 1668, 2007])
** Interoperable among diverse platforms and applications
** Fully published and available royalty-free
** Fully and independently implementable by multiple software providers on multiple platforms without any intellectual property restrictions for necessary technology
** Developed and maintained by an open standards organization with a well-defined inclusive process for evolution of the standard.

From: [https://library.stanford.edu/research/data-management-services/data-best-practices/best-practices-file-formats Stanford University Library - Best practices for file formats]

File format examples:
* Structured, open standard, machine-readable format e.g. (text) PDF/A, HTML, Plain text, (images) TIFF, JPEG 2000, GIF, (audio) MP3, AIFF, WAVE, (video) MOV, MPEG, AVI, (Tabular data) CSV
* structured, open standard, non-machine-readable format, e.g. PDF, HTML, JPG
* Proprietary format, e.g. doc (Word), .xls (Excel), .ppt (PowerPoint), .sav

Read more:
* [https://www.ands.org.au/__data/assets/pdf_file/0003/731775/File-Formats.pdf ANDS]
* [https://www.europeandataportal.eu/elearning/en/module9/#/id/co-01 European Data Portal]

|-
|| I2
|| About schema standards
|| Schema standards are schemas that have gone through a formal validation process by a standards organisation, such as the International Standards Organisation (ISO) or an equivalent body such as the Dublin Core Metadata Initiative (DCMI); or, commonly used and consistently applied metadata schemas that are well documented, endorsed, and maintained can also become ‘de-facto standards’, e.g. RIF-CS for describing data collections and services used in Research Data Australia.

Read more: [https://www.ands.org.au/guides/metadata-working Metadata schema]

Standardised open and universal schemas, e.g, [https://www.dcc.ac.uk/resources/metadata-standards/datacite-metadata-schema DataCite Metadata Schema], [https://www.dcc.ac.uk/resources/metadata-standards/prov PROV], [https://www.dcc.ac.uk/resources/metadata-standards/dublin-core Dublin Core]

Domain-specific standards, e.g. [http://human-phenotype-ontology.github.io/ HPO - Human Phenotype Ontology], [https://www.nlm.nih.gov/mesh/ MeSH - Medical Subject Headings] , [https://mcp-profile-docs.readthedocs.io/en/stable/ Marine Community Profile], [https://ddialliance.org/ DDI - Data Documentation Initiative].

For more examples of standards: [http://rd-alliance.github.io/metadata-directory/ Here].

Resolvable global identifiers - An identifier is any label used to name something uniquely (whether online or offline). URLs are an example of an identifier. So are serial numbers, and personal names. A persistent identifier is guaranteed to be managed and kept up to date over a defined time period.

Read more: [https://www.ands.org.au/guides/persistent-identifiers-awareness Persistent Identifiers]

|-
||I3
|| About metadata links
|| The goal is to create as many meaningful linkages as possible between (meta)data resources to enrich the contextual knowledge about the data, balanced against the time/energy involved in making a good data model.

'''Machine-readable (meta)data'''

(Meta)data in a format that can be automatically read and processed by a computer, such as [http://opendatahandbook.org/glossary/en/terms/csv/ CSV], [http://opendatahandbook.org/glossary/en/terms/json/ JSON], [http://opendatahandbook.org/glossary/en/terms/xml/ XML] etc. For more information: [http://opendatahandbook.org/glossary/en/terms/machine-readable/ Open Data Handbook]

'''Linked Data'''

Linked Data (also known as Linking Data) can be applied to improve the exploitation of the “Web of data.” The expression refers to the publishing of structured data in a way that typed links are created between data from different sources to provide a higher level of usability. By using Linked Data, it is possible to find other, related data. Structured data should meet four requirements to be called
Linked Data:

* URIs should be assigned to all entities of the dataset.
* HTTP URIs are required to ensure that all entities can be referenced and cited by users and user agents.
* Entities should be described using standard formats such as RDF/XML.
* Links should be created to other, related entity URIs.

All data that fulfil these requirements and are released for the public are called Linked Open Data (LOD). http://www.lesliesikos.com/semantic-web-machine-readable-structured-data-with-meaningful-annotations/

Read more under the heading (meta)data include qualified references to other (meta)data in the following resource: https://www.dtls.nl/fair-data/fair-principles-explained/

For more information on Linked Data standards - RDF - https://www.w3.org/RDF/

Other related linked data standards:
* OWL - https://www.w3.org/OWL/
* SKOS - https://www.w3.org/2004/02/skos/
* SPARQL - https://www.w3.org/TR/rdf-sparql-query/

|-
! rowspan="3" | R
|| R
|| About Reusable
|| Reusable data should maintain its initial richness. For example, it should not be abridged for the purpose of explaining the findings in one particular publication. It needs a clear machine-readable [https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse licence] and [https://www.ands.org.au/partners-and-communities/ands-communities/data-provenance-interest-group provenance] information on how the data was formed. It should also use discipline-specific data and [https://www.ands.org.au/working-with-data/metadata metadata] standards to give it rich contextual information that will allow for accurate interpretation and reuse.

|-
|| R1.1
|| About licenses
|| If data is not licensed no-one else can use it. In Australia, no licence is regarded as the same as 'all rights reserved', confining any reuse to very limited circumstances. Applying a Creative Commons licence to your data is a simple way to ensure that your data can be reused. The less restrictive the licence, the more that can be done with the data.
To make it easy for the Web to know when a work is available under a particular license, a “machine readable” version of the license provides a summary of the key freedoms and obligations written into a format that software systems, search engines, and other kinds of technology can understand.
Example from the [https://creativecommons.org/choose/ Creative Commons License Choser]:

What appears in the metadata:

This work is licensed under a [https://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International License].

Code snippet:

<code>
<a rel="license" href="http://creativecommons.org/licenses/by/4.0/">

<img alt="Creative Commons License" style="border-width:0"

src="https://i.creativecommons.org/l/by/4.0/88x31.png"/>

</a> This work is licensed under a

<a rel="license" href="http://creativecommons.org/licenses/by/4.0/">

Creative Commons Attribution 4.0 International License</a>.
<\code>

Read more: https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse

|-
||R1.2
|| About provenance
|| Data provenance is used to document where a piece of data comes from and the process and methodology by which it is produced. It is important to confirm the authenticity of data enabling trust, credibility and reproducibility. This is becoming increasingly important, especially in the eScience community where research is data intensive and often involves complex data transformations and procedures ([https://www.ands.org.au/partners-and-communities/ands-communities/data-provenance-interest-group Read more]). Provenance vocabularies such as [https://www.w3.org/2005/Incubator/prov/XGR-prov-20101214/#Recommendations Provenir Ontology (PROV-O) and others].

|}

== Results for SMILES and ShareWind ==

{| class="mw-collapsible wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Datasets from the SMILES and ShareWind platforms was analysed using the self-assessment tool provided by ARDC.
! rowspan="5" |
| rowspan="5" |
! Database Code
| rowspan="24" style="background: #ABE" |
| ED4
| ED9
|-
! Database Title
|| SMILES
| ShareWind
|-
! Database Link
|| https://www.ecria-smiles.eu/
| https://sharewind.eu/
|-
! Sample Link
|| [https://www.ecria-smiles.eu/data-files/-/document_library/W32cCAfL2jMX/view_file664466?_com_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX_redirect=https%3A%2F%2Fwww.ecria-smiles.eu%3A443%2Fdata-files%2F-%2Fdocument_library%2FW32cCAfL2jMX%2Fview%2F664423%3F_com_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX_redirect%3Dhttps%253A%252F%252Fwww.ecria-smiles.eu%253A443%252Fdata-files%253Fp_p_id%253Dcom_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX%2526p_p_lifecycle%253D0%2526p_p_state%253Dnormal%2526p_p_mode%253Dview here] (link has 585 characters)
| https://sharewind.eu/records/f9d31abbc2824284b8c1d28894d9619a (data found at https://zenodo.org/record/1162738)
|-
! Sample Size
|| 4 KB
| 37 MB
|-
| colspan="6" style="background: #ABE" |
|-
! rowspan="4" | Findable
| F1. (meta)data are assigned a globally unique and persistent identifier
! Does the dataset have any identifiers assigned?
|| Web adress (URL)
| Globally unique, citable and persistant (e.g. DOI, PURL, ARK or Handle)
|-

| F2. data are described with rich metadata (defined by R1 below)
! Is the dataset identifier included in all metadata records/files describing the data?
|| No
| No
|-

| F3. metadata clearly and explicitly include the identifier of the data it describes
! How is the data described with metadata?
|| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
|-

| F4. (meta)data are registered or indexed in a searchable resource
! What type of repository or registry is the metadata record in?
|| Domain-specific repository
| Data is in one place but discoverable through several registries
|-

! rowspan="3" | Accessible

| A1. (meta)data are retrievable by their identifier using a standardized communications protocol
! How accessible is the data?
|| Publicly accessible
| Publicly accessible
|-
| A1.1 the protocol is open, free, and universally implementable; A1.2 the protocol allows for an authentication and authorization procedure, where necessary
! Is the data available online without requiring specialised protocols or tools once access has been approved?
|| File download from online location
| Standard web service API (e.g. OGC)
|-
| A2. metadata are accessible, even when the data are no longer available
! Will the metadata record be available even if the data is no longer available?
|| Unsure
| Yes
|-

! rowspan="3" | Interoperable
| I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
! What (file) format(s) is the data available in?
|| In a structured, open standard, machine-readable format
|| In a structured, open standard, non-machine-readable format
|-
| I2. (meta)data use vocabularies that follow FAIR principles
! What best describes the types of vocabularies/ontologies/tagging schemas used to define the data elements?
|| Standardised vocabularies/ontologies/schema without global identifiers
|| Standardised vocabularies/ontologies/schema without global identifiers
|-
| I3. (meta)data include qualified references to other (meta)data
! How is the metadata linked to other data and metadata (to enhance context and clearly indicate relationships)?
|| There are no links to other metadata
|| Metadata is represented in a machine readable format, e.g. in a linked data format such as Resource Description Framework (RDF)
|-

! rowspan="3" | Reusable
| R1.1. (meta)data are released with a clear and accessible data usage license
! Which of the following best describes the license/usage rights attached to the data?
|| No license
| Standard machine-readable license (e.g. Creative Commons)
|-
| R1.2. (meta)data are associated with detailed provenance
! How much provenance information has been captured to facilitate data reuse?
|| Partially recorded
|| Partially recorded
|-
| R1.3. (meta)data meet domain-relevant community standards
|
|
|
|-
| colspan="6" style="background: #ABE" |
|-
! Total across F.A.I.R
|
! Low: < 50%; Medium: 50-60%; High: > 60%
|| ~ 45 %
|| ~ 85 %
|-
| colspan="6" style="background: #ABE" |
|-
! Remarks about database
||
||
||
|| The data is provided by the open-source platform Zenodo, not the project website itself. A very positive aspect is that information on different versions of the data as well as the number of views and downloads is available.
|-
! Identified gaps
||
||
||
* No DOI, only URL
* Extremely long URL link spanning several lines with 585 characters, that can easily become corrupted
* No links to other metadata
* No license information given
* Provenance not fully recorded

||
* Dataset identifiers should be included in the files describing the data
* Provenance details are only partially recorded with a note with contact name for further information
|}

= Metadata assessment =

== Dublin Core Elements ==
{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ The DCMI Metadata Terms lists the current set of the Dublin Core vocabulary. Source: [https://dublincore.org/specifications/dublin-core/dcmi-terms/ Dublin Core Metadata Initiative]
| 1 || abstract || 11 || contributor || 21 || extent || 31 || isReplacedBy || 41 || publisher || 51 || tableOfContents
|-
| 2 || accessRights || 12 || coverage || 22 || format || 32 || isRequiredBy || 42 || references || 52 || temporal
|-
| 3 || accrualMethod || 13 || created || 23 || hasFormat || 33 || issued || 43 || relation || 53 || title
|-
| 4 || accrualPeriodicity || 14 || creator || 24 || hasPart || 34 || isVersionOf || 44 || replaces || 54 || type
|-
| 5 || accrualPolicy || 15 || date || 25 || hasVersion || 35 || language || 45 || requires || 55 || valid
|-
| 6 || alternative || 16 || dateAccepted || 26 || identifier || 36 || license || 46 || rights || ||
|-
| 7 || audience || 17 || dateCopyrighted || 27 || instructionalMethod || 37 || mediator || 47 || rightsHolder || ||
|-
| 8 || available || 18 || dateSubmitted || 28 || isFormatOf || 38 || medium || 48 || source || ||
|-
| 9 || bibliographicCitation || 19 || description || 29 || isPartOf || 39 || modified || 49 || spatial || ||
|-
| 10 || conformsTo || 20 || educationLevel || 30 || isReferencedBy || 40 || provenance || 50 || subject || ||
|}

'''Metadata Category Types according to [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO]'''

There are three main types of metadata:
* Descriptive metadata describes a resource for purposes such as discovery and identification. It can include elements such as title, abstract, author, and keywords.
* Structural metadata indicates how compound objects are put together, for example, how pages are ordered to form chapters.
* Administrative metadata provides information to help manage a resource, such as when and how it was created, file type and other technical information, and who can access it. There are several subsets of administrative data; two that sometimes are listed as separate metadata types are:
** Rights management meta-data, which deals with intellectual property rights,and
** Preservation metadata, which contains information needed to archive and preserve a resource.
''

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Simple Dublin Core Elements. 15 DC elements with their (shortened) official definitions and examples.
! DC element name !! DC definition ([https://www.dublincore.org/specifications/dublin-core/dces/ DCMI]) !! Example (Energy Domain) !! Category (determined with the aid of [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO])
|-
! Title
| A name given to the resource || Outdoor monitoring of a hybrid micro-CPV solar panel with integrated micro-tracking and diffuse capture || Descriptive
|-
! Subject
| The topic of the resource || concentrator photovoltaics; CPV; rooftop photovoltaics; integrated tracking || Descriptive
|-
! Description
| An account of the resource || Dataset from the outdoor characterization of a B Series module from Insolight at the rooftop of the Instituto de Energía Solar - Universidad Politécnica de Madrid. …. || Descriptive
|-
! Creator
| An entity primarily responsible for making the resource || Stephen Askins; Gaël Nardin || Descriptive
|-
! Publisher
| An entity responsible for making the resource available || Stephen Askins || Descriptive
|-
! Contributor
| An entity responsible for making contributions to the resource || Stephen Askins || Descriptive
|-
! Date
| A point or period of time associated with an event in the lifecycle of the resource || 2019-07-23; Date will be associated with the creation or availability of the resource. || Administrative
|-
! Type
| The nature or genre of the resource || info:eu-repo/semantics/other; dataset || Descriptive
|-
! Format
| The file format, physical medium, or dimensions of the resource || image
|| Structural
|-
! Identifier
| An unambiguous reference to the resource within a given context || https://zenodo.org/record/3346823; 10.5281/zenodo.3346823; oai:zenodo.org:3346823 || Descriptive
|-
! Source
| A related resource from which the described resource is derived || RC607.A26W574 1996 [where "RC607.A26W574 1996" is the call number of the print version of the resource, from which the present version was scanned] || Descriptive
|-
! Language
| A language of the resource || eng || Descriptive
|-
! Relation
| A related resource || info:eu-repo/grantAgreement/EC/H2020/787289/ || Descriptive
|-
! Coverage
| The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant || 1995-1996; Madrid, Spain || Descriptive
|-
! Rights
| Information about rights held in and over the resource || info:eu-repo/semantics/openAccess; https://creativecommons.org/licenses/by/4.0/legalcode || Administrative
|}

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Non-DC elements

! Non-DC element name !! Definition !! Example !! Category
|-
! Date of availability
| Since when is the data available
||
|| Impact/Exploitation

|-
! Number of users
| Is there any information on the number of accesses or users?
||
|| Impact/Exploitation

|-
! Number of scientific publications where data is cited/used
| Is there any information on the number of citations/uses?
||
|| Impact/Exploitation

|-
! Curation activities (is the versioning ongoing)
| Update, maintainance
||
|| Impact/Exploitation
|}

Attempting to put a value on Impact/Exploitation:
* '''ENTSO-E''': Found 172 results in Advanced Search (ALL) in Web of Science ([https://apps.webofknowledge.com/ WoSc]) (16/06/2020). Citations of all these search result add up to 714. This however does not mean, that all search results really explicitly used data from ENTSO-E. Some (for sure, for some were found) only mentioned it saying they adoped workflow etc. from the site.
* '''SMARD''': Found 82 results in Advanced Search (ALL=(SMARD)) in Web of Science ([https://apps.webofknowledge.com/ WoSc]) (16/06/2020). However: SMARD is also an acronym for ''Spinal muscular atrophy with respiratory distress (SMARD)''. This somewhat complicates results and needs some further discussion. All 82 search results accumumlated 2359 citations themselves.

== Results for SMILES ==

{| class="wikitable mw-collapsible"
! DC element name
! DC definition ([https://www.dublincore.org/specifications/dublin-core/dces/ DCMI])
! Category (determined with the aid of [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO])
! rowspan = "16" style="background: #ABE" |
! SMILES (ED4)
! Notes (SMILES (ED4) )

|-
! Title
| A name given to the resource
|| Descriptive
|| A
||
|-
! Subject
| The topic of the resource || Descriptive || A ||
|-
! Description
| An account of the resource || Descriptive || PA || The description of the data is very limited. For all files a general description is given. This is linked to the project deliverables but it is not indicated which deliverable are exactly corresponding. This seems to be a time-consuming process for researchers.
|-
! Creator
| An entity primarily responsible for making the resource || Descriptive || A || The metadata file uses the tag "author"
|-
! Publisher
| An entity responsible for making the resource available || Descriptive || A || This information only available under the URL but not in the metadata file
|-
! Contributor
| An entity responsible for making contributions to the resource || Descriptive || NA ||
|-
! Date
| A point or period of time associated with an event in the lifecycle of the resource || Administrative || A || This information only available under the URL but not in the metadata file
|-
! Type
| The nature or genre of the resource || Descriptive || NA || No explicit mention of the type although it could be derived from the context
|-
! Format
| The file format, physical medium, or dimensions of the resource || Structural || PA || The metadata file uses the tag "datatype" and "file"
|-
! Identifier
| An unambiguous reference to the resource within a given context || Descriptive || NA ||
|-
! Source
| A related resource from which the described resource is derived || Descriptive || NA ||
|-
! Language
| A language of the resource || Descriptive || NA || No explicit mention of the language although it could be derived from the context
|-
! Relation
| A related resource || Descriptive || NA || This information (EU grant information) only generally available under the URL but not in the metadata file
|-
! Coverage
| The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant || Descriptive || NA ||
|-
! Rights
| Information about rights held in and over the resource || Administrative || NA ||
|}

Gap analysis

2020-08-28T12:29:23Z

Christopherbs: /* Results for SMILES */

<accesscontrol>Administrators,consortium</accesscontrol>

This is the page for the gap analysis.

= Tacit knowledge "FAIRification and opening of low carbon energy research data" =
 Consortium members can add any time, they feel that something is important to note even though it is not mentioned in a standard deliverable. In other words, this page collects uncodified, tacit knowledge. It will help us later to compile suggestions and lessons learned. This kind of knowledge is collected two-fold:
#Anytime if someone feels that this should be noted. Please write down: Issue, Date, Author (could also be "anonymous"), the issue described in a few words or maximal lines>
#During the final day of workshops

=== Learning process: ===

*People are hesitant to adopt new IT technologies, this is even the case among researchers heavily relying on data, algorithms and collaborative online software (e.g., R, platforms, online conferencing, HPC, ...). The effort to encourage change is not to underestimate. Reasons are several, notably, lack of time and uncertainty about potential benefit as well as overall risk aversion preferences. EERAdata is using the online software "Only Office" to facilitate collaboration (in particular also during the Covid-19 period).
 '''FAIR and open criteria:'''
*Consortium members have a fair understanding of what FAIR/O is, but there is little knowledge and/or technical experience on how to approach the FAIRification and opening. All, however, share the view that we are at a critical point in time, where we need to implement these criteria. 
*To deepen knowledge about FAIR/O criteria, it is useful to test the criteria on a database one is familiar with. For this purpose (and to start brainstorming about the platform), AIT has developed a questionnaire for application in the use cases.
'''Metadata:'''
*A good starting point is to think about metadata and to look into existing metadata concepts in one's field. The first step is to understand that also metadata need to adhere to the FAIR/O principles.
*The next step is to increase knowledge on IT specific terms, i.e. to understand what the difference is between different '''[https://energydata.onlyoffice.eu/Products/Files/doceditor.aspx?fileid=5404340 metadata frameworks] (taxonomy, thesaurus, ontology) as well as classification of metadata (high-level, medium-level, low-level OR administrative, structural and descriptive metadata).  '''WP 2 being in charge of aligning approaches between use cases, participates in all use case kickoffs to bring everybody on the same page. The presentation is linked with "metadata frameworks".
*It is useful to supply consortium members with read aheads and watch aheads on metadata to prepare the '''[https://energydata.onlyoffice.eu/Products/Files/doceditor.aspx?fileid=5296590 first EERAdata workshop]'''. The workshop starts applications and discussions in the use cases break out sessions (and bringing insights back to the plenary), using selected databases.

= FAIR Assessment=

== FAIR Guiding Principles ==
{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ This table shows the FAIR guiding principles described by [https://www.doi.org/10.1038/sdata.2016.18 Wilkinson et al., 2016].

! To be Findable
|
* F1. (meta)data are assigned a globally unique and persistent identifier
* F2. data are described with rich metadata (defined by R1 below)
* F3. metadata clearly and explicitly include the identifier of the data it describes
* F4. (meta)data are registered or indexed in a searchable resource

|-
! To be Accessible
|
* A1. (meta)data are retrievable by their identifier using a standardized communications protocol
** A1.1 the protocol is open, free, and universally implementable
** A1.2 the protocol allows for an authentication and authorization procedure, where necessary
* A2. metadata are accessible, even when the data are no longer available

|-
! To be Interoperable
|
* I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
* I2. (meta)data use vocabularies that follow FAIR principles
* I3. (meta)data include qualified references to other (meta)data

|-
! To be Reusable
|
* R1. meta(data) are richly described with a plurality of accurate and relevant attributes
* R1.1. (meta)data are released with a clear and accessible data usage license
* R1.2. (meta)data are associated with detailed provenance
* R1.3. (meta)data meet domain-relevant community standards

|}

== Identified gaps after Workshop 1 (02/06/2020 – 04/06/2020) ==

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ This table shows a summary of the specific issues that were identified in Workshop 1. The aim was to categorise different issues to get a better understanding of how to tackle these challenges.
! FAIR/O !! Specific FAIR/O !! Gaps !! Gaps for energy domain !! Use case specific gaps !! Consequences for researchers
|-
! F
| F2
|| Metadata scope
|| Different fields require different metadata (potentially very specific)
|| UC3: lack of additional metadata for applications of materials
|| Data are less useful for the specific field
|-
! F
|| F1/F3
|| Identifiers
||
* Missing identifiers on websites
* Identifier not in downloaded data files
||
|| Makes research more difficult.
|-
! I
| I2
|| Taxonomy/ontology/common vocabulary and language issues
||
* Lack of standardisation
* Heterogeneous data makes standardisation hard
* No vocabulary documentation on websites
* Words used for same term in other languages may differ
* Databases in other languages than English
||
|| Research costs more time
|-
! I
| I3
|| References to other (meta)data
||
* No linking to source documents and related publications (for contextual knowledge)
* Possible need for links to other fields
|| UC3: link between microscopic and macroscopic materials (e.g., turbine blades)
||
* Data cannot be connected to the source
* Makes research more difficult.

|-
! R
| R1.1
|| Licensing
||
* No licenses available may mean the data is not reusable for the researcher
* Licenses not clear and accessible
* Obtaining licenses may result in more effort and costs
||
|| Data potentially not reusable

|-
! R
| R1.2
|| Reliability and provenance of data
|| When the data is collected from different and high number of sources, its reliability decreases
||
|| Data potentially not reusable

|-
! O
|
|| Privacy concerns and expected disadvantages
|| Not publishing data due to privacy concerns (sensitive data)
|| UC2: In case of distribution network data this is relevant
|| Data not accessible

|-
! Other
|
|| Conducting FAIR assessments
||
* No qualitative assessment of the data itself (which may be limited)
* Discrepancies between results conducted by humans versus machines
* Sometimes not even DC standards are met
* Metadata is not updated
* Lacking encryption of websites (https)
* Problems assessing whether metadata will be available after data is unavailable
* Interface design/layout may be unclear, incomplete or not intuitive for humans
* Authentication details (user registration login / good or bad, clarify)

||
* UC1 lack of data availability for time-series
||
* Assessment is uncertain (human assessment may need more clarification and understanding)
* Makes research more time-intensive

|}

== FAIR Self-Assessment tool ==

To conduct a human FAIR evaluation for different databases and their respective datasets, different free online tools were considered. Some seemed to be quite useful (e.g. [https://satifyd.dans.knaw.nl/ SATISFYD] ) and some were simple self-evaluation PDFs to be printed and filled out.

We decided to chose the [https://ardc.edu.au/resources/working-with-data/fair-data/fair-self-assessment-tool/ self-assessment tool] provided by the Australian Research Data Commons (ARDC). The decision was based manly upon the fact that the tools questions closely matched the FAIR categories specified by [https://www.doi.org/10.1038/sdata.2016.18 Wilkinson et al., 2016] which would make it easily comparable to the existing preferable FAIR conditions and aid with the repeatability of the results, and the half-automated scoring features that show the extent of the "FAIRness" of the analysed sample as a simple bar chart.

The result of the FAIR assessment is provided as a bar chart where an empty bar chart displays 0 % FAIR compatibility, and a full bar chart represents 100 % FAIR compatibility. The score is therefore not returned as a specific value. During the assessment below, the "Total Across FAIR" in Table ... was estimated according to the level of the bar chart from 0 % to 100 %.
Resulting scores where categorised as follows: Low: < 50%; Medium: 50-60%; High: > 60% .

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ All options for each question of the ARDCs self-assessment tool
!colspan="4"|Findable
!colspan="3"|Accessible
!colspan="3"|Interoperable
!colspan="3"|Reusable
|-
| F1. (meta)data are assigned a globally unique and persistent identifier
|| F2. data are described with rich metadata (defined by R1 below)
|| F3. metadata clearly and explicitly include the identifier of the data it describes
|| F4. (meta)data are registered or indexed in a searchable resource
|| A1. (meta)data are retrievable by their identifier using a standardized communications protocol
|| A1.1 the protocol is open, free, and universally implementable and A1.2 the protocol allows for an authentication and authorization procedure, where necessary
|| A2. metadata are accessible, even when the data are no longer available
|| I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
|| I2. (meta)data use vocabularies that follow FAIR principles
|| I3. (meta)data include qualified references to other (meta)data
|| R1.1. (meta)data are released with a clear and accessible data usage license
|| R1.2. (meta)data are associated with detailed provenance
|| R1.3. (meta)data meet domain-relevant community standards
|-
! Does the dataset have any identifiers assigned?
! Is the dataset identifier included in all metadata records/files describing the data?
! How is the data described with metadata?
! What type of repository or registry is the metadata record in?
! How accessible is the data?
! Is the data available online without requiring specialised protocols or tools once access has been approved?
! Will the metadata record be available even if the data is no longer available?
! What (file) format(s) is the data available in?
! What best describes the types of vocabularies/ontologies/tagging schemas used to define the data elements?
! How is the metadata linked to other data and metadata (to enhance context and clearly indicate relationships)?
! Which of the following best describes the license/usage rights attached to the data?
! How much provenance information has been captured to facilitate data reuse?
!
|-
| Globally unique, citable and persistant (e.g. DOI, PURL, ARK or Handle)
|| Yes
|| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
|| Data is in one place but discoverable through several registries
|| Publicly accessible
|| Standard web service API (e.g. OGC)
|| Yes
|| In a structured, open standard, machine-readable format
|| Standardised open and universal using resolvable global identifiers linking to explainations
|| Meadata is represented in a machine readable format, e.g. in a linked data format such as Resource Description Framework (RDF)
|| Standard machine-readable license (e.g. Creative Commons)
|| Fully recorded in a machine-readable format
||
|-
| Web adress (URL)
|| No
|| Comprehensively, but in a text-based, non-standard format
|| Generalist public repository
|| Fully accessible to person who meet explicitly stated conditions, e.g. ethics approval for sensitive data
|| Non-standard web service (e.g. OpenAPI/Swagger/informal API)
|| No
|| In a structured, open standard, non-machine-readable format
|| Standardised vocabularies/ontologies/schema without global identifiers
|| The metadata record includes URI links to related metadata, data and definitions
|| Standard text based license
|| Fully recorded in a text format
||
|-
| Local identifier
||
|| Brief title and description
|| Domain-specific repository
|| A de-identified / modified subset of the data is publicly accessible
|| File download from online location
|| Unsure
|| Mostly in a proprietary format
|| No standards have been applied in the description of data elements
|| There are no links to other metadata
|| Non-standard machine-readably license (clearly indicating under what conditions the data may be reused)
|| Partially recorded
||
|-
| No identifier
||
|| The data is not described
|| Local institutional repository
|| Embaged access after a specific date
|| By individual arrangement
||
||
|| Data elements not described
||
|| Non-standard text-based license
|| No provenance information is recorded
||
|-
|
||
||
|| The data is not described in any repository
|| Unspecified conditional access e.g. contact the data custodian for access
|| No access to data
||
||
||
||
|| No license
||
||
|-
|
||
||
||
|| Access to metadata only
||
||
||
||
||
||
||
||
|-
|
||
||
||
|| No access to data or metadata
||
||
||
||
||
||
||
||
|}

At this point it is important to note that the assessment of databases and/or datasets using this tool is solely considered a human assessment. This may lead to different evaluations by different users. To counteract this phenomenon, it was suggested that the evaluations should be completed using the 'four-eyes principle' to create more verifiable results. Also, it is planned to additionally evaluate the respective databases using an external tool to perform a machine-assessment.

In addition, not all questions are weighted equally and thus a worse result for one question may not have a large impact whereas only choosing the second-best option in another question will bring down the overall FAIR value considerably.

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ The ARDC tool provides detailed information about the requirements for each of the options of its tool. These descriptions are documented here.
|-
! rowspan="5" | F
|| F
|| About Findable
|| Making data Findable includes assigning a [https://www.ands.org.au/guides/persistent-identifiers-awareness persistent identifier] (like a [https://www.ands.org.au/guides/doi DOI] or [https://www.ands.org.au/guides/identify-my-data Handle] ), having rich [https://www.ands.org.au/working-with-data/metadata metadata] to describe the data and making sure it is findable through disciplinary and generalist discovery portals (local and international).

|-
| F1
|| About identifiers
|| Identifiers are essential for identifying, finding, retrieving, linking and citing datasets. A Web address (URL) can be used to specify the online location of a resource but over time URLs tend to change which leads to broken links to the data. To be useful, identifiers need to be persistent and unique. Digital Object Identifiers (DOIs) and other persistent identifiers (PIDs) provide a permanent citable reference to a particular dataset. The DOI is a permanent fixed reference to the dataset no matter where it is located online and enables citation and citation metrics. Services to create a persistent identifier are often offered by your affiliated institution or the repository you are using to describe your data. Talk to your library service for more information.

Read more: [https://www.ands.org.au/working-with-data/citation-and-identifiers Persistent identifiers]

|-
| F2
|| About identifiers
|| The identifier (preferably a persistent identifier) needs to be clearly stated in the metadata record describing the data collection, and also in any associated data files or metadata.

|-
|F3
|| About metadata descriptions
|| Comprehensive metadata records will include descriptive content that facilitates discovery, access and reuse of the data being described. While there is no 'one size fits all' list, comprehensive metadata should include:
* a globally unique persistent identifier e.g. a DOI
* a title
* related people, i.e. the data creator or custodian
* how to access the data and file formats
* a description of how the data were created and how to interpret the data subject or keywords
* citation information that clearly indicates how the data should be cited
* a machine-readable data licence
* provenance and contextual information such as:
* links to related publications, projects, services and software
* methodology and processes involved in data production
* spatial and temporal coverage (if relevant)
* object-level data description

Providing metadata in a standard schema allows it to be read and used by machines as well as humans.

Read more: [https://www.ands.org.au/working-with-data/metadata Metadata]

|-
| F4
|| About metadata repositories
|| A rich metadata description alone does not ensure a dataset’s ‘findability’ on the internet; the dataset needs to be registered or indexed in a searchable resource, such as a generalist (e.g. [https://figshare.com/ Figshare], [http://datadryad.org/ Dryad], [https://zenodo.org/ Zenodo], domain-specific (e.g. [https://pangaea.de/ PANGAEA], [https://ada.edu.au/ ADA], [https://www.icpsr.umich.edu/ ICPSR]), or institutional (e.g. [https://openresearch-repository.anu.edu.au/ ANU Research Repository] , [https://ses.library.usyd.edu.au/ Sydney eScholarship Repository], [https://data.gov.au/ data.gov.au]) data repository or registry. Ideally these repositories/registries are indexed by search engines such as Google and/or Google Scholar.

|-
! rowspan = "4" | A
| A
|| About Accessible
|| [https://www.ands.org.au/working-with-data/publishing-and-reusing-data/publishing To make data accessible] may include making the data [https://www.ands.org.au/working-with-data/articulating-the-value-of-open-data/open-data open] using a standardised protocol. However the data does not necessarily have to be open. There are sometimes good reasons why data [https://www.ands.org.au/working-with-data/sensitive-data cannot be made open], for example privacy concerns, national security or commercial interests. If it is not open there should be clarity and transparency around the conditions governing access and [https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse reuse].
|-
| A1
|| About data accessibility
|| Not all data that is discoverable can be freely accessed. Often there are embargoes, access controls, and access permissions associated with data due to a variety of issues such as privacy and commercial interests. Even with all these issues much [https://www.ands.org.au/working-with-data/sensitive-data sensitive data] can be shared. Many other issues that could be perceived as blockers to sharing data may be [http://datapub.cdlib.org/closed-data-excuses-excuses overcome].
|-
| A1.1/A1.2
|| About protocols
|| Ideally users would like to retrieve appropriate internet content directly and unhindered once they have located it. Internet protocols (e.g. http and ftp) define rules and conventions for communication between devices, and tools and services are available to facilitate this process, e.g. APIs.

Protocols:
* [https://www.lifewire.com/hypertext-transfer-protocol-817944 HTTP] (Hypertext Transfer Protocol) is the set of rules for transferring files (text, graphic images, sound, video, and other multimedia files) on the [https://whatis.techtarget.com/definition/World-Wide-Web World Wide Web]. As soon as a Web user opens their Web browser, the user is indirectly making use of HTTP.
* [https://www.lifewire.com/file-transfer-protocol-817943 FTP] (File Transfer Protocol) is a standard Internet [http://searchnetworking.techtarget.com/definition/protocol protocol] for transmitting files between computers on the Internet over TCP/IP connections. Read more: [https://www.lifewire.com/definition-of-protocol-network-817949 Web protocols]
* API (Application Programming Interface): When information is made available in any machine readable format, it becomes possible to make that information directly available to programs that request that information over the web. An API is the way this information is made directly available to other machines.
View more: [https://www.youtube.com/watch?v=s7wmiS2mSXY What is an API?]

OGC - http://www.opengeospatial.org/ogc

|-
| A2
|| About metadata availability
|| Effort is often required to maintain data resources online which can often lead to it being neglected especially over long periods of time. This often leads to broken links between the metadata and the data. Having at least a description of the data in the form of a metadata record means there is a record of the data's existence allowing the possibility of someone tracking it down.

|-
! rowspan="4" | I
|| I
|| About Interoperability
|| To be interoperable (i.e. data that is interpretable by a computer, so that they can be automatically combined with other data) the data will need to use community agreed [https://www.ands.org.au/guides/file-formats formats], language and [https://www.ands.org.au/guides/vocabularies-and-research-data vocabularies]. The [https://www.ands.org.au/working-with-data/metadata metadata] will also need to use a community agreed standards and vocabularies, and contain links to related information using [https://www.ands.org.au/working-with-data/citation-and-identifiers identifiers].
|-
|| I1
|| About file formats
|| When selecting file formats for archiving, the formats should ideally be:
* Non-proprietary
* Unencrypted
* Uncompressed
* In common usage by the research community
* Adherent to an open, documented standard, such as described by the State of California (see [http://www.leginfo.ca.gov/pub/07-08/bill/asm/ab_1651-1700/ab_1668_bill_20070524_amended_asm_v96.pdf AB 1668, 2007])
** Interoperable among diverse platforms and applications
** Fully published and available royalty-free
** Fully and independently implementable by multiple software providers on multiple platforms without any intellectual property restrictions for necessary technology
** Developed and maintained by an open standards organization with a well-defined inclusive process for evolution of the standard.

From: [https://library.stanford.edu/research/data-management-services/data-best-practices/best-practices-file-formats Stanford University Library - Best practices for file formats]

File format examples:
* Structured, open standard, machine-readable format e.g. (text) PDF/A, HTML, Plain text, (images) TIFF, JPEG 2000, GIF, (audio) MP3, AIFF, WAVE, (video) MOV, MPEG, AVI, (Tabular data) CSV
* structured, open standard, non-machine-readable format, e.g. PDF, HTML, JPG
* Proprietary format, e.g. doc (Word), .xls (Excel), .ppt (PowerPoint), .sav

Read more:
* [https://www.ands.org.au/__data/assets/pdf_file/0003/731775/File-Formats.pdf ANDS]
* [https://www.europeandataportal.eu/elearning/en/module9/#/id/co-01 European Data Portal]

|-
|| I2
|| About schema standards
|| Schema standards are schemas that have gone through a formal validation process by a standards organisation, such as the International Standards Organisation (ISO) or an equivalent body such as the Dublin Core Metadata Initiative (DCMI); or, commonly used and consistently applied metadata schemas that are well documented, endorsed, and maintained can also become ‘de-facto standards’, e.g. RIF-CS for describing data collections and services used in Research Data Australia.

Read more: [https://www.ands.org.au/guides/metadata-working Metadata schema]

Standardised open and universal schemas, e.g, [https://www.dcc.ac.uk/resources/metadata-standards/datacite-metadata-schema DataCite Metadata Schema], [https://www.dcc.ac.uk/resources/metadata-standards/prov PROV], [https://www.dcc.ac.uk/resources/metadata-standards/dublin-core Dublin Core]

Domain-specific standards, e.g. [http://human-phenotype-ontology.github.io/ HPO - Human Phenotype Ontology], [https://www.nlm.nih.gov/mesh/ MeSH - Medical Subject Headings] , [https://mcp-profile-docs.readthedocs.io/en/stable/ Marine Community Profile], [https://ddialliance.org/ DDI - Data Documentation Initiative].

For more examples of standards: [http://rd-alliance.github.io/metadata-directory/ Here].

Resolvable global identifiers - An identifier is any label used to name something uniquely (whether online or offline). URLs are an example of an identifier. So are serial numbers, and personal names. A persistent identifier is guaranteed to be managed and kept up to date over a defined time period.

Read more: [https://www.ands.org.au/guides/persistent-identifiers-awareness Persistent Identifiers]

|-
||I3
|| About metadata links
|| The goal is to create as many meaningful linkages as possible between (meta)data resources to enrich the contextual knowledge about the data, balanced against the time/energy involved in making a good data model.

'''Machine-readable (meta)data'''

(Meta)data in a format that can be automatically read and processed by a computer, such as [http://opendatahandbook.org/glossary/en/terms/csv/ CSV], [http://opendatahandbook.org/glossary/en/terms/json/ JSON], [http://opendatahandbook.org/glossary/en/terms/xml/ XML] etc. For more information: [http://opendatahandbook.org/glossary/en/terms/machine-readable/ Open Data Handbook]

'''Linked Data'''

Linked Data (also known as Linking Data) can be applied to improve the exploitation of the “Web of data.” The expression refers to the publishing of structured data in a way that typed links are created between data from different sources to provide a higher level of usability. By using Linked Data, it is possible to find other, related data. Structured data should meet four requirements to be called
Linked Data:

* URIs should be assigned to all entities of the dataset.
* HTTP URIs are required to ensure that all entities can be referenced and cited by users and user agents.
* Entities should be described using standard formats such as RDF/XML.
* Links should be created to other, related entity URIs.

All data that fulfil these requirements and are released for the public are called Linked Open Data (LOD). http://www.lesliesikos.com/semantic-web-machine-readable-structured-data-with-meaningful-annotations/

Read more under the heading (meta)data include qualified references to other (meta)data in the following resource: https://www.dtls.nl/fair-data/fair-principles-explained/

For more information on Linked Data standards - RDF - https://www.w3.org/RDF/

Other related linked data standards:
* OWL - https://www.w3.org/OWL/
* SKOS - https://www.w3.org/2004/02/skos/
* SPARQL - https://www.w3.org/TR/rdf-sparql-query/

|-
! rowspan="3" | R
|| R
|| About Reusable
|| Reusable data should maintain its initial richness. For example, it should not be abridged for the purpose of explaining the findings in one particular publication. It needs a clear machine-readable [https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse licence] and [https://www.ands.org.au/partners-and-communities/ands-communities/data-provenance-interest-group provenance] information on how the data was formed. It should also use discipline-specific data and [https://www.ands.org.au/working-with-data/metadata metadata] standards to give it rich contextual information that will allow for accurate interpretation and reuse.

|-
|| R1.1
|| About licenses
|| If data is not licensed no-one else can use it. In Australia, no licence is regarded as the same as 'all rights reserved', confining any reuse to very limited circumstances. Applying a Creative Commons licence to your data is a simple way to ensure that your data can be reused. The less restrictive the licence, the more that can be done with the data.
To make it easy for the Web to know when a work is available under a particular license, a “machine readable” version of the license provides a summary of the key freedoms and obligations written into a format that software systems, search engines, and other kinds of technology can understand.
Example from the [https://creativecommons.org/choose/ Creative Commons License Choser]:

What appears in the metadata:

This work is licensed under a [https://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International License].

Code snippet:

<code>
<a rel="license" href="http://creativecommons.org/licenses/by/4.0/">

<img alt="Creative Commons License" style="border-width:0"

src="https://i.creativecommons.org/l/by/4.0/88x31.png"/>

</a> This work is licensed under a

<a rel="license" href="http://creativecommons.org/licenses/by/4.0/">

Creative Commons Attribution 4.0 International License</a>.
<\code>

Read more: https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse

|-
||R1.2
|| About provenance
|| Data provenance is used to document where a piece of data comes from and the process and methodology by which it is produced. It is important to confirm the authenticity of data enabling trust, credibility and reproducibility. This is becoming increasingly important, especially in the eScience community where research is data intensive and often involves complex data transformations and procedures ([https://www.ands.org.au/partners-and-communities/ands-communities/data-provenance-interest-group Read more]). Provenance vocabularies such as [https://www.w3.org/2005/Incubator/prov/XGR-prov-20101214/#Recommendations Provenir Ontology (PROV-O) and others].

|}

== Results for SMILES and ShareWind ==

{| class="mw-collapsible wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Datasets from the SMILES and ShareWind platforms was analysed using the self-assessment tool provided by ARDC.
! rowspan="5" |
| rowspan="5" |
! Database Code
| rowspan="24" style="background: #ABE" |
| ED4
| ED9
|-
! Database Title
|| SMILES
| ShareWind
|-
! Database Link
|| https://www.ecria-smiles.eu/
| https://sharewind.eu/
|-
! Sample Link
|| [https://www.ecria-smiles.eu/data-files/-/document_library/W32cCAfL2jMX/view_file664466?_com_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX_redirect=https%3A%2F%2Fwww.ecria-smiles.eu%3A443%2Fdata-files%2F-%2Fdocument_library%2FW32cCAfL2jMX%2Fview%2F664423%3F_com_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX_redirect%3Dhttps%253A%252F%252Fwww.ecria-smiles.eu%253A443%252Fdata-files%253Fp_p_id%253Dcom_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX%2526p_p_lifecycle%253D0%2526p_p_state%253Dnormal%2526p_p_mode%253Dview here] (link has 585 characters)
| https://sharewind.eu/records/f9d31abbc2824284b8c1d28894d9619a (data found at https://zenodo.org/record/1162738)
|-
! Sample Size
|| 4 KB
| 37 MB
|-
| colspan="6" style="background: #ABE" |
|-
! rowspan="4" | Findable
| F1. (meta)data are assigned a globally unique and persistent identifier
! Does the dataset have any identifiers assigned?
|| Web adress (URL)
| Globally unique, citable and persistant (e.g. DOI, PURL, ARK or Handle)
|-

| F2. data are described with rich metadata (defined by R1 below)
! Is the dataset identifier included in all metadata records/files describing the data?
|| No
| No
|-

| F3. metadata clearly and explicitly include the identifier of the data it describes
! How is the data described with metadata?
|| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
|-

| F4. (meta)data are registered or indexed in a searchable resource
! What type of repository or registry is the metadata record in?
|| Domain-specific repository
| Data is in one place but discoverable through several registries
|-

! rowspan="3" | Accessible

| A1. (meta)data are retrievable by their identifier using a standardized communications protocol
! How accessible is the data?
|| Publicly accessible
| Publicly accessible
|-
| A1.1 the protocol is open, free, and universally implementable; A1.2 the protocol allows for an authentication and authorization procedure, where necessary
! Is the data available online without requiring specialised protocols or tools once access has been approved?
|| File download from online location
| Standard web service API (e.g. OGC)
|-
| A2. metadata are accessible, even when the data are no longer available
! Will the metadata record be available even if the data is no longer available?
|| Unsure
| Yes
|-

! rowspan="3" | Interoperable
| I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
! What (file) format(s) is the data available in?
|| In a structured, open standard, machine-readable format
|| In a structured, open standard, non-machine-readable format
|-
| I2. (meta)data use vocabularies that follow FAIR principles
! What best describes the types of vocabularies/ontologies/tagging schemas used to define the data elements?
|| Standardised vocabularies/ontologies/schema without global identifiers
|| Standardised vocabularies/ontologies/schema without global identifiers
|-
| I3. (meta)data include qualified references to other (meta)data
! How is the metadata linked to other data and metadata (to enhance context and clearly indicate relationships)?
|| There are no links to other metadata
|| Metadata is represented in a machine readable format, e.g. in a linked data format such as Resource Description Framework (RDF)
|-

! rowspan="3" | Reusable
| R1.1. (meta)data are released with a clear and accessible data usage license
! Which of the following best describes the license/usage rights attached to the data?
|| No license
| Standard machine-readable license (e.g. Creative Commons)
|-
| R1.2. (meta)data are associated with detailed provenance
! How much provenance information has been captured to facilitate data reuse?
|| Partially recorded
|| Partially recorded
|-
| R1.3. (meta)data meet domain-relevant community standards
|
|
|
|-
| colspan="6" style="background: #ABE" |
|-
! Total across F.A.I.R
|
! Low: < 50%; Medium: 50-60%; High: > 60%
|| ~ 45 %
|| ~ 85 %
|-
| colspan="6" style="background: #ABE" |
|-
! Remarks about database
||
||
||
|| The data is provided by the open-source platform Zenodo, not the project website itself. A very positive aspect is that information on different versions of the data as well as the number of views and downloads is available.
|-
! Identified gaps
||
||
||
* No DOI, only URL
* Extremely long URL link spanning several lines with 585 characters, that can easily become corrupted
* No links to other metadata
* No license information given
* Provenance not fully recorded

||
* Dataset identifiers should be included in the files describing the data
* Provenance details are only partially recorded with a note with contact name for further information
|}

= Metadata assessment =

== Dublin Core Elements ==
{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ The DCMI Metadata Terms lists the current set of the Dublin Core vocabulary. Source: [https://dublincore.org/specifications/dublin-core/dcmi-terms/ Dublin Core Metadata Initiative]
| 1 || abstract || 11 || contributor || 21 || extent || 31 || isReplacedBy || 41 || publisher || 51 || tableOfContents
|-
| 2 || accessRights || 12 || coverage || 22 || format || 32 || isRequiredBy || 42 || references || 52 || temporal
|-
| 3 || accrualMethod || 13 || created || 23 || hasFormat || 33 || issued || 43 || relation || 53 || title
|-
| 4 || accrualPeriodicity || 14 || creator || 24 || hasPart || 34 || isVersionOf || 44 || replaces || 54 || type
|-
| 5 || accrualPolicy || 15 || date || 25 || hasVersion || 35 || language || 45 || requires || 55 || valid
|-
| 6 || alternative || 16 || dateAccepted || 26 || identifier || 36 || license || 46 || rights || ||
|-
| 7 || audience || 17 || dateCopyrighted || 27 || instructionalMethod || 37 || mediator || 47 || rightsHolder || ||
|-
| 8 || available || 18 || dateSubmitted || 28 || isFormatOf || 38 || medium || 48 || source || ||
|-
| 9 || bibliographicCitation || 19 || description || 29 || isPartOf || 39 || modified || 49 || spatial || ||
|-
| 10 || conformsTo || 20 || educationLevel || 30 || isReferencedBy || 40 || provenance || 50 || subject || ||
|}

'''Metadata Category Types according to [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO]'''

There are three main types of metadata:
* Descriptive metadata describes a resource for purposes such as discovery and identification. It can include elements such as title, abstract, author, and keywords.
* Structural metadata indicates how compound objects are put together, for example, how pages are ordered to form chapters.
* Administrative metadata provides information to help manage a resource, such as when and how it was created, file type and other technical information, and who can access it. There are several subsets of administrative data; two that sometimes are listed as separate metadata types are:
** Rights management meta-data, which deals with intellectual property rights,and
** Preservation metadata, which contains information needed to archive and preserve a resource.
''

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Simple Dublin Core Elements. 15 DC elements with their (shortened) official definitions and examples.
! DC element name !! DC definition ([https://www.dublincore.org/specifications/dublin-core/dces/ DCMI]) !! Example (Energy Domain) !! Category (determined with the aid of [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO])
|-
! Title
| A name given to the resource || Outdoor monitoring of a hybrid micro-CPV solar panel with integrated micro-tracking and diffuse capture || Descriptive
|-
! Subject
| The topic of the resource || concentrator photovoltaics; CPV; rooftop photovoltaics; integrated tracking || Descriptive
|-
! Description
| An account of the resource || Dataset from the outdoor characterization of a B Series module from Insolight at the rooftop of the Instituto de Energía Solar - Universidad Politécnica de Madrid. …. || Descriptive
|-
! Creator
| An entity primarily responsible for making the resource || Stephen Askins; Gaël Nardin || Descriptive
|-
! Publisher
| An entity responsible for making the resource available || Stephen Askins || Descriptive
|-
! Contributor
| An entity responsible for making contributions to the resource || Stephen Askins || Descriptive
|-
! Date
| A point or period of time associated with an event in the lifecycle of the resource || 2019-07-23; Date will be associated with the creation or availability of the resource. || Administrative
|-
! Type
| The nature or genre of the resource || info:eu-repo/semantics/other; dataset || Descriptive
|-
! Format
| The file format, physical medium, or dimensions of the resource || image
|| Structural
|-
! Identifier
| An unambiguous reference to the resource within a given context || https://zenodo.org/record/3346823; 10.5281/zenodo.3346823; oai:zenodo.org:3346823 || Descriptive
|-
! Source
| A related resource from which the described resource is derived || RC607.A26W574 1996 [where "RC607.A26W574 1996" is the call number of the print version of the resource, from which the present version was scanned] || Descriptive
|-
! Language
| A language of the resource || eng || Descriptive
|-
! Relation
| A related resource || info:eu-repo/grantAgreement/EC/H2020/787289/ || Descriptive
|-
! Coverage
| The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant || 1995-1996; Madrid, Spain || Descriptive
|-
! Rights
| Information about rights held in and over the resource || info:eu-repo/semantics/openAccess; https://creativecommons.org/licenses/by/4.0/legalcode || Administrative
|}

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Non-DC elements

! Non-DC element name !! Definition !! Example !! Category
|-
! Date of availability
| Since when is the data available
||
|| Impact/Exploitation

|-
! Number of users
| Is there any information on the number of accesses or users?
||
|| Impact/Exploitation

|-
! Number of scientific publications where data is cited/used
| Is there any information on the number of citations/uses?
||
|| Impact/Exploitation

|-
! Curation activities (is the versioning ongoing)
| Update, maintainance
||
|| Impact/Exploitation
|}

Attempting to put a value on Impact/Exploitation:
* '''ENTSO-E''': Found 172 results in Advanced Search (ALL) in Web of Science ([https://apps.webofknowledge.com/ WoSc]) (16/06/2020). Citations of all these search result add up to 714. This however does not mean, that all search results really explicitly used data from ENTSO-E. Some (for sure, for some were found) only mentioned it saying they adoped workflow etc. from the site.
* '''SMARD''': Found 82 results in Advanced Search (ALL=(SMARD)) in Web of Science ([https://apps.webofknowledge.com/ WoSc]) (16/06/2020). However: SMARD is also an acronym for ''Spinal muscular atrophy with respiratory distress (SMARD)''. This somewhat complicates results and needs some further discussion. All 82 search results accumumlated 2359 citations themselves.

== Results for SMILES ==

{| class="wikitable mw-collapsible"
! DC element name
! DC definition ([https://www.dublincore.org/specifications/dublin-core/dces/ DCMI])
! Category (determined with the aid of [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO])
! rowspan = "16" style="background: #ABE" |
! SMILES (ED4)
! Notes (SMILES (ED4) )

|-
! Title
| A name given to the resource
|| Descriptive
|| A
||
|-
! Subject
| The topic of the resource || Descriptive || A ||
|-
! Description
| An account of the resource || Descriptive || PA || The information given is very limited
|-
! Creator
| An entity primarily responsible for making the resource || Descriptive || A || The metadata file uses the tag "author"
|-
! Publisher
| An entity responsible for making the resource available || Descriptive || A || This information only available under the URL but not in the metadata file
|-
! Contributor
| An entity responsible for making contributions to the resource || Descriptive || NA ||
|-
! Date
| A point or period of time associated with an event in the lifecycle of the resource || Administrative || A || This information only available under the URL but not in the metadata file
|-
! Type
| The nature or genre of the resource || Descriptive || NA || No explicit mention of the type although it could be derived from the context
|-
! Format
| The file format, physical medium, or dimensions of the resource || Structural || PA || The metadata file uses the tag "datatype" and "file"
|-
! Identifier
| An unambiguous reference to the resource within a given context || Descriptive || NA ||
|-
! Source
| A related resource from which the described resource is derived || Descriptive || NA ||
|-
! Language
| A language of the resource || Descriptive || NA || No explicit mention of the language although it could be derived from the context
|-
! Relation
| A related resource || Descriptive || NA || This information (EU grant information) only generally available under the URL but not in the metadata file
|-
! Coverage
| The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant || Descriptive || NA ||
|-
! Rights
| Information about rights held in and over the resource || Administrative || NA ||
|}

Gap analysis

2020-08-28T12:26:42Z

Christopherbs: /* Results for SMILES */

<accesscontrol>Administrators,consortium</accesscontrol>

This is the page for the gap analysis.

= Tacit knowledge "FAIRification and opening of low carbon energy research data" =
 Consortium members can add any time, they feel that something is important to note even though it is not mentioned in a standard deliverable. In other words, this page collects uncodified, tacit knowledge. It will help us later to compile suggestions and lessons learned. This kind of knowledge is collected two-fold:
#Anytime if someone feels that this should be noted. Please write down: Issue, Date, Author (could also be "anonymous"), the issue described in a few words or maximal lines>
#During the final day of workshops

=== Learning process: ===

*People are hesitant to adopt new IT technologies, this is even the case among researchers heavily relying on data, algorithms and collaborative online software (e.g., R, platforms, online conferencing, HPC, ...). The effort to encourage change is not to underestimate. Reasons are several, notably, lack of time and uncertainty about potential benefit as well as overall risk aversion preferences. EERAdata is using the online software "Only Office" to facilitate collaboration (in particular also during the Covid-19 period).
 '''FAIR and open criteria:'''
*Consortium members have a fair understanding of what FAIR/O is, but there is little knowledge and/or technical experience on how to approach the FAIRification and opening. All, however, share the view that we are at a critical point in time, where we need to implement these criteria. 
*To deepen knowledge about FAIR/O criteria, it is useful to test the criteria on a database one is familiar with. For this purpose (and to start brainstorming about the platform), AIT has developed a questionnaire for application in the use cases.
'''Metadata:'''
*A good starting point is to think about metadata and to look into existing metadata concepts in one's field. The first step is to understand that also metadata need to adhere to the FAIR/O principles.
*The next step is to increase knowledge on IT specific terms, i.e. to understand what the difference is between different '''[https://energydata.onlyoffice.eu/Products/Files/doceditor.aspx?fileid=5404340 metadata frameworks] (taxonomy, thesaurus, ontology) as well as classification of metadata (high-level, medium-level, low-level OR administrative, structural and descriptive metadata).  '''WP 2 being in charge of aligning approaches between use cases, participates in all use case kickoffs to bring everybody on the same page. The presentation is linked with "metadata frameworks".
*It is useful to supply consortium members with read aheads and watch aheads on metadata to prepare the '''[https://energydata.onlyoffice.eu/Products/Files/doceditor.aspx?fileid=5296590 first EERAdata workshop]'''. The workshop starts applications and discussions in the use cases break out sessions (and bringing insights back to the plenary), using selected databases.

= FAIR Assessment=

== FAIR Guiding Principles ==
{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ This table shows the FAIR guiding principles described by [https://www.doi.org/10.1038/sdata.2016.18 Wilkinson et al., 2016].

! To be Findable
|
* F1. (meta)data are assigned a globally unique and persistent identifier
* F2. data are described with rich metadata (defined by R1 below)
* F3. metadata clearly and explicitly include the identifier of the data it describes
* F4. (meta)data are registered or indexed in a searchable resource

|-
! To be Accessible
|
* A1. (meta)data are retrievable by their identifier using a standardized communications protocol
** A1.1 the protocol is open, free, and universally implementable
** A1.2 the protocol allows for an authentication and authorization procedure, where necessary
* A2. metadata are accessible, even when the data are no longer available

|-
! To be Interoperable
|
* I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
* I2. (meta)data use vocabularies that follow FAIR principles
* I3. (meta)data include qualified references to other (meta)data

|-
! To be Reusable
|
* R1. meta(data) are richly described with a plurality of accurate and relevant attributes
* R1.1. (meta)data are released with a clear and accessible data usage license
* R1.2. (meta)data are associated with detailed provenance
* R1.3. (meta)data meet domain-relevant community standards

|}

== Identified gaps after Workshop 1 (02/06/2020 – 04/06/2020) ==

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ This table shows a summary of the specific issues that were identified in Workshop 1. The aim was to categorise different issues to get a better understanding of how to tackle these challenges.
! FAIR/O !! Specific FAIR/O !! Gaps !! Gaps for energy domain !! Use case specific gaps !! Consequences for researchers
|-
! F
| F2
|| Metadata scope
|| Different fields require different metadata (potentially very specific)
|| UC3: lack of additional metadata for applications of materials
|| Data are less useful for the specific field
|-
! F
|| F1/F3
|| Identifiers
||
* Missing identifiers on websites
* Identifier not in downloaded data files
||
|| Makes research more difficult.
|-
! I
| I2
|| Taxonomy/ontology/common vocabulary and language issues
||
* Lack of standardisation
* Heterogeneous data makes standardisation hard
* No vocabulary documentation on websites
* Words used for same term in other languages may differ
* Databases in other languages than English
||
|| Research costs more time
|-
! I
| I3
|| References to other (meta)data
||
* No linking to source documents and related publications (for contextual knowledge)
* Possible need for links to other fields
|| UC3: link between microscopic and macroscopic materials (e.g., turbine blades)
||
* Data cannot be connected to the source
* Makes research more difficult.

|-
! R
| R1.1
|| Licensing
||
* No licenses available may mean the data is not reusable for the researcher
* Licenses not clear and accessible
* Obtaining licenses may result in more effort and costs
||
|| Data potentially not reusable

|-
! R
| R1.2
|| Reliability and provenance of data
|| When the data is collected from different and high number of sources, its reliability decreases
||
|| Data potentially not reusable

|-
! O
|
|| Privacy concerns and expected disadvantages
|| Not publishing data due to privacy concerns (sensitive data)
|| UC2: In case of distribution network data this is relevant
|| Data not accessible

|-
! Other
|
|| Conducting FAIR assessments
||
* No qualitative assessment of the data itself (which may be limited)
* Discrepancies between results conducted by humans versus machines
* Sometimes not even DC standards are met
* Metadata is not updated
* Lacking encryption of websites (https)
* Problems assessing whether metadata will be available after data is unavailable
* Interface design/layout may be unclear, incomplete or not intuitive for humans
* Authentication details (user registration login / good or bad, clarify)

||
* UC1 lack of data availability for time-series
||
* Assessment is uncertain (human assessment may need more clarification and understanding)
* Makes research more time-intensive

|}

== FAIR Self-Assessment tool ==

To conduct a human FAIR evaluation for different databases and their respective datasets, different free online tools were considered. Some seemed to be quite useful (e.g. [https://satifyd.dans.knaw.nl/ SATISFYD] ) and some were simple self-evaluation PDFs to be printed and filled out.

We decided to chose the [https://ardc.edu.au/resources/working-with-data/fair-data/fair-self-assessment-tool/ self-assessment tool] provided by the Australian Research Data Commons (ARDC). The decision was based manly upon the fact that the tools questions closely matched the FAIR categories specified by [https://www.doi.org/10.1038/sdata.2016.18 Wilkinson et al., 2016] which would make it easily comparable to the existing preferable FAIR conditions and aid with the repeatability of the results, and the half-automated scoring features that show the extent of the "FAIRness" of the analysed sample as a simple bar chart.

The result of the FAIR assessment is provided as a bar chart where an empty bar chart displays 0 % FAIR compatibility, and a full bar chart represents 100 % FAIR compatibility. The score is therefore not returned as a specific value. During the assessment below, the "Total Across FAIR" in Table ... was estimated according to the level of the bar chart from 0 % to 100 %.
Resulting scores where categorised as follows: Low: < 50%; Medium: 50-60%; High: > 60% .

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ All options for each question of the ARDCs self-assessment tool
!colspan="4"|Findable
!colspan="3"|Accessible
!colspan="3"|Interoperable
!colspan="3"|Reusable
|-
| F1. (meta)data are assigned a globally unique and persistent identifier
|| F2. data are described with rich metadata (defined by R1 below)
|| F3. metadata clearly and explicitly include the identifier of the data it describes
|| F4. (meta)data are registered or indexed in a searchable resource
|| A1. (meta)data are retrievable by their identifier using a standardized communications protocol
|| A1.1 the protocol is open, free, and universally implementable and A1.2 the protocol allows for an authentication and authorization procedure, where necessary
|| A2. metadata are accessible, even when the data are no longer available
|| I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
|| I2. (meta)data use vocabularies that follow FAIR principles
|| I3. (meta)data include qualified references to other (meta)data
|| R1.1. (meta)data are released with a clear and accessible data usage license
|| R1.2. (meta)data are associated with detailed provenance
|| R1.3. (meta)data meet domain-relevant community standards
|-
! Does the dataset have any identifiers assigned?
! Is the dataset identifier included in all metadata records/files describing the data?
! How is the data described with metadata?
! What type of repository or registry is the metadata record in?
! How accessible is the data?
! Is the data available online without requiring specialised protocols or tools once access has been approved?
! Will the metadata record be available even if the data is no longer available?
! What (file) format(s) is the data available in?
! What best describes the types of vocabularies/ontologies/tagging schemas used to define the data elements?
! How is the metadata linked to other data and metadata (to enhance context and clearly indicate relationships)?
! Which of the following best describes the license/usage rights attached to the data?
! How much provenance information has been captured to facilitate data reuse?
!
|-
| Globally unique, citable and persistant (e.g. DOI, PURL, ARK or Handle)
|| Yes
|| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
|| Data is in one place but discoverable through several registries
|| Publicly accessible
|| Standard web service API (e.g. OGC)
|| Yes
|| In a structured, open standard, machine-readable format
|| Standardised open and universal using resolvable global identifiers linking to explainations
|| Meadata is represented in a machine readable format, e.g. in a linked data format such as Resource Description Framework (RDF)
|| Standard machine-readable license (e.g. Creative Commons)
|| Fully recorded in a machine-readable format
||
|-
| Web adress (URL)
|| No
|| Comprehensively, but in a text-based, non-standard format
|| Generalist public repository
|| Fully accessible to person who meet explicitly stated conditions, e.g. ethics approval for sensitive data
|| Non-standard web service (e.g. OpenAPI/Swagger/informal API)
|| No
|| In a structured, open standard, non-machine-readable format
|| Standardised vocabularies/ontologies/schema without global identifiers
|| The metadata record includes URI links to related metadata, data and definitions
|| Standard text based license
|| Fully recorded in a text format
||
|-
| Local identifier
||
|| Brief title and description
|| Domain-specific repository
|| A de-identified / modified subset of the data is publicly accessible
|| File download from online location
|| Unsure
|| Mostly in a proprietary format
|| No standards have been applied in the description of data elements
|| There are no links to other metadata
|| Non-standard machine-readably license (clearly indicating under what conditions the data may be reused)
|| Partially recorded
||
|-
| No identifier
||
|| The data is not described
|| Local institutional repository
|| Embaged access after a specific date
|| By individual arrangement
||
||
|| Data elements not described
||
|| Non-standard text-based license
|| No provenance information is recorded
||
|-
|
||
||
|| The data is not described in any repository
|| Unspecified conditional access e.g. contact the data custodian for access
|| No access to data
||
||
||
||
|| No license
||
||
|-
|
||
||
||
|| Access to metadata only
||
||
||
||
||
||
||
||
|-
|
||
||
||
|| No access to data or metadata
||
||
||
||
||
||
||
||
|}

At this point it is important to note that the assessment of databases and/or datasets using this tool is solely considered a human assessment. This may lead to different evaluations by different users. To counteract this phenomenon, it was suggested that the evaluations should be completed using the 'four-eyes principle' to create more verifiable results. Also, it is planned to additionally evaluate the respective databases using an external tool to perform a machine-assessment.

In addition, not all questions are weighted equally and thus a worse result for one question may not have a large impact whereas only choosing the second-best option in another question will bring down the overall FAIR value considerably.

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ The ARDC tool provides detailed information about the requirements for each of the options of its tool. These descriptions are documented here.
|-
! rowspan="5" | F
|| F
|| About Findable
|| Making data Findable includes assigning a [https://www.ands.org.au/guides/persistent-identifiers-awareness persistent identifier] (like a [https://www.ands.org.au/guides/doi DOI] or [https://www.ands.org.au/guides/identify-my-data Handle] ), having rich [https://www.ands.org.au/working-with-data/metadata metadata] to describe the data and making sure it is findable through disciplinary and generalist discovery portals (local and international).

|-
| F1
|| About identifiers
|| Identifiers are essential for identifying, finding, retrieving, linking and citing datasets. A Web address (URL) can be used to specify the online location of a resource but over time URLs tend to change which leads to broken links to the data. To be useful, identifiers need to be persistent and unique. Digital Object Identifiers (DOIs) and other persistent identifiers (PIDs) provide a permanent citable reference to a particular dataset. The DOI is a permanent fixed reference to the dataset no matter where it is located online and enables citation and citation metrics. Services to create a persistent identifier are often offered by your affiliated institution or the repository you are using to describe your data. Talk to your library service for more information.

Read more: [https://www.ands.org.au/working-with-data/citation-and-identifiers Persistent identifiers]

|-
| F2
|| About identifiers
|| The identifier (preferably a persistent identifier) needs to be clearly stated in the metadata record describing the data collection, and also in any associated data files or metadata.

|-
|F3
|| About metadata descriptions
|| Comprehensive metadata records will include descriptive content that facilitates discovery, access and reuse of the data being described. While there is no 'one size fits all' list, comprehensive metadata should include:
* a globally unique persistent identifier e.g. a DOI
* a title
* related people, i.e. the data creator or custodian
* how to access the data and file formats
* a description of how the data were created and how to interpret the data subject or keywords
* citation information that clearly indicates how the data should be cited
* a machine-readable data licence
* provenance and contextual information such as:
* links to related publications, projects, services and software
* methodology and processes involved in data production
* spatial and temporal coverage (if relevant)
* object-level data description

Providing metadata in a standard schema allows it to be read and used by machines as well as humans.

Read more: [https://www.ands.org.au/working-with-data/metadata Metadata]

|-
| F4
|| About metadata repositories
|| A rich metadata description alone does not ensure a dataset’s ‘findability’ on the internet; the dataset needs to be registered or indexed in a searchable resource, such as a generalist (e.g. [https://figshare.com/ Figshare], [http://datadryad.org/ Dryad], [https://zenodo.org/ Zenodo], domain-specific (e.g. [https://pangaea.de/ PANGAEA], [https://ada.edu.au/ ADA], [https://www.icpsr.umich.edu/ ICPSR]), or institutional (e.g. [https://openresearch-repository.anu.edu.au/ ANU Research Repository] , [https://ses.library.usyd.edu.au/ Sydney eScholarship Repository], [https://data.gov.au/ data.gov.au]) data repository or registry. Ideally these repositories/registries are indexed by search engines such as Google and/or Google Scholar.

|-
! rowspan = "4" | A
| A
|| About Accessible
|| [https://www.ands.org.au/working-with-data/publishing-and-reusing-data/publishing To make data accessible] may include making the data [https://www.ands.org.au/working-with-data/articulating-the-value-of-open-data/open-data open] using a standardised protocol. However the data does not necessarily have to be open. There are sometimes good reasons why data [https://www.ands.org.au/working-with-data/sensitive-data cannot be made open], for example privacy concerns, national security or commercial interests. If it is not open there should be clarity and transparency around the conditions governing access and [https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse reuse].
|-
| A1
|| About data accessibility
|| Not all data that is discoverable can be freely accessed. Often there are embargoes, access controls, and access permissions associated with data due to a variety of issues such as privacy and commercial interests. Even with all these issues much [https://www.ands.org.au/working-with-data/sensitive-data sensitive data] can be shared. Many other issues that could be perceived as blockers to sharing data may be [http://datapub.cdlib.org/closed-data-excuses-excuses overcome].
|-
| A1.1/A1.2
|| About protocols
|| Ideally users would like to retrieve appropriate internet content directly and unhindered once they have located it. Internet protocols (e.g. http and ftp) define rules and conventions for communication between devices, and tools and services are available to facilitate this process, e.g. APIs.

Protocols:
* [https://www.lifewire.com/hypertext-transfer-protocol-817944 HTTP] (Hypertext Transfer Protocol) is the set of rules for transferring files (text, graphic images, sound, video, and other multimedia files) on the [https://whatis.techtarget.com/definition/World-Wide-Web World Wide Web]. As soon as a Web user opens their Web browser, the user is indirectly making use of HTTP.
* [https://www.lifewire.com/file-transfer-protocol-817943 FTP] (File Transfer Protocol) is a standard Internet [http://searchnetworking.techtarget.com/definition/protocol protocol] for transmitting files between computers on the Internet over TCP/IP connections. Read more: [https://www.lifewire.com/definition-of-protocol-network-817949 Web protocols]
* API (Application Programming Interface): When information is made available in any machine readable format, it becomes possible to make that information directly available to programs that request that information over the web. An API is the way this information is made directly available to other machines.
View more: [https://www.youtube.com/watch?v=s7wmiS2mSXY What is an API?]

OGC - http://www.opengeospatial.org/ogc

|-
| A2
|| About metadata availability
|| Effort is often required to maintain data resources online which can often lead to it being neglected especially over long periods of time. This often leads to broken links between the metadata and the data. Having at least a description of the data in the form of a metadata record means there is a record of the data's existence allowing the possibility of someone tracking it down.

|-
! rowspan="4" | I
|| I
|| About Interoperability
|| To be interoperable (i.e. data that is interpretable by a computer, so that they can be automatically combined with other data) the data will need to use community agreed [https://www.ands.org.au/guides/file-formats formats], language and [https://www.ands.org.au/guides/vocabularies-and-research-data vocabularies]. The [https://www.ands.org.au/working-with-data/metadata metadata] will also need to use a community agreed standards and vocabularies, and contain links to related information using [https://www.ands.org.au/working-with-data/citation-and-identifiers identifiers].
|-
|| I1
|| About file formats
|| When selecting file formats for archiving, the formats should ideally be:
* Non-proprietary
* Unencrypted
* Uncompressed
* In common usage by the research community
* Adherent to an open, documented standard, such as described by the State of California (see [http://www.leginfo.ca.gov/pub/07-08/bill/asm/ab_1651-1700/ab_1668_bill_20070524_amended_asm_v96.pdf AB 1668, 2007])
** Interoperable among diverse platforms and applications
** Fully published and available royalty-free
** Fully and independently implementable by multiple software providers on multiple platforms without any intellectual property restrictions for necessary technology
** Developed and maintained by an open standards organization with a well-defined inclusive process for evolution of the standard.

From: [https://library.stanford.edu/research/data-management-services/data-best-practices/best-practices-file-formats Stanford University Library - Best practices for file formats]

File format examples:
* Structured, open standard, machine-readable format e.g. (text) PDF/A, HTML, Plain text, (images) TIFF, JPEG 2000, GIF, (audio) MP3, AIFF, WAVE, (video) MOV, MPEG, AVI, (Tabular data) CSV
* structured, open standard, non-machine-readable format, e.g. PDF, HTML, JPG
* Proprietary format, e.g. doc (Word), .xls (Excel), .ppt (PowerPoint), .sav

Read more:
* [https://www.ands.org.au/__data/assets/pdf_file/0003/731775/File-Formats.pdf ANDS]
* [https://www.europeandataportal.eu/elearning/en/module9/#/id/co-01 European Data Portal]

|-
|| I2
|| About schema standards
|| Schema standards are schemas that have gone through a formal validation process by a standards organisation, such as the International Standards Organisation (ISO) or an equivalent body such as the Dublin Core Metadata Initiative (DCMI); or, commonly used and consistently applied metadata schemas that are well documented, endorsed, and maintained can also become ‘de-facto standards’, e.g. RIF-CS for describing data collections and services used in Research Data Australia.

Read more: [https://www.ands.org.au/guides/metadata-working Metadata schema]

Standardised open and universal schemas, e.g, [https://www.dcc.ac.uk/resources/metadata-standards/datacite-metadata-schema DataCite Metadata Schema], [https://www.dcc.ac.uk/resources/metadata-standards/prov PROV], [https://www.dcc.ac.uk/resources/metadata-standards/dublin-core Dublin Core]

Domain-specific standards, e.g. [http://human-phenotype-ontology.github.io/ HPO - Human Phenotype Ontology], [https://www.nlm.nih.gov/mesh/ MeSH - Medical Subject Headings] , [https://mcp-profile-docs.readthedocs.io/en/stable/ Marine Community Profile], [https://ddialliance.org/ DDI - Data Documentation Initiative].

For more examples of standards: [http://rd-alliance.github.io/metadata-directory/ Here].

Resolvable global identifiers - An identifier is any label used to name something uniquely (whether online or offline). URLs are an example of an identifier. So are serial numbers, and personal names. A persistent identifier is guaranteed to be managed and kept up to date over a defined time period.

Read more: [https://www.ands.org.au/guides/persistent-identifiers-awareness Persistent Identifiers]

|-
||I3
|| About metadata links
|| The goal is to create as many meaningful linkages as possible between (meta)data resources to enrich the contextual knowledge about the data, balanced against the time/energy involved in making a good data model.

'''Machine-readable (meta)data'''

(Meta)data in a format that can be automatically read and processed by a computer, such as [http://opendatahandbook.org/glossary/en/terms/csv/ CSV], [http://opendatahandbook.org/glossary/en/terms/json/ JSON], [http://opendatahandbook.org/glossary/en/terms/xml/ XML] etc. For more information: [http://opendatahandbook.org/glossary/en/terms/machine-readable/ Open Data Handbook]

'''Linked Data'''

Linked Data (also known as Linking Data) can be applied to improve the exploitation of the “Web of data.” The expression refers to the publishing of structured data in a way that typed links are created between data from different sources to provide a higher level of usability. By using Linked Data, it is possible to find other, related data. Structured data should meet four requirements to be called
Linked Data:

* URIs should be assigned to all entities of the dataset.
* HTTP URIs are required to ensure that all entities can be referenced and cited by users and user agents.
* Entities should be described using standard formats such as RDF/XML.
* Links should be created to other, related entity URIs.

All data that fulfil these requirements and are released for the public are called Linked Open Data (LOD). http://www.lesliesikos.com/semantic-web-machine-readable-structured-data-with-meaningful-annotations/

Read more under the heading (meta)data include qualified references to other (meta)data in the following resource: https://www.dtls.nl/fair-data/fair-principles-explained/

For more information on Linked Data standards - RDF - https://www.w3.org/RDF/

Other related linked data standards:
* OWL - https://www.w3.org/OWL/
* SKOS - https://www.w3.org/2004/02/skos/
* SPARQL - https://www.w3.org/TR/rdf-sparql-query/

|-
! rowspan="3" | R
|| R
|| About Reusable
|| Reusable data should maintain its initial richness. For example, it should not be abridged for the purpose of explaining the findings in one particular publication. It needs a clear machine-readable [https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse licence] and [https://www.ands.org.au/partners-and-communities/ands-communities/data-provenance-interest-group provenance] information on how the data was formed. It should also use discipline-specific data and [https://www.ands.org.au/working-with-data/metadata metadata] standards to give it rich contextual information that will allow for accurate interpretation and reuse.

|-
|| R1.1
|| About licenses
|| If data is not licensed no-one else can use it. In Australia, no licence is regarded as the same as 'all rights reserved', confining any reuse to very limited circumstances. Applying a Creative Commons licence to your data is a simple way to ensure that your data can be reused. The less restrictive the licence, the more that can be done with the data.
To make it easy for the Web to know when a work is available under a particular license, a “machine readable” version of the license provides a summary of the key freedoms and obligations written into a format that software systems, search engines, and other kinds of technology can understand.
Example from the [https://creativecommons.org/choose/ Creative Commons License Choser]:

What appears in the metadata:

This work is licensed under a [https://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International License].

Code snippet:

<code>
<a rel="license" href="http://creativecommons.org/licenses/by/4.0/">

<img alt="Creative Commons License" style="border-width:0"

src="https://i.creativecommons.org/l/by/4.0/88x31.png"/>

</a> This work is licensed under a

<a rel="license" href="http://creativecommons.org/licenses/by/4.0/">

Creative Commons Attribution 4.0 International License</a>.
<\code>

Read more: https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse

|-
||R1.2
|| About provenance
|| Data provenance is used to document where a piece of data comes from and the process and methodology by which it is produced. It is important to confirm the authenticity of data enabling trust, credibility and reproducibility. This is becoming increasingly important, especially in the eScience community where research is data intensive and often involves complex data transformations and procedures ([https://www.ands.org.au/partners-and-communities/ands-communities/data-provenance-interest-group Read more]). Provenance vocabularies such as [https://www.w3.org/2005/Incubator/prov/XGR-prov-20101214/#Recommendations Provenir Ontology (PROV-O) and others].

|}

== Results for SMILES and ShareWind ==

{| class="mw-collapsible wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Datasets from the SMILES and ShareWind platforms was analysed using the self-assessment tool provided by ARDC.
! rowspan="5" |
| rowspan="5" |
! Database Code
| rowspan="24" style="background: #ABE" |
| ED4
| ED9
|-
! Database Title
|| SMILES
| ShareWind
|-
! Database Link
|| https://www.ecria-smiles.eu/
| https://sharewind.eu/
|-
! Sample Link
|| [https://www.ecria-smiles.eu/data-files/-/document_library/W32cCAfL2jMX/view_file664466?_com_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX_redirect=https%3A%2F%2Fwww.ecria-smiles.eu%3A443%2Fdata-files%2F-%2Fdocument_library%2FW32cCAfL2jMX%2Fview%2F664423%3F_com_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX_redirect%3Dhttps%253A%252F%252Fwww.ecria-smiles.eu%253A443%252Fdata-files%253Fp_p_id%253Dcom_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX%2526p_p_lifecycle%253D0%2526p_p_state%253Dnormal%2526p_p_mode%253Dview here] (link has 585 characters)
| https://sharewind.eu/records/f9d31abbc2824284b8c1d28894d9619a (data found at https://zenodo.org/record/1162738)
|-
! Sample Size
|| 4 KB
| 37 MB
|-
| colspan="6" style="background: #ABE" |
|-
! rowspan="4" | Findable
| F1. (meta)data are assigned a globally unique and persistent identifier
! Does the dataset have any identifiers assigned?
|| Web adress (URL)
| Globally unique, citable and persistant (e.g. DOI, PURL, ARK or Handle)
|-

| F2. data are described with rich metadata (defined by R1 below)
! Is the dataset identifier included in all metadata records/files describing the data?
|| No
| No
|-

| F3. metadata clearly and explicitly include the identifier of the data it describes
! How is the data described with metadata?
|| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
|-

| F4. (meta)data are registered or indexed in a searchable resource
! What type of repository or registry is the metadata record in?
|| Domain-specific repository
| Data is in one place but discoverable through several registries
|-

! rowspan="3" | Accessible

| A1. (meta)data are retrievable by their identifier using a standardized communications protocol
! How accessible is the data?
|| Publicly accessible
| Publicly accessible
|-
| A1.1 the protocol is open, free, and universally implementable; A1.2 the protocol allows for an authentication and authorization procedure, where necessary
! Is the data available online without requiring specialised protocols or tools once access has been approved?
|| File download from online location
| Standard web service API (e.g. OGC)
|-
| A2. metadata are accessible, even when the data are no longer available
! Will the metadata record be available even if the data is no longer available?
|| Unsure
| Yes
|-

! rowspan="3" | Interoperable
| I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
! What (file) format(s) is the data available in?
|| In a structured, open standard, machine-readable format
|| In a structured, open standard, non-machine-readable format
|-
| I2. (meta)data use vocabularies that follow FAIR principles
! What best describes the types of vocabularies/ontologies/tagging schemas used to define the data elements?
|| Standardised vocabularies/ontologies/schema without global identifiers
|| Standardised vocabularies/ontologies/schema without global identifiers
|-
| I3. (meta)data include qualified references to other (meta)data
! How is the metadata linked to other data and metadata (to enhance context and clearly indicate relationships)?
|| There are no links to other metadata
|| Metadata is represented in a machine readable format, e.g. in a linked data format such as Resource Description Framework (RDF)
|-

! rowspan="3" | Reusable
| R1.1. (meta)data are released with a clear and accessible data usage license
! Which of the following best describes the license/usage rights attached to the data?
|| No license
| Standard machine-readable license (e.g. Creative Commons)
|-
| R1.2. (meta)data are associated with detailed provenance
! How much provenance information has been captured to facilitate data reuse?
|| Partially recorded
|| Partially recorded
|-
| R1.3. (meta)data meet domain-relevant community standards
|
|
|
|-
| colspan="6" style="background: #ABE" |
|-
! Total across F.A.I.R
|
! Low: < 50%; Medium: 50-60%; High: > 60%
|| ~ 45 %
|| ~ 85 %
|-
| colspan="6" style="background: #ABE" |
|-
! Remarks about database
||
||
||
|| The data is provided by the open-source platform Zenodo, not the project website itself. A very positive aspect is that information on different versions of the data as well as the number of views and downloads is available.
|-
! Identified gaps
||
||
||
* No DOI, only URL
* Extremely long URL link spanning several lines with 585 characters, that can easily become corrupted
* No links to other metadata
* No license information given
* Provenance not fully recorded

||
* Dataset identifiers should be included in the files describing the data
* Provenance details are only partially recorded with a note with contact name for further information
|}

= Metadata assessment =

== Dublin Core Elements ==
{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ The DCMI Metadata Terms lists the current set of the Dublin Core vocabulary. Source: [https://dublincore.org/specifications/dublin-core/dcmi-terms/ Dublin Core Metadata Initiative]
| 1 || abstract || 11 || contributor || 21 || extent || 31 || isReplacedBy || 41 || publisher || 51 || tableOfContents
|-
| 2 || accessRights || 12 || coverage || 22 || format || 32 || isRequiredBy || 42 || references || 52 || temporal
|-
| 3 || accrualMethod || 13 || created || 23 || hasFormat || 33 || issued || 43 || relation || 53 || title
|-
| 4 || accrualPeriodicity || 14 || creator || 24 || hasPart || 34 || isVersionOf || 44 || replaces || 54 || type
|-
| 5 || accrualPolicy || 15 || date || 25 || hasVersion || 35 || language || 45 || requires || 55 || valid
|-
| 6 || alternative || 16 || dateAccepted || 26 || identifier || 36 || license || 46 || rights || ||
|-
| 7 || audience || 17 || dateCopyrighted || 27 || instructionalMethod || 37 || mediator || 47 || rightsHolder || ||
|-
| 8 || available || 18 || dateSubmitted || 28 || isFormatOf || 38 || medium || 48 || source || ||
|-
| 9 || bibliographicCitation || 19 || description || 29 || isPartOf || 39 || modified || 49 || spatial || ||
|-
| 10 || conformsTo || 20 || educationLevel || 30 || isReferencedBy || 40 || provenance || 50 || subject || ||
|}

'''Metadata Category Types according to [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO]'''

There are three main types of metadata:
* Descriptive metadata describes a resource for purposes such as discovery and identification. It can include elements such as title, abstract, author, and keywords.
* Structural metadata indicates how compound objects are put together, for example, how pages are ordered to form chapters.
* Administrative metadata provides information to help manage a resource, such as when and how it was created, file type and other technical information, and who can access it. There are several subsets of administrative data; two that sometimes are listed as separate metadata types are:
** Rights management meta-data, which deals with intellectual property rights,and
** Preservation metadata, which contains information needed to archive and preserve a resource.
''

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Simple Dublin Core Elements. 15 DC elements with their (shortened) official definitions and examples.
! DC element name !! DC definition ([https://www.dublincore.org/specifications/dublin-core/dces/ DCMI]) !! Example (Energy Domain) !! Category (determined with the aid of [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO])
|-
! Title
| A name given to the resource || Outdoor monitoring of a hybrid micro-CPV solar panel with integrated micro-tracking and diffuse capture || Descriptive
|-
! Subject
| The topic of the resource || concentrator photovoltaics; CPV; rooftop photovoltaics; integrated tracking || Descriptive
|-
! Description
| An account of the resource || Dataset from the outdoor characterization of a B Series module from Insolight at the rooftop of the Instituto de Energía Solar - Universidad Politécnica de Madrid. …. || Descriptive
|-
! Creator
| An entity primarily responsible for making the resource || Stephen Askins; Gaël Nardin || Descriptive
|-
! Publisher
| An entity responsible for making the resource available || Stephen Askins || Descriptive
|-
! Contributor
| An entity responsible for making contributions to the resource || Stephen Askins || Descriptive
|-
! Date
| A point or period of time associated with an event in the lifecycle of the resource || 2019-07-23; Date will be associated with the creation or availability of the resource. || Administrative
|-
! Type
| The nature or genre of the resource || info:eu-repo/semantics/other; dataset || Descriptive
|-
! Format
| The file format, physical medium, or dimensions of the resource || image
|| Structural
|-
! Identifier
| An unambiguous reference to the resource within a given context || https://zenodo.org/record/3346823; 10.5281/zenodo.3346823; oai:zenodo.org:3346823 || Descriptive
|-
! Source
| A related resource from which the described resource is derived || RC607.A26W574 1996 [where "RC607.A26W574 1996" is the call number of the print version of the resource, from which the present version was scanned] || Descriptive
|-
! Language
| A language of the resource || eng || Descriptive
|-
! Relation
| A related resource || info:eu-repo/grantAgreement/EC/H2020/787289/ || Descriptive
|-
! Coverage
| The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant || 1995-1996; Madrid, Spain || Descriptive
|-
! Rights
| Information about rights held in and over the resource || info:eu-repo/semantics/openAccess; https://creativecommons.org/licenses/by/4.0/legalcode || Administrative
|}

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Non-DC elements

! Non-DC element name !! Definition !! Example !! Category
|-
! Date of availability
| Since when is the data available
||
|| Impact/Exploitation

|-
! Number of users
| Is there any information on the number of accesses or users?
||
|| Impact/Exploitation

|-
! Number of scientific publications where data is cited/used
| Is there any information on the number of citations/uses?
||
|| Impact/Exploitation

|-
! Curation activities (is the versioning ongoing)
| Update, maintainance
||
|| Impact/Exploitation
|}

Attempting to put a value on Impact/Exploitation:
* '''ENTSO-E''': Found 172 results in Advanced Search (ALL) in Web of Science ([https://apps.webofknowledge.com/ WoSc]) (16/06/2020). Citations of all these search result add up to 714. This however does not mean, that all search results really explicitly used data from ENTSO-E. Some (for sure, for some were found) only mentioned it saying they adoped workflow etc. from the site.
* '''SMARD''': Found 82 results in Advanced Search (ALL=(SMARD)) in Web of Science ([https://apps.webofknowledge.com/ WoSc]) (16/06/2020). However: SMARD is also an acronym for ''Spinal muscular atrophy with respiratory distress (SMARD)''. This somewhat complicates results and needs some further discussion. All 82 search results accumumlated 2359 citations themselves.

== Results for SMILES ==

{| class="wikitable mw-collapsible"
! DC element name
! DC definition ([https://www.dublincore.org/specifications/dublin-core/dces/ DCMI])
! Category (determined with the aid of [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO])
! rowspan = "16" style="background: #ABE" |
! SMILES (ED4)
! Notes

|-
! Title
| A name given to the resource
|| Descriptive
|| A
||
|-
! Subject
| The topic of the resource || Descriptive || A ||
|-
! Description
| An account of the resource || Descriptive || PA || The information given is very limited
|-
! Creator
| An entity primarily responsible for making the resource || Descriptive || A || The metadata file uses the tag "author"
|-
! Publisher
| An entity responsible for making the resource available || Descriptive || A || This information only available under the URL but not in the metadata file
|-
! Contributor
| An entity responsible for making contributions to the resource || Descriptive || NA ||
|-
! Date
| A point or period of time associated with an event in the lifecycle of the resource || Administrative || A || This information only available under the URL but not in the metadata file
|-
! Type
| The nature or genre of the resource || Descriptive || NA || No explicit mention of the type although it could be derived from the context
|-
! Format
| The file format, physical medium, or dimensions of the resource || Structural || PA || The metadata file uses the tag "datatype" and "file"
|-
! Identifier
| An unambiguous reference to the resource within a given context || Descriptive || NA ||
|-
! Source
| A related resource from which the described resource is derived || Descriptive || NA ||
|-
! Language
| A language of the resource || Descriptive || NA || No explicit mention of the language although it could be derived from the context
|-
! Relation
| A related resource || Descriptive || NA || This information (EU grant information) only generally available under the URL but not in the metadata file
|-
! Coverage
| The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant || Descriptive || NA ||
|-
! Rights
| Information about rights held in and over the resource || Administrative || NA ||
|}

Gap analysis

2020-08-28T12:23:49Z

Christopherbs: /* Results for SMILES */

<accesscontrol>Administrators,consortium</accesscontrol>

This is the page for the gap analysis.

= Tacit knowledge "FAIRification and opening of low carbon energy research data" =
 Consortium members can add any time, they feel that something is important to note even though it is not mentioned in a standard deliverable. In other words, this page collects uncodified, tacit knowledge. It will help us later to compile suggestions and lessons learned. This kind of knowledge is collected two-fold:
#Anytime if someone feels that this should be noted. Please write down: Issue, Date, Author (could also be "anonymous"), the issue described in a few words or maximal lines>
#During the final day of workshops

=== Learning process: ===

*People are hesitant to adopt new IT technologies, this is even the case among researchers heavily relying on data, algorithms and collaborative online software (e.g., R, platforms, online conferencing, HPC, ...). The effort to encourage change is not to underestimate. Reasons are several, notably, lack of time and uncertainty about potential benefit as well as overall risk aversion preferences. EERAdata is using the online software "Only Office" to facilitate collaboration (in particular also during the Covid-19 period).
 '''FAIR and open criteria:'''
*Consortium members have a fair understanding of what FAIR/O is, but there is little knowledge and/or technical experience on how to approach the FAIRification and opening. All, however, share the view that we are at a critical point in time, where we need to implement these criteria. 
*To deepen knowledge about FAIR/O criteria, it is useful to test the criteria on a database one is familiar with. For this purpose (and to start brainstorming about the platform), AIT has developed a questionnaire for application in the use cases.
'''Metadata:'''
*A good starting point is to think about metadata and to look into existing metadata concepts in one's field. The first step is to understand that also metadata need to adhere to the FAIR/O principles.
*The next step is to increase knowledge on IT specific terms, i.e. to understand what the difference is between different '''[https://energydata.onlyoffice.eu/Products/Files/doceditor.aspx?fileid=5404340 metadata frameworks] (taxonomy, thesaurus, ontology) as well as classification of metadata (high-level, medium-level, low-level OR administrative, structural and descriptive metadata).  '''WP 2 being in charge of aligning approaches between use cases, participates in all use case kickoffs to bring everybody on the same page. The presentation is linked with "metadata frameworks".
*It is useful to supply consortium members with read aheads and watch aheads on metadata to prepare the '''[https://energydata.onlyoffice.eu/Products/Files/doceditor.aspx?fileid=5296590 first EERAdata workshop]'''. The workshop starts applications and discussions in the use cases break out sessions (and bringing insights back to the plenary), using selected databases.

= FAIR Assessment=

== FAIR Guiding Principles ==
{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ This table shows the FAIR guiding principles described by [https://www.doi.org/10.1038/sdata.2016.18 Wilkinson et al., 2016].

! To be Findable
|
* F1. (meta)data are assigned a globally unique and persistent identifier
* F2. data are described with rich metadata (defined by R1 below)
* F3. metadata clearly and explicitly include the identifier of the data it describes
* F4. (meta)data are registered or indexed in a searchable resource

|-
! To be Accessible
|
* A1. (meta)data are retrievable by their identifier using a standardized communications protocol
** A1.1 the protocol is open, free, and universally implementable
** A1.2 the protocol allows for an authentication and authorization procedure, where necessary
* A2. metadata are accessible, even when the data are no longer available

|-
! To be Interoperable
|
* I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
* I2. (meta)data use vocabularies that follow FAIR principles
* I3. (meta)data include qualified references to other (meta)data

|-
! To be Reusable
|
* R1. meta(data) are richly described with a plurality of accurate and relevant attributes
* R1.1. (meta)data are released with a clear and accessible data usage license
* R1.2. (meta)data are associated with detailed provenance
* R1.3. (meta)data meet domain-relevant community standards

|}

== Identified gaps after Workshop 1 (02/06/2020 – 04/06/2020) ==

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ This table shows a summary of the specific issues that were identified in Workshop 1. The aim was to categorise different issues to get a better understanding of how to tackle these challenges.
! FAIR/O !! Specific FAIR/O !! Gaps !! Gaps for energy domain !! Use case specific gaps !! Consequences for researchers
|-
! F
| F2
|| Metadata scope
|| Different fields require different metadata (potentially very specific)
|| UC3: lack of additional metadata for applications of materials
|| Data are less useful for the specific field
|-
! F
|| F1/F3
|| Identifiers
||
* Missing identifiers on websites
* Identifier not in downloaded data files
||
|| Makes research more difficult.
|-
! I
| I2
|| Taxonomy/ontology/common vocabulary and language issues
||
* Lack of standardisation
* Heterogeneous data makes standardisation hard
* No vocabulary documentation on websites
* Words used for same term in other languages may differ
* Databases in other languages than English
||
|| Research costs more time
|-
! I
| I3
|| References to other (meta)data
||
* No linking to source documents and related publications (for contextual knowledge)
* Possible need for links to other fields
|| UC3: link between microscopic and macroscopic materials (e.g., turbine blades)
||
* Data cannot be connected to the source
* Makes research more difficult.

|-
! R
| R1.1
|| Licensing
||
* No licenses available may mean the data is not reusable for the researcher
* Licenses not clear and accessible
* Obtaining licenses may result in more effort and costs
||
|| Data potentially not reusable

|-
! R
| R1.2
|| Reliability and provenance of data
|| When the data is collected from different and high number of sources, its reliability decreases
||
|| Data potentially not reusable

|-
! O
|
|| Privacy concerns and expected disadvantages
|| Not publishing data due to privacy concerns (sensitive data)
|| UC2: In case of distribution network data this is relevant
|| Data not accessible

|-
! Other
|
|| Conducting FAIR assessments
||
* No qualitative assessment of the data itself (which may be limited)
* Discrepancies between results conducted by humans versus machines
* Sometimes not even DC standards are met
* Metadata is not updated
* Lacking encryption of websites (https)
* Problems assessing whether metadata will be available after data is unavailable
* Interface design/layout may be unclear, incomplete or not intuitive for humans
* Authentication details (user registration login / good or bad, clarify)

||
* UC1 lack of data availability for time-series
||
* Assessment is uncertain (human assessment may need more clarification and understanding)
* Makes research more time-intensive

|}

== FAIR Self-Assessment tool ==

To conduct a human FAIR evaluation for different databases and their respective datasets, different free online tools were considered. Some seemed to be quite useful (e.g. [https://satifyd.dans.knaw.nl/ SATISFYD] ) and some were simple self-evaluation PDFs to be printed and filled out.

We decided to chose the [https://ardc.edu.au/resources/working-with-data/fair-data/fair-self-assessment-tool/ self-assessment tool] provided by the Australian Research Data Commons (ARDC). The decision was based manly upon the fact that the tools questions closely matched the FAIR categories specified by [https://www.doi.org/10.1038/sdata.2016.18 Wilkinson et al., 2016] which would make it easily comparable to the existing preferable FAIR conditions and aid with the repeatability of the results, and the half-automated scoring features that show the extent of the "FAIRness" of the analysed sample as a simple bar chart.

The result of the FAIR assessment is provided as a bar chart where an empty bar chart displays 0 % FAIR compatibility, and a full bar chart represents 100 % FAIR compatibility. The score is therefore not returned as a specific value. During the assessment below, the "Total Across FAIR" in Table ... was estimated according to the level of the bar chart from 0 % to 100 %.
Resulting scores where categorised as follows: Low: < 50%; Medium: 50-60%; High: > 60% .

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ All options for each question of the ARDCs self-assessment tool
!colspan="4"|Findable
!colspan="3"|Accessible
!colspan="3"|Interoperable
!colspan="3"|Reusable
|-
| F1. (meta)data are assigned a globally unique and persistent identifier
|| F2. data are described with rich metadata (defined by R1 below)
|| F3. metadata clearly and explicitly include the identifier of the data it describes
|| F4. (meta)data are registered or indexed in a searchable resource
|| A1. (meta)data are retrievable by their identifier using a standardized communications protocol
|| A1.1 the protocol is open, free, and universally implementable and A1.2 the protocol allows for an authentication and authorization procedure, where necessary
|| A2. metadata are accessible, even when the data are no longer available
|| I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
|| I2. (meta)data use vocabularies that follow FAIR principles
|| I3. (meta)data include qualified references to other (meta)data
|| R1.1. (meta)data are released with a clear and accessible data usage license
|| R1.2. (meta)data are associated with detailed provenance
|| R1.3. (meta)data meet domain-relevant community standards
|-
! Does the dataset have any identifiers assigned?
! Is the dataset identifier included in all metadata records/files describing the data?
! How is the data described with metadata?
! What type of repository or registry is the metadata record in?
! How accessible is the data?
! Is the data available online without requiring specialised protocols or tools once access has been approved?
! Will the metadata record be available even if the data is no longer available?
! What (file) format(s) is the data available in?
! What best describes the types of vocabularies/ontologies/tagging schemas used to define the data elements?
! How is the metadata linked to other data and metadata (to enhance context and clearly indicate relationships)?
! Which of the following best describes the license/usage rights attached to the data?
! How much provenance information has been captured to facilitate data reuse?
!
|-
| Globally unique, citable and persistant (e.g. DOI, PURL, ARK or Handle)
|| Yes
|| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
|| Data is in one place but discoverable through several registries
|| Publicly accessible
|| Standard web service API (e.g. OGC)
|| Yes
|| In a structured, open standard, machine-readable format
|| Standardised open and universal using resolvable global identifiers linking to explainations
|| Meadata is represented in a machine readable format, e.g. in a linked data format such as Resource Description Framework (RDF)
|| Standard machine-readable license (e.g. Creative Commons)
|| Fully recorded in a machine-readable format
||
|-
| Web adress (URL)
|| No
|| Comprehensively, but in a text-based, non-standard format
|| Generalist public repository
|| Fully accessible to person who meet explicitly stated conditions, e.g. ethics approval for sensitive data
|| Non-standard web service (e.g. OpenAPI/Swagger/informal API)
|| No
|| In a structured, open standard, non-machine-readable format
|| Standardised vocabularies/ontologies/schema without global identifiers
|| The metadata record includes URI links to related metadata, data and definitions
|| Standard text based license
|| Fully recorded in a text format
||
|-
| Local identifier
||
|| Brief title and description
|| Domain-specific repository
|| A de-identified / modified subset of the data is publicly accessible
|| File download from online location
|| Unsure
|| Mostly in a proprietary format
|| No standards have been applied in the description of data elements
|| There are no links to other metadata
|| Non-standard machine-readably license (clearly indicating under what conditions the data may be reused)
|| Partially recorded
||
|-
| No identifier
||
|| The data is not described
|| Local institutional repository
|| Embaged access after a specific date
|| By individual arrangement
||
||
|| Data elements not described
||
|| Non-standard text-based license
|| No provenance information is recorded
||
|-
|
||
||
|| The data is not described in any repository
|| Unspecified conditional access e.g. contact the data custodian for access
|| No access to data
||
||
||
||
|| No license
||
||
|-
|
||
||
||
|| Access to metadata only
||
||
||
||
||
||
||
||
|-
|
||
||
||
|| No access to data or metadata
||
||
||
||
||
||
||
||
|}

At this point it is important to note that the assessment of databases and/or datasets using this tool is solely considered a human assessment. This may lead to different evaluations by different users. To counteract this phenomenon, it was suggested that the evaluations should be completed using the 'four-eyes principle' to create more verifiable results. Also, it is planned to additionally evaluate the respective databases using an external tool to perform a machine-assessment.

In addition, not all questions are weighted equally and thus a worse result for one question may not have a large impact whereas only choosing the second-best option in another question will bring down the overall FAIR value considerably.

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ The ARDC tool provides detailed information about the requirements for each of the options of its tool. These descriptions are documented here.
|-
! rowspan="5" | F
|| F
|| About Findable
|| Making data Findable includes assigning a [https://www.ands.org.au/guides/persistent-identifiers-awareness persistent identifier] (like a [https://www.ands.org.au/guides/doi DOI] or [https://www.ands.org.au/guides/identify-my-data Handle] ), having rich [https://www.ands.org.au/working-with-data/metadata metadata] to describe the data and making sure it is findable through disciplinary and generalist discovery portals (local and international).

|-
| F1
|| About identifiers
|| Identifiers are essential for identifying, finding, retrieving, linking and citing datasets. A Web address (URL) can be used to specify the online location of a resource but over time URLs tend to change which leads to broken links to the data. To be useful, identifiers need to be persistent and unique. Digital Object Identifiers (DOIs) and other persistent identifiers (PIDs) provide a permanent citable reference to a particular dataset. The DOI is a permanent fixed reference to the dataset no matter where it is located online and enables citation and citation metrics. Services to create a persistent identifier are often offered by your affiliated institution or the repository you are using to describe your data. Talk to your library service for more information.

Read more: [https://www.ands.org.au/working-with-data/citation-and-identifiers Persistent identifiers]

|-
| F2
|| About identifiers
|| The identifier (preferably a persistent identifier) needs to be clearly stated in the metadata record describing the data collection, and also in any associated data files or metadata.

|-
|F3
|| About metadata descriptions
|| Comprehensive metadata records will include descriptive content that facilitates discovery, access and reuse of the data being described. While there is no 'one size fits all' list, comprehensive metadata should include:
* a globally unique persistent identifier e.g. a DOI
* a title
* related people, i.e. the data creator or custodian
* how to access the data and file formats
* a description of how the data were created and how to interpret the data subject or keywords
* citation information that clearly indicates how the data should be cited
* a machine-readable data licence
* provenance and contextual information such as:
* links to related publications, projects, services and software
* methodology and processes involved in data production
* spatial and temporal coverage (if relevant)
* object-level data description

Providing metadata in a standard schema allows it to be read and used by machines as well as humans.

Read more: [https://www.ands.org.au/working-with-data/metadata Metadata]

|-
| F4
|| About metadata repositories
|| A rich metadata description alone does not ensure a dataset’s ‘findability’ on the internet; the dataset needs to be registered or indexed in a searchable resource, such as a generalist (e.g. [https://figshare.com/ Figshare], [http://datadryad.org/ Dryad], [https://zenodo.org/ Zenodo], domain-specific (e.g. [https://pangaea.de/ PANGAEA], [https://ada.edu.au/ ADA], [https://www.icpsr.umich.edu/ ICPSR]), or institutional (e.g. [https://openresearch-repository.anu.edu.au/ ANU Research Repository] , [https://ses.library.usyd.edu.au/ Sydney eScholarship Repository], [https://data.gov.au/ data.gov.au]) data repository or registry. Ideally these repositories/registries are indexed by search engines such as Google and/or Google Scholar.

|-
! rowspan = "4" | A
| A
|| About Accessible
|| [https://www.ands.org.au/working-with-data/publishing-and-reusing-data/publishing To make data accessible] may include making the data [https://www.ands.org.au/working-with-data/articulating-the-value-of-open-data/open-data open] using a standardised protocol. However the data does not necessarily have to be open. There are sometimes good reasons why data [https://www.ands.org.au/working-with-data/sensitive-data cannot be made open], for example privacy concerns, national security or commercial interests. If it is not open there should be clarity and transparency around the conditions governing access and [https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse reuse].
|-
| A1
|| About data accessibility
|| Not all data that is discoverable can be freely accessed. Often there are embargoes, access controls, and access permissions associated with data due to a variety of issues such as privacy and commercial interests. Even with all these issues much [https://www.ands.org.au/working-with-data/sensitive-data sensitive data] can be shared. Many other issues that could be perceived as blockers to sharing data may be [http://datapub.cdlib.org/closed-data-excuses-excuses overcome].
|-
| A1.1/A1.2
|| About protocols
|| Ideally users would like to retrieve appropriate internet content directly and unhindered once they have located it. Internet protocols (e.g. http and ftp) define rules and conventions for communication between devices, and tools and services are available to facilitate this process, e.g. APIs.

Protocols:
* [https://www.lifewire.com/hypertext-transfer-protocol-817944 HTTP] (Hypertext Transfer Protocol) is the set of rules for transferring files (text, graphic images, sound, video, and other multimedia files) on the [https://whatis.techtarget.com/definition/World-Wide-Web World Wide Web]. As soon as a Web user opens their Web browser, the user is indirectly making use of HTTP.
* [https://www.lifewire.com/file-transfer-protocol-817943 FTP] (File Transfer Protocol) is a standard Internet [http://searchnetworking.techtarget.com/definition/protocol protocol] for transmitting files between computers on the Internet over TCP/IP connections. Read more: [https://www.lifewire.com/definition-of-protocol-network-817949 Web protocols]
* API (Application Programming Interface): When information is made available in any machine readable format, it becomes possible to make that information directly available to programs that request that information over the web. An API is the way this information is made directly available to other machines.
View more: [https://www.youtube.com/watch?v=s7wmiS2mSXY What is an API?]

OGC - http://www.opengeospatial.org/ogc

|-
| A2
|| About metadata availability
|| Effort is often required to maintain data resources online which can often lead to it being neglected especially over long periods of time. This often leads to broken links between the metadata and the data. Having at least a description of the data in the form of a metadata record means there is a record of the data's existence allowing the possibility of someone tracking it down.

|-
! rowspan="4" | I
|| I
|| About Interoperability
|| To be interoperable (i.e. data that is interpretable by a computer, so that they can be automatically combined with other data) the data will need to use community agreed [https://www.ands.org.au/guides/file-formats formats], language and [https://www.ands.org.au/guides/vocabularies-and-research-data vocabularies]. The [https://www.ands.org.au/working-with-data/metadata metadata] will also need to use a community agreed standards and vocabularies, and contain links to related information using [https://www.ands.org.au/working-with-data/citation-and-identifiers identifiers].
|-
|| I1
|| About file formats
|| When selecting file formats for archiving, the formats should ideally be:
* Non-proprietary
* Unencrypted
* Uncompressed
* In common usage by the research community
* Adherent to an open, documented standard, such as described by the State of California (see [http://www.leginfo.ca.gov/pub/07-08/bill/asm/ab_1651-1700/ab_1668_bill_20070524_amended_asm_v96.pdf AB 1668, 2007])
** Interoperable among diverse platforms and applications
** Fully published and available royalty-free
** Fully and independently implementable by multiple software providers on multiple platforms without any intellectual property restrictions for necessary technology
** Developed and maintained by an open standards organization with a well-defined inclusive process for evolution of the standard.

From: [https://library.stanford.edu/research/data-management-services/data-best-practices/best-practices-file-formats Stanford University Library - Best practices for file formats]

File format examples:
* Structured, open standard, machine-readable format e.g. (text) PDF/A, HTML, Plain text, (images) TIFF, JPEG 2000, GIF, (audio) MP3, AIFF, WAVE, (video) MOV, MPEG, AVI, (Tabular data) CSV
* structured, open standard, non-machine-readable format, e.g. PDF, HTML, JPG
* Proprietary format, e.g. doc (Word), .xls (Excel), .ppt (PowerPoint), .sav

Read more:
* [https://www.ands.org.au/__data/assets/pdf_file/0003/731775/File-Formats.pdf ANDS]
* [https://www.europeandataportal.eu/elearning/en/module9/#/id/co-01 European Data Portal]

|-
|| I2
|| About schema standards
|| Schema standards are schemas that have gone through a formal validation process by a standards organisation, such as the International Standards Organisation (ISO) or an equivalent body such as the Dublin Core Metadata Initiative (DCMI); or, commonly used and consistently applied metadata schemas that are well documented, endorsed, and maintained can also become ‘de-facto standards’, e.g. RIF-CS for describing data collections and services used in Research Data Australia.

Read more: [https://www.ands.org.au/guides/metadata-working Metadata schema]

Standardised open and universal schemas, e.g, [https://www.dcc.ac.uk/resources/metadata-standards/datacite-metadata-schema DataCite Metadata Schema], [https://www.dcc.ac.uk/resources/metadata-standards/prov PROV], [https://www.dcc.ac.uk/resources/metadata-standards/dublin-core Dublin Core]

Domain-specific standards, e.g. [http://human-phenotype-ontology.github.io/ HPO - Human Phenotype Ontology], [https://www.nlm.nih.gov/mesh/ MeSH - Medical Subject Headings] , [https://mcp-profile-docs.readthedocs.io/en/stable/ Marine Community Profile], [https://ddialliance.org/ DDI - Data Documentation Initiative].

For more examples of standards: [http://rd-alliance.github.io/metadata-directory/ Here].

Resolvable global identifiers - An identifier is any label used to name something uniquely (whether online or offline). URLs are an example of an identifier. So are serial numbers, and personal names. A persistent identifier is guaranteed to be managed and kept up to date over a defined time period.

Read more: [https://www.ands.org.au/guides/persistent-identifiers-awareness Persistent Identifiers]

|-
||I3
|| About metadata links
|| The goal is to create as many meaningful linkages as possible between (meta)data resources to enrich the contextual knowledge about the data, balanced against the time/energy involved in making a good data model.

'''Machine-readable (meta)data'''

(Meta)data in a format that can be automatically read and processed by a computer, such as [http://opendatahandbook.org/glossary/en/terms/csv/ CSV], [http://opendatahandbook.org/glossary/en/terms/json/ JSON], [http://opendatahandbook.org/glossary/en/terms/xml/ XML] etc. For more information: [http://opendatahandbook.org/glossary/en/terms/machine-readable/ Open Data Handbook]

'''Linked Data'''

Linked Data (also known as Linking Data) can be applied to improve the exploitation of the “Web of data.” The expression refers to the publishing of structured data in a way that typed links are created between data from different sources to provide a higher level of usability. By using Linked Data, it is possible to find other, related data. Structured data should meet four requirements to be called
Linked Data:

* URIs should be assigned to all entities of the dataset.
* HTTP URIs are required to ensure that all entities can be referenced and cited by users and user agents.
* Entities should be described using standard formats such as RDF/XML.
* Links should be created to other, related entity URIs.

All data that fulfil these requirements and are released for the public are called Linked Open Data (LOD). http://www.lesliesikos.com/semantic-web-machine-readable-structured-data-with-meaningful-annotations/

Read more under the heading (meta)data include qualified references to other (meta)data in the following resource: https://www.dtls.nl/fair-data/fair-principles-explained/

For more information on Linked Data standards - RDF - https://www.w3.org/RDF/

Other related linked data standards:
* OWL - https://www.w3.org/OWL/
* SKOS - https://www.w3.org/2004/02/skos/
* SPARQL - https://www.w3.org/TR/rdf-sparql-query/

|-
! rowspan="3" | R
|| R
|| About Reusable
|| Reusable data should maintain its initial richness. For example, it should not be abridged for the purpose of explaining the findings in one particular publication. It needs a clear machine-readable [https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse licence] and [https://www.ands.org.au/partners-and-communities/ands-communities/data-provenance-interest-group provenance] information on how the data was formed. It should also use discipline-specific data and [https://www.ands.org.au/working-with-data/metadata metadata] standards to give it rich contextual information that will allow for accurate interpretation and reuse.

|-
|| R1.1
|| About licenses
|| If data is not licensed no-one else can use it. In Australia, no licence is regarded as the same as 'all rights reserved', confining any reuse to very limited circumstances. Applying a Creative Commons licence to your data is a simple way to ensure that your data can be reused. The less restrictive the licence, the more that can be done with the data.
To make it easy for the Web to know when a work is available under a particular license, a “machine readable” version of the license provides a summary of the key freedoms and obligations written into a format that software systems, search engines, and other kinds of technology can understand.
Example from the [https://creativecommons.org/choose/ Creative Commons License Choser]:

What appears in the metadata:

This work is licensed under a [https://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International License].

Code snippet:

<code>
<a rel="license" href="http://creativecommons.org/licenses/by/4.0/">

<img alt="Creative Commons License" style="border-width:0"

src="https://i.creativecommons.org/l/by/4.0/88x31.png"/>

</a> This work is licensed under a

<a rel="license" href="http://creativecommons.org/licenses/by/4.0/">

Creative Commons Attribution 4.0 International License</a>.
<\code>

Read more: https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse

|-
||R1.2
|| About provenance
|| Data provenance is used to document where a piece of data comes from and the process and methodology by which it is produced. It is important to confirm the authenticity of data enabling trust, credibility and reproducibility. This is becoming increasingly important, especially in the eScience community where research is data intensive and often involves complex data transformations and procedures ([https://www.ands.org.au/partners-and-communities/ands-communities/data-provenance-interest-group Read more]). Provenance vocabularies such as [https://www.w3.org/2005/Incubator/prov/XGR-prov-20101214/#Recommendations Provenir Ontology (PROV-O) and others].

|}

== Results for SMILES and ShareWind ==

{| class="mw-collapsible wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Datasets from the SMILES and ShareWind platforms was analysed using the self-assessment tool provided by ARDC.
! rowspan="5" |
| rowspan="5" |
! Database Code
| rowspan="24" style="background: #ABE" |
| ED4
| ED9
|-
! Database Title
|| SMILES
| ShareWind
|-
! Database Link
|| https://www.ecria-smiles.eu/
| https://sharewind.eu/
|-
! Sample Link
|| [https://www.ecria-smiles.eu/data-files/-/document_library/W32cCAfL2jMX/view_file664466?_com_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX_redirect=https%3A%2F%2Fwww.ecria-smiles.eu%3A443%2Fdata-files%2F-%2Fdocument_library%2FW32cCAfL2jMX%2Fview%2F664423%3F_com_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX_redirect%3Dhttps%253A%252F%252Fwww.ecria-smiles.eu%253A443%252Fdata-files%253Fp_p_id%253Dcom_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX%2526p_p_lifecycle%253D0%2526p_p_state%253Dnormal%2526p_p_mode%253Dview here] (link has 585 characters)
| https://sharewind.eu/records/f9d31abbc2824284b8c1d28894d9619a (data found at https://zenodo.org/record/1162738)
|-
! Sample Size
|| 4 KB
| 37 MB
|-
| colspan="6" style="background: #ABE" |
|-
! rowspan="4" | Findable
| F1. (meta)data are assigned a globally unique and persistent identifier
! Does the dataset have any identifiers assigned?
|| Web adress (URL)
| Globally unique, citable and persistant (e.g. DOI, PURL, ARK or Handle)
|-

| F2. data are described with rich metadata (defined by R1 below)
! Is the dataset identifier included in all metadata records/files describing the data?
|| No
| No
|-

| F3. metadata clearly and explicitly include the identifier of the data it describes
! How is the data described with metadata?
|| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
|-

| F4. (meta)data are registered or indexed in a searchable resource
! What type of repository or registry is the metadata record in?
|| Domain-specific repository
| Data is in one place but discoverable through several registries
|-

! rowspan="3" | Accessible

| A1. (meta)data are retrievable by their identifier using a standardized communications protocol
! How accessible is the data?
|| Publicly accessible
| Publicly accessible
|-
| A1.1 the protocol is open, free, and universally implementable; A1.2 the protocol allows for an authentication and authorization procedure, where necessary
! Is the data available online without requiring specialised protocols or tools once access has been approved?
|| File download from online location
| Standard web service API (e.g. OGC)
|-
| A2. metadata are accessible, even when the data are no longer available
! Will the metadata record be available even if the data is no longer available?
|| Unsure
| Yes
|-

! rowspan="3" | Interoperable
| I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
! What (file) format(s) is the data available in?
|| In a structured, open standard, machine-readable format
|| In a structured, open standard, non-machine-readable format
|-
| I2. (meta)data use vocabularies that follow FAIR principles
! What best describes the types of vocabularies/ontologies/tagging schemas used to define the data elements?
|| Standardised vocabularies/ontologies/schema without global identifiers
|| Standardised vocabularies/ontologies/schema without global identifiers
|-
| I3. (meta)data include qualified references to other (meta)data
! How is the metadata linked to other data and metadata (to enhance context and clearly indicate relationships)?
|| There are no links to other metadata
|| Metadata is represented in a machine readable format, e.g. in a linked data format such as Resource Description Framework (RDF)
|-

! rowspan="3" | Reusable
| R1.1. (meta)data are released with a clear and accessible data usage license
! Which of the following best describes the license/usage rights attached to the data?
|| No license
| Standard machine-readable license (e.g. Creative Commons)
|-
| R1.2. (meta)data are associated with detailed provenance
! How much provenance information has been captured to facilitate data reuse?
|| Partially recorded
|| Partially recorded
|-
| R1.3. (meta)data meet domain-relevant community standards
|
|
|
|-
| colspan="6" style="background: #ABE" |
|-
! Total across F.A.I.R
|
! Low: < 50%; Medium: 50-60%; High: > 60%
|| ~ 45 %
|| ~ 85 %
|-
| colspan="6" style="background: #ABE" |
|-
! Remarks about database
||
||
||
|| The data is provided by the open-source platform Zenodo, not the project website itself. A very positive aspect is that information on different versions of the data as well as the number of views and downloads is available.
|-
! Identified gaps
||
||
||
* No DOI, only URL
* Extremely long URL link spanning several lines with 585 characters, that can easily become corrupted
* No links to other metadata
* No license information given
* Provenance not fully recorded

||
* Dataset identifiers should be included in the files describing the data
* Provenance details are only partially recorded with a note with contact name for further information
|}

= Metadata assessment =

== Dublin Core Elements ==
{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ The DCMI Metadata Terms lists the current set of the Dublin Core vocabulary. Source: [https://dublincore.org/specifications/dublin-core/dcmi-terms/ Dublin Core Metadata Initiative]
| 1 || abstract || 11 || contributor || 21 || extent || 31 || isReplacedBy || 41 || publisher || 51 || tableOfContents
|-
| 2 || accessRights || 12 || coverage || 22 || format || 32 || isRequiredBy || 42 || references || 52 || temporal
|-
| 3 || accrualMethod || 13 || created || 23 || hasFormat || 33 || issued || 43 || relation || 53 || title
|-
| 4 || accrualPeriodicity || 14 || creator || 24 || hasPart || 34 || isVersionOf || 44 || replaces || 54 || type
|-
| 5 || accrualPolicy || 15 || date || 25 || hasVersion || 35 || language || 45 || requires || 55 || valid
|-
| 6 || alternative || 16 || dateAccepted || 26 || identifier || 36 || license || 46 || rights || ||
|-
| 7 || audience || 17 || dateCopyrighted || 27 || instructionalMethod || 37 || mediator || 47 || rightsHolder || ||
|-
| 8 || available || 18 || dateSubmitted || 28 || isFormatOf || 38 || medium || 48 || source || ||
|-
| 9 || bibliographicCitation || 19 || description || 29 || isPartOf || 39 || modified || 49 || spatial || ||
|-
| 10 || conformsTo || 20 || educationLevel || 30 || isReferencedBy || 40 || provenance || 50 || subject || ||
|}

'''Metadata Category Types according to [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO]'''

There are three main types of metadata:
* Descriptive metadata describes a resource for purposes such as discovery and identification. It can include elements such as title, abstract, author, and keywords.
* Structural metadata indicates how compound objects are put together, for example, how pages are ordered to form chapters.
* Administrative metadata provides information to help manage a resource, such as when and how it was created, file type and other technical information, and who can access it. There are several subsets of administrative data; two that sometimes are listed as separate metadata types are:
** Rights management meta-data, which deals with intellectual property rights,and
** Preservation metadata, which contains information needed to archive and preserve a resource.
''

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Simple Dublin Core Elements. 15 DC elements with their (shortened) official definitions and examples.
! DC element name !! DC definition ([https://www.dublincore.org/specifications/dublin-core/dces/ DCMI]) !! Example (Energy Domain) !! Category (determined with the aid of [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO])
|-
! Title
| A name given to the resource || Outdoor monitoring of a hybrid micro-CPV solar panel with integrated micro-tracking and diffuse capture || Descriptive
|-
! Subject
| The topic of the resource || concentrator photovoltaics; CPV; rooftop photovoltaics; integrated tracking || Descriptive
|-
! Description
| An account of the resource || Dataset from the outdoor characterization of a B Series module from Insolight at the rooftop of the Instituto de Energía Solar - Universidad Politécnica de Madrid. …. || Descriptive
|-
! Creator
| An entity primarily responsible for making the resource || Stephen Askins; Gaël Nardin || Descriptive
|-
! Publisher
| An entity responsible for making the resource available || Stephen Askins || Descriptive
|-
! Contributor
| An entity responsible for making contributions to the resource || Stephen Askins || Descriptive
|-
! Date
| A point or period of time associated with an event in the lifecycle of the resource || 2019-07-23; Date will be associated with the creation or availability of the resource. || Administrative
|-
! Type
| The nature or genre of the resource || info:eu-repo/semantics/other; dataset || Descriptive
|-
! Format
| The file format, physical medium, or dimensions of the resource || image
|| Structural
|-
! Identifier
| An unambiguous reference to the resource within a given context || https://zenodo.org/record/3346823; 10.5281/zenodo.3346823; oai:zenodo.org:3346823 || Descriptive
|-
! Source
| A related resource from which the described resource is derived || RC607.A26W574 1996 [where "RC607.A26W574 1996" is the call number of the print version of the resource, from which the present version was scanned] || Descriptive
|-
! Language
| A language of the resource || eng || Descriptive
|-
! Relation
| A related resource || info:eu-repo/grantAgreement/EC/H2020/787289/ || Descriptive
|-
! Coverage
| The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant || 1995-1996; Madrid, Spain || Descriptive
|-
! Rights
| Information about rights held in and over the resource || info:eu-repo/semantics/openAccess; https://creativecommons.org/licenses/by/4.0/legalcode || Administrative
|}

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Non-DC elements

! Non-DC element name !! Definition !! Example !! Category
|-
! Date of availability
| Since when is the data available
||
|| Impact/Exploitation

|-
! Number of users
| Is there any information on the number of accesses or users?
||
|| Impact/Exploitation

|-
! Number of scientific publications where data is cited/used
| Is there any information on the number of citations/uses?
||
|| Impact/Exploitation

|-
! Curation activities (is the versioning ongoing)
| Update, maintainance
||
|| Impact/Exploitation
|}

Attempting to put a value on Impact/Exploitation:
* '''ENTSO-E''': Found 172 results in Advanced Search (ALL) in Web of Science ([https://apps.webofknowledge.com/ WoSc]) (16/06/2020). Citations of all these search result add up to 714. This however does not mean, that all search results really explicitly used data from ENTSO-E. Some (for sure, for some were found) only mentioned it saying they adoped workflow etc. from the site.
* '''SMARD''': Found 82 results in Advanced Search (ALL=(SMARD)) in Web of Science ([https://apps.webofknowledge.com/ WoSc]) (16/06/2020). However: SMARD is also an acronym for ''Spinal muscular atrophy with respiratory distress (SMARD)''. This somewhat complicates results and needs some further discussion. All 82 search results accumumlated 2359 citations themselves.

== Results for SMILES ==

{| class="wikitable"
! DC element name
! DC definition ([https://www.dublincore.org/specifications/dublin-core/dces/ DCMI])
! Category (determined with the aid of [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO])
! rowspan = "16" style="background: #ABE" |
! SMILES (ED4)
! Notes

|-
! Title
| A name given to the resource
|| Descriptive
|| A
||
|-
! Subject
| The topic of the resource || Descriptive || A ||
|-
! Description
| An account of the resource || Descriptive || PA || The information given is very limited
|-
! Creator
| An entity primarily responsible for making the resource || Descriptive || A || The metadata file uses the tag "author"
|-
! Publisher
| An entity responsible for making the resource available || Descriptive || A || This information only available under the URL but not in the metadata file
|-
! Contributor
| An entity responsible for making contributions to the resource || Descriptive || NA ||
|-
! Date
| A point or period of time associated with an event in the lifecycle of the resource || Administrative || A || This information only available under the URL but not in the metadata file
|-
! Type
| The nature or genre of the resource || Descriptive || NA || No explicit mention of the type although it could be derived from the context
|-
! Format
| The file format, physical medium, or dimensions of the resource || Structural || PA || The metadata file uses the tag "datatype" and "file"
|-
! Identifier
| An unambiguous reference to the resource within a given context || Descriptive || NA ||
|-
! Source
| A related resource from which the described resource is derived || Descriptive || NA ||
|-
! Language
| A language of the resource || Descriptive || NA || No explicit mention of the language although it could be derived from the context
|-
! Relation
| A related resource || Descriptive || NA || This information (EU grant information) only generally available under the URL but not in the metadata file
|-
! Coverage
| The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant || Descriptive || NA ||
|-
! Rights
| Information about rights held in and over the resource || Administrative || NA ||
|}

Gap analysis

2020-08-28T12:17:51Z

Christopherbs: /* Metadata assessment */ Added SMILES Assessment

<accesscontrol>Administrators,consortium</accesscontrol>

This is the page for the gap analysis.

= Tacit knowledge "FAIRification and opening of low carbon energy research data" =
 Consortium members can add any time, they feel that something is important to note even though it is not mentioned in a standard deliverable. In other words, this page collects uncodified, tacit knowledge. It will help us later to compile suggestions and lessons learned. This kind of knowledge is collected two-fold:
#Anytime if someone feels that this should be noted. Please write down: Issue, Date, Author (could also be "anonymous"), the issue described in a few words or maximal lines>
#During the final day of workshops

=== Learning process: ===

*People are hesitant to adopt new IT technologies, this is even the case among researchers heavily relying on data, algorithms and collaborative online software (e.g., R, platforms, online conferencing, HPC, ...). The effort to encourage change is not to underestimate. Reasons are several, notably, lack of time and uncertainty about potential benefit as well as overall risk aversion preferences. EERAdata is using the online software "Only Office" to facilitate collaboration (in particular also during the Covid-19 period).
 '''FAIR and open criteria:'''
*Consortium members have a fair understanding of what FAIR/O is, but there is little knowledge and/or technical experience on how to approach the FAIRification and opening. All, however, share the view that we are at a critical point in time, where we need to implement these criteria. 
*To deepen knowledge about FAIR/O criteria, it is useful to test the criteria on a database one is familiar with. For this purpose (and to start brainstorming about the platform), AIT has developed a questionnaire for application in the use cases.
'''Metadata:'''
*A good starting point is to think about metadata and to look into existing metadata concepts in one's field. The first step is to understand that also metadata need to adhere to the FAIR/O principles.
*The next step is to increase knowledge on IT specific terms, i.e. to understand what the difference is between different '''[https://energydata.onlyoffice.eu/Products/Files/doceditor.aspx?fileid=5404340 metadata frameworks] (taxonomy, thesaurus, ontology) as well as classification of metadata (high-level, medium-level, low-level OR administrative, structural and descriptive metadata).  '''WP 2 being in charge of aligning approaches between use cases, participates in all use case kickoffs to bring everybody on the same page. The presentation is linked with "metadata frameworks".
*It is useful to supply consortium members with read aheads and watch aheads on metadata to prepare the '''[https://energydata.onlyoffice.eu/Products/Files/doceditor.aspx?fileid=5296590 first EERAdata workshop]'''. The workshop starts applications and discussions in the use cases break out sessions (and bringing insights back to the plenary), using selected databases.

= FAIR Assessment=

== FAIR Guiding Principles ==
{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ This table shows the FAIR guiding principles described by [https://www.doi.org/10.1038/sdata.2016.18 Wilkinson et al., 2016].

! To be Findable
|
* F1. (meta)data are assigned a globally unique and persistent identifier
* F2. data are described with rich metadata (defined by R1 below)
* F3. metadata clearly and explicitly include the identifier of the data it describes
* F4. (meta)data are registered or indexed in a searchable resource

|-
! To be Accessible
|
* A1. (meta)data are retrievable by their identifier using a standardized communications protocol
** A1.1 the protocol is open, free, and universally implementable
** A1.2 the protocol allows for an authentication and authorization procedure, where necessary
* A2. metadata are accessible, even when the data are no longer available

|-
! To be Interoperable
|
* I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
* I2. (meta)data use vocabularies that follow FAIR principles
* I3. (meta)data include qualified references to other (meta)data

|-
! To be Reusable
|
* R1. meta(data) are richly described with a plurality of accurate and relevant attributes
* R1.1. (meta)data are released with a clear and accessible data usage license
* R1.2. (meta)data are associated with detailed provenance
* R1.3. (meta)data meet domain-relevant community standards

|}

== Identified gaps after Workshop 1 (02/06/2020 – 04/06/2020) ==

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ This table shows a summary of the specific issues that were identified in Workshop 1. The aim was to categorise different issues to get a better understanding of how to tackle these challenges.
! FAIR/O !! Specific FAIR/O !! Gaps !! Gaps for energy domain !! Use case specific gaps !! Consequences for researchers
|-
! F
| F2
|| Metadata scope
|| Different fields require different metadata (potentially very specific)
|| UC3: lack of additional metadata for applications of materials
|| Data are less useful for the specific field
|-
! F
|| F1/F3
|| Identifiers
||
* Missing identifiers on websites
* Identifier not in downloaded data files
||
|| Makes research more difficult.
|-
! I
| I2
|| Taxonomy/ontology/common vocabulary and language issues
||
* Lack of standardisation
* Heterogeneous data makes standardisation hard
* No vocabulary documentation on websites
* Words used for same term in other languages may differ
* Databases in other languages than English
||
|| Research costs more time
|-
! I
| I3
|| References to other (meta)data
||
* No linking to source documents and related publications (for contextual knowledge)
* Possible need for links to other fields
|| UC3: link between microscopic and macroscopic materials (e.g., turbine blades)
||
* Data cannot be connected to the source
* Makes research more difficult.

|-
! R
| R1.1
|| Licensing
||
* No licenses available may mean the data is not reusable for the researcher
* Licenses not clear and accessible
* Obtaining licenses may result in more effort and costs
||
|| Data potentially not reusable

|-
! R
| R1.2
|| Reliability and provenance of data
|| When the data is collected from different and high number of sources, its reliability decreases
||
|| Data potentially not reusable

|-
! O
|
|| Privacy concerns and expected disadvantages
|| Not publishing data due to privacy concerns (sensitive data)
|| UC2: In case of distribution network data this is relevant
|| Data not accessible

|-
! Other
|
|| Conducting FAIR assessments
||
* No qualitative assessment of the data itself (which may be limited)
* Discrepancies between results conducted by humans versus machines
* Sometimes not even DC standards are met
* Metadata is not updated
* Lacking encryption of websites (https)
* Problems assessing whether metadata will be available after data is unavailable
* Interface design/layout may be unclear, incomplete or not intuitive for humans
* Authentication details (user registration login / good or bad, clarify)

||
* UC1 lack of data availability for time-series
||
* Assessment is uncertain (human assessment may need more clarification and understanding)
* Makes research more time-intensive

|}

== FAIR Self-Assessment tool ==

To conduct a human FAIR evaluation for different databases and their respective datasets, different free online tools were considered. Some seemed to be quite useful (e.g. [https://satifyd.dans.knaw.nl/ SATISFYD] ) and some were simple self-evaluation PDFs to be printed and filled out.

We decided to chose the [https://ardc.edu.au/resources/working-with-data/fair-data/fair-self-assessment-tool/ self-assessment tool] provided by the Australian Research Data Commons (ARDC). The decision was based manly upon the fact that the tools questions closely matched the FAIR categories specified by [https://www.doi.org/10.1038/sdata.2016.18 Wilkinson et al., 2016] which would make it easily comparable to the existing preferable FAIR conditions and aid with the repeatability of the results, and the half-automated scoring features that show the extent of the "FAIRness" of the analysed sample as a simple bar chart.

The result of the FAIR assessment is provided as a bar chart where an empty bar chart displays 0 % FAIR compatibility, and a full bar chart represents 100 % FAIR compatibility. The score is therefore not returned as a specific value. During the assessment below, the "Total Across FAIR" in Table ... was estimated according to the level of the bar chart from 0 % to 100 %.
Resulting scores where categorised as follows: Low: < 50%; Medium: 50-60%; High: > 60% .

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ All options for each question of the ARDCs self-assessment tool
!colspan="4"|Findable
!colspan="3"|Accessible
!colspan="3"|Interoperable
!colspan="3"|Reusable
|-
| F1. (meta)data are assigned a globally unique and persistent identifier
|| F2. data are described with rich metadata (defined by R1 below)
|| F3. metadata clearly and explicitly include the identifier of the data it describes
|| F4. (meta)data are registered or indexed in a searchable resource
|| A1. (meta)data are retrievable by their identifier using a standardized communications protocol
|| A1.1 the protocol is open, free, and universally implementable and A1.2 the protocol allows for an authentication and authorization procedure, where necessary
|| A2. metadata are accessible, even when the data are no longer available
|| I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
|| I2. (meta)data use vocabularies that follow FAIR principles
|| I3. (meta)data include qualified references to other (meta)data
|| R1.1. (meta)data are released with a clear and accessible data usage license
|| R1.2. (meta)data are associated with detailed provenance
|| R1.3. (meta)data meet domain-relevant community standards
|-
! Does the dataset have any identifiers assigned?
! Is the dataset identifier included in all metadata records/files describing the data?
! How is the data described with metadata?
! What type of repository or registry is the metadata record in?
! How accessible is the data?
! Is the data available online without requiring specialised protocols or tools once access has been approved?
! Will the metadata record be available even if the data is no longer available?
! What (file) format(s) is the data available in?
! What best describes the types of vocabularies/ontologies/tagging schemas used to define the data elements?
! How is the metadata linked to other data and metadata (to enhance context and clearly indicate relationships)?
! Which of the following best describes the license/usage rights attached to the data?
! How much provenance information has been captured to facilitate data reuse?
!
|-
| Globally unique, citable and persistant (e.g. DOI, PURL, ARK or Handle)
|| Yes
|| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
|| Data is in one place but discoverable through several registries
|| Publicly accessible
|| Standard web service API (e.g. OGC)
|| Yes
|| In a structured, open standard, machine-readable format
|| Standardised open and universal using resolvable global identifiers linking to explainations
|| Meadata is represented in a machine readable format, e.g. in a linked data format such as Resource Description Framework (RDF)
|| Standard machine-readable license (e.g. Creative Commons)
|| Fully recorded in a machine-readable format
||
|-
| Web adress (URL)
|| No
|| Comprehensively, but in a text-based, non-standard format
|| Generalist public repository
|| Fully accessible to person who meet explicitly stated conditions, e.g. ethics approval for sensitive data
|| Non-standard web service (e.g. OpenAPI/Swagger/informal API)
|| No
|| In a structured, open standard, non-machine-readable format
|| Standardised vocabularies/ontologies/schema without global identifiers
|| The metadata record includes URI links to related metadata, data and definitions
|| Standard text based license
|| Fully recorded in a text format
||
|-
| Local identifier
||
|| Brief title and description
|| Domain-specific repository
|| A de-identified / modified subset of the data is publicly accessible
|| File download from online location
|| Unsure
|| Mostly in a proprietary format
|| No standards have been applied in the description of data elements
|| There are no links to other metadata
|| Non-standard machine-readably license (clearly indicating under what conditions the data may be reused)
|| Partially recorded
||
|-
| No identifier
||
|| The data is not described
|| Local institutional repository
|| Embaged access after a specific date
|| By individual arrangement
||
||
|| Data elements not described
||
|| Non-standard text-based license
|| No provenance information is recorded
||
|-
|
||
||
|| The data is not described in any repository
|| Unspecified conditional access e.g. contact the data custodian for access
|| No access to data
||
||
||
||
|| No license
||
||
|-
|
||
||
||
|| Access to metadata only
||
||
||
||
||
||
||
||
|-
|
||
||
||
|| No access to data or metadata
||
||
||
||
||
||
||
||
|}

At this point it is important to note that the assessment of databases and/or datasets using this tool is solely considered a human assessment. This may lead to different evaluations by different users. To counteract this phenomenon, it was suggested that the evaluations should be completed using the 'four-eyes principle' to create more verifiable results. Also, it is planned to additionally evaluate the respective databases using an external tool to perform a machine-assessment.

In addition, not all questions are weighted equally and thus a worse result for one question may not have a large impact whereas only choosing the second-best option in another question will bring down the overall FAIR value considerably.

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ The ARDC tool provides detailed information about the requirements for each of the options of its tool. These descriptions are documented here.
|-
! rowspan="5" | F
|| F
|| About Findable
|| Making data Findable includes assigning a [https://www.ands.org.au/guides/persistent-identifiers-awareness persistent identifier] (like a [https://www.ands.org.au/guides/doi DOI] or [https://www.ands.org.au/guides/identify-my-data Handle] ), having rich [https://www.ands.org.au/working-with-data/metadata metadata] to describe the data and making sure it is findable through disciplinary and generalist discovery portals (local and international).

|-
| F1
|| About identifiers
|| Identifiers are essential for identifying, finding, retrieving, linking and citing datasets. A Web address (URL) can be used to specify the online location of a resource but over time URLs tend to change which leads to broken links to the data. To be useful, identifiers need to be persistent and unique. Digital Object Identifiers (DOIs) and other persistent identifiers (PIDs) provide a permanent citable reference to a particular dataset. The DOI is a permanent fixed reference to the dataset no matter where it is located online and enables citation and citation metrics. Services to create a persistent identifier are often offered by your affiliated institution or the repository you are using to describe your data. Talk to your library service for more information.

Read more: [https://www.ands.org.au/working-with-data/citation-and-identifiers Persistent identifiers]

|-
| F2
|| About identifiers
|| The identifier (preferably a persistent identifier) needs to be clearly stated in the metadata record describing the data collection, and also in any associated data files or metadata.

|-
|F3
|| About metadata descriptions
|| Comprehensive metadata records will include descriptive content that facilitates discovery, access and reuse of the data being described. While there is no 'one size fits all' list, comprehensive metadata should include:
* a globally unique persistent identifier e.g. a DOI
* a title
* related people, i.e. the data creator or custodian
* how to access the data and file formats
* a description of how the data were created and how to interpret the data subject or keywords
* citation information that clearly indicates how the data should be cited
* a machine-readable data licence
* provenance and contextual information such as:
* links to related publications, projects, services and software
* methodology and processes involved in data production
* spatial and temporal coverage (if relevant)
* object-level data description

Providing metadata in a standard schema allows it to be read and used by machines as well as humans.

Read more: [https://www.ands.org.au/working-with-data/metadata Metadata]

|-
| F4
|| About metadata repositories
|| A rich metadata description alone does not ensure a dataset’s ‘findability’ on the internet; the dataset needs to be registered or indexed in a searchable resource, such as a generalist (e.g. [https://figshare.com/ Figshare], [http://datadryad.org/ Dryad], [https://zenodo.org/ Zenodo], domain-specific (e.g. [https://pangaea.de/ PANGAEA], [https://ada.edu.au/ ADA], [https://www.icpsr.umich.edu/ ICPSR]), or institutional (e.g. [https://openresearch-repository.anu.edu.au/ ANU Research Repository] , [https://ses.library.usyd.edu.au/ Sydney eScholarship Repository], [https://data.gov.au/ data.gov.au]) data repository or registry. Ideally these repositories/registries are indexed by search engines such as Google and/or Google Scholar.

|-
! rowspan = "4" | A
| A
|| About Accessible
|| [https://www.ands.org.au/working-with-data/publishing-and-reusing-data/publishing To make data accessible] may include making the data [https://www.ands.org.au/working-with-data/articulating-the-value-of-open-data/open-data open] using a standardised protocol. However the data does not necessarily have to be open. There are sometimes good reasons why data [https://www.ands.org.au/working-with-data/sensitive-data cannot be made open], for example privacy concerns, national security or commercial interests. If it is not open there should be clarity and transparency around the conditions governing access and [https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse reuse].
|-
| A1
|| About data accessibility
|| Not all data that is discoverable can be freely accessed. Often there are embargoes, access controls, and access permissions associated with data due to a variety of issues such as privacy and commercial interests. Even with all these issues much [https://www.ands.org.au/working-with-data/sensitive-data sensitive data] can be shared. Many other issues that could be perceived as blockers to sharing data may be [http://datapub.cdlib.org/closed-data-excuses-excuses overcome].
|-
| A1.1/A1.2
|| About protocols
|| Ideally users would like to retrieve appropriate internet content directly and unhindered once they have located it. Internet protocols (e.g. http and ftp) define rules and conventions for communication between devices, and tools and services are available to facilitate this process, e.g. APIs.

Protocols:
* [https://www.lifewire.com/hypertext-transfer-protocol-817944 HTTP] (Hypertext Transfer Protocol) is the set of rules for transferring files (text, graphic images, sound, video, and other multimedia files) on the [https://whatis.techtarget.com/definition/World-Wide-Web World Wide Web]. As soon as a Web user opens their Web browser, the user is indirectly making use of HTTP.
* [https://www.lifewire.com/file-transfer-protocol-817943 FTP] (File Transfer Protocol) is a standard Internet [http://searchnetworking.techtarget.com/definition/protocol protocol] for transmitting files between computers on the Internet over TCP/IP connections. Read more: [https://www.lifewire.com/definition-of-protocol-network-817949 Web protocols]
* API (Application Programming Interface): When information is made available in any machine readable format, it becomes possible to make that information directly available to programs that request that information over the web. An API is the way this information is made directly available to other machines.
View more: [https://www.youtube.com/watch?v=s7wmiS2mSXY What is an API?]

OGC - http://www.opengeospatial.org/ogc

|-
| A2
|| About metadata availability
|| Effort is often required to maintain data resources online which can often lead to it being neglected especially over long periods of time. This often leads to broken links between the metadata and the data. Having at least a description of the data in the form of a metadata record means there is a record of the data's existence allowing the possibility of someone tracking it down.

|-
! rowspan="4" | I
|| I
|| About Interoperability
|| To be interoperable (i.e. data that is interpretable by a computer, so that they can be automatically combined with other data) the data will need to use community agreed [https://www.ands.org.au/guides/file-formats formats], language and [https://www.ands.org.au/guides/vocabularies-and-research-data vocabularies]. The [https://www.ands.org.au/working-with-data/metadata metadata] will also need to use a community agreed standards and vocabularies, and contain links to related information using [https://www.ands.org.au/working-with-data/citation-and-identifiers identifiers].
|-
|| I1
|| About file formats
|| When selecting file formats for archiving, the formats should ideally be:
* Non-proprietary
* Unencrypted
* Uncompressed
* In common usage by the research community
* Adherent to an open, documented standard, such as described by the State of California (see [http://www.leginfo.ca.gov/pub/07-08/bill/asm/ab_1651-1700/ab_1668_bill_20070524_amended_asm_v96.pdf AB 1668, 2007])
** Interoperable among diverse platforms and applications
** Fully published and available royalty-free
** Fully and independently implementable by multiple software providers on multiple platforms without any intellectual property restrictions for necessary technology
** Developed and maintained by an open standards organization with a well-defined inclusive process for evolution of the standard.

From: [https://library.stanford.edu/research/data-management-services/data-best-practices/best-practices-file-formats Stanford University Library - Best practices for file formats]

File format examples:
* Structured, open standard, machine-readable format e.g. (text) PDF/A, HTML, Plain text, (images) TIFF, JPEG 2000, GIF, (audio) MP3, AIFF, WAVE, (video) MOV, MPEG, AVI, (Tabular data) CSV
* structured, open standard, non-machine-readable format, e.g. PDF, HTML, JPG
* Proprietary format, e.g. doc (Word), .xls (Excel), .ppt (PowerPoint), .sav

Read more:
* [https://www.ands.org.au/__data/assets/pdf_file/0003/731775/File-Formats.pdf ANDS]
* [https://www.europeandataportal.eu/elearning/en/module9/#/id/co-01 European Data Portal]

|-
|| I2
|| About schema standards
|| Schema standards are schemas that have gone through a formal validation process by a standards organisation, such as the International Standards Organisation (ISO) or an equivalent body such as the Dublin Core Metadata Initiative (DCMI); or, commonly used and consistently applied metadata schemas that are well documented, endorsed, and maintained can also become ‘de-facto standards’, e.g. RIF-CS for describing data collections and services used in Research Data Australia.

Read more: [https://www.ands.org.au/guides/metadata-working Metadata schema]

Standardised open and universal schemas, e.g, [https://www.dcc.ac.uk/resources/metadata-standards/datacite-metadata-schema DataCite Metadata Schema], [https://www.dcc.ac.uk/resources/metadata-standards/prov PROV], [https://www.dcc.ac.uk/resources/metadata-standards/dublin-core Dublin Core]

Domain-specific standards, e.g. [http://human-phenotype-ontology.github.io/ HPO - Human Phenotype Ontology], [https://www.nlm.nih.gov/mesh/ MeSH - Medical Subject Headings] , [https://mcp-profile-docs.readthedocs.io/en/stable/ Marine Community Profile], [https://ddialliance.org/ DDI - Data Documentation Initiative].

For more examples of standards: [http://rd-alliance.github.io/metadata-directory/ Here].

Resolvable global identifiers - An identifier is any label used to name something uniquely (whether online or offline). URLs are an example of an identifier. So are serial numbers, and personal names. A persistent identifier is guaranteed to be managed and kept up to date over a defined time period.

Read more: [https://www.ands.org.au/guides/persistent-identifiers-awareness Persistent Identifiers]

|-
||I3
|| About metadata links
|| The goal is to create as many meaningful linkages as possible between (meta)data resources to enrich the contextual knowledge about the data, balanced against the time/energy involved in making a good data model.

'''Machine-readable (meta)data'''

(Meta)data in a format that can be automatically read and processed by a computer, such as [http://opendatahandbook.org/glossary/en/terms/csv/ CSV], [http://opendatahandbook.org/glossary/en/terms/json/ JSON], [http://opendatahandbook.org/glossary/en/terms/xml/ XML] etc. For more information: [http://opendatahandbook.org/glossary/en/terms/machine-readable/ Open Data Handbook]

'''Linked Data'''

Linked Data (also known as Linking Data) can be applied to improve the exploitation of the “Web of data.” The expression refers to the publishing of structured data in a way that typed links are created between data from different sources to provide a higher level of usability. By using Linked Data, it is possible to find other, related data. Structured data should meet four requirements to be called
Linked Data:

* URIs should be assigned to all entities of the dataset.
* HTTP URIs are required to ensure that all entities can be referenced and cited by users and user agents.
* Entities should be described using standard formats such as RDF/XML.
* Links should be created to other, related entity URIs.

All data that fulfil these requirements and are released for the public are called Linked Open Data (LOD). http://www.lesliesikos.com/semantic-web-machine-readable-structured-data-with-meaningful-annotations/

Read more under the heading (meta)data include qualified references to other (meta)data in the following resource: https://www.dtls.nl/fair-data/fair-principles-explained/

For more information on Linked Data standards - RDF - https://www.w3.org/RDF/

Other related linked data standards:
* OWL - https://www.w3.org/OWL/
* SKOS - https://www.w3.org/2004/02/skos/
* SPARQL - https://www.w3.org/TR/rdf-sparql-query/

|-
! rowspan="3" | R
|| R
|| About Reusable
|| Reusable data should maintain its initial richness. For example, it should not be abridged for the purpose of explaining the findings in one particular publication. It needs a clear machine-readable [https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse licence] and [https://www.ands.org.au/partners-and-communities/ands-communities/data-provenance-interest-group provenance] information on how the data was formed. It should also use discipline-specific data and [https://www.ands.org.au/working-with-data/metadata metadata] standards to give it rich contextual information that will allow for accurate interpretation and reuse.

|-
|| R1.1
|| About licenses
|| If data is not licensed no-one else can use it. In Australia, no licence is regarded as the same as 'all rights reserved', confining any reuse to very limited circumstances. Applying a Creative Commons licence to your data is a simple way to ensure that your data can be reused. The less restrictive the licence, the more that can be done with the data.
To make it easy for the Web to know when a work is available under a particular license, a “machine readable” version of the license provides a summary of the key freedoms and obligations written into a format that software systems, search engines, and other kinds of technology can understand.
Example from the [https://creativecommons.org/choose/ Creative Commons License Choser]:

What appears in the metadata:

This work is licensed under a [https://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International License].

Code snippet:

<code>
<a rel="license" href="http://creativecommons.org/licenses/by/4.0/">

<img alt="Creative Commons License" style="border-width:0"

src="https://i.creativecommons.org/l/by/4.0/88x31.png"/>

</a> This work is licensed under a

<a rel="license" href="http://creativecommons.org/licenses/by/4.0/">

Creative Commons Attribution 4.0 International License</a>.
<\code>

Read more: https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse

|-
||R1.2
|| About provenance
|| Data provenance is used to document where a piece of data comes from and the process and methodology by which it is produced. It is important to confirm the authenticity of data enabling trust, credibility and reproducibility. This is becoming increasingly important, especially in the eScience community where research is data intensive and often involves complex data transformations and procedures ([https://www.ands.org.au/partners-and-communities/ands-communities/data-provenance-interest-group Read more]). Provenance vocabularies such as [https://www.w3.org/2005/Incubator/prov/XGR-prov-20101214/#Recommendations Provenir Ontology (PROV-O) and others].

|}

== Results for SMILES and ShareWind ==

{| class="mw-collapsible wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Datasets from the SMILES and ShareWind platforms was analysed using the self-assessment tool provided by ARDC.
! rowspan="5" |
| rowspan="5" |
! Database Code
| rowspan="24" style="background: #ABE" |
| ED4
| ED9
|-
! Database Title
|| SMILES
| ShareWind
|-
! Database Link
|| https://www.ecria-smiles.eu/
| https://sharewind.eu/
|-
! Sample Link
|| [https://www.ecria-smiles.eu/data-files/-/document_library/W32cCAfL2jMX/view_file664466?_com_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX_redirect=https%3A%2F%2Fwww.ecria-smiles.eu%3A443%2Fdata-files%2F-%2Fdocument_library%2FW32cCAfL2jMX%2Fview%2F664423%3F_com_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX_redirect%3Dhttps%253A%252F%252Fwww.ecria-smiles.eu%253A443%252Fdata-files%253Fp_p_id%253Dcom_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX%2526p_p_lifecycle%253D0%2526p_p_state%253Dnormal%2526p_p_mode%253Dview here] (link has 585 characters)
| https://sharewind.eu/records/f9d31abbc2824284b8c1d28894d9619a (data found at https://zenodo.org/record/1162738)
|-
! Sample Size
|| 4 KB
| 37 MB
|-
| colspan="6" style="background: #ABE" |
|-
! rowspan="4" | Findable
| F1. (meta)data are assigned a globally unique and persistent identifier
! Does the dataset have any identifiers assigned?
|| Web adress (URL)
| Globally unique, citable and persistant (e.g. DOI, PURL, ARK or Handle)
|-

| F2. data are described with rich metadata (defined by R1 below)
! Is the dataset identifier included in all metadata records/files describing the data?
|| No
| No
|-

| F3. metadata clearly and explicitly include the identifier of the data it describes
! How is the data described with metadata?
|| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
|-

| F4. (meta)data are registered or indexed in a searchable resource
! What type of repository or registry is the metadata record in?
|| Domain-specific repository
| Data is in one place but discoverable through several registries
|-

! rowspan="3" | Accessible

| A1. (meta)data are retrievable by their identifier using a standardized communications protocol
! How accessible is the data?
|| Publicly accessible
| Publicly accessible
|-
| A1.1 the protocol is open, free, and universally implementable; A1.2 the protocol allows for an authentication and authorization procedure, where necessary
! Is the data available online without requiring specialised protocols or tools once access has been approved?
|| File download from online location
| Standard web service API (e.g. OGC)
|-
| A2. metadata are accessible, even when the data are no longer available
! Will the metadata record be available even if the data is no longer available?
|| Unsure
| Yes
|-

! rowspan="3" | Interoperable
| I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
! What (file) format(s) is the data available in?
|| In a structured, open standard, machine-readable format
|| In a structured, open standard, non-machine-readable format
|-
| I2. (meta)data use vocabularies that follow FAIR principles
! What best describes the types of vocabularies/ontologies/tagging schemas used to define the data elements?
|| Standardised vocabularies/ontologies/schema without global identifiers
|| Standardised vocabularies/ontologies/schema without global identifiers
|-
| I3. (meta)data include qualified references to other (meta)data
! How is the metadata linked to other data and metadata (to enhance context and clearly indicate relationships)?
|| There are no links to other metadata
|| Metadata is represented in a machine readable format, e.g. in a linked data format such as Resource Description Framework (RDF)
|-

! rowspan="3" | Reusable
| R1.1. (meta)data are released with a clear and accessible data usage license
! Which of the following best describes the license/usage rights attached to the data?
|| No license
| Standard machine-readable license (e.g. Creative Commons)
|-
| R1.2. (meta)data are associated with detailed provenance
! How much provenance information has been captured to facilitate data reuse?
|| Partially recorded
|| Partially recorded
|-
| R1.3. (meta)data meet domain-relevant community standards
|
|
|
|-
| colspan="6" style="background: #ABE" |
|-
! Total across F.A.I.R
|
! Low: < 50%; Medium: 50-60%; High: > 60%
|| ~ 45 %
|| ~ 85 %
|-
| colspan="6" style="background: #ABE" |
|-
! Remarks about database
||
||
||
|| The data is provided by the open-source platform Zenodo, not the project website itself. A very positive aspect is that information on different versions of the data as well as the number of views and downloads is available.
|-
! Identified gaps
||
||
||
* No DOI, only URL
* Extremely long URL link spanning several lines with 585 characters, that can easily become corrupted
* No links to other metadata
* No license information given
* Provenance not fully recorded

||
* Dataset identifiers should be included in the files describing the data
* Provenance details are only partially recorded with a note with contact name for further information
|}

= Metadata assessment =

== Dublin Core Elements ==
{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ The DCMI Metadata Terms lists the current set of the Dublin Core vocabulary. Source: [https://dublincore.org/specifications/dublin-core/dcmi-terms/ Dublin Core Metadata Initiative]
| 1 || abstract || 11 || contributor || 21 || extent || 31 || isReplacedBy || 41 || publisher || 51 || tableOfContents
|-
| 2 || accessRights || 12 || coverage || 22 || format || 32 || isRequiredBy || 42 || references || 52 || temporal
|-
| 3 || accrualMethod || 13 || created || 23 || hasFormat || 33 || issued || 43 || relation || 53 || title
|-
| 4 || accrualPeriodicity || 14 || creator || 24 || hasPart || 34 || isVersionOf || 44 || replaces || 54 || type
|-
| 5 || accrualPolicy || 15 || date || 25 || hasVersion || 35 || language || 45 || requires || 55 || valid
|-
| 6 || alternative || 16 || dateAccepted || 26 || identifier || 36 || license || 46 || rights || ||
|-
| 7 || audience || 17 || dateCopyrighted || 27 || instructionalMethod || 37 || mediator || 47 || rightsHolder || ||
|-
| 8 || available || 18 || dateSubmitted || 28 || isFormatOf || 38 || medium || 48 || source || ||
|-
| 9 || bibliographicCitation || 19 || description || 29 || isPartOf || 39 || modified || 49 || spatial || ||
|-
| 10 || conformsTo || 20 || educationLevel || 30 || isReferencedBy || 40 || provenance || 50 || subject || ||
|}

'''Metadata Category Types according to [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO]'''

There are three main types of metadata:
* Descriptive metadata describes a resource for purposes such as discovery and identification. It can include elements such as title, abstract, author, and keywords.
* Structural metadata indicates how compound objects are put together, for example, how pages are ordered to form chapters.
* Administrative metadata provides information to help manage a resource, such as when and how it was created, file type and other technical information, and who can access it. There are several subsets of administrative data; two that sometimes are listed as separate metadata types are:
** Rights management meta-data, which deals with intellectual property rights,and
** Preservation metadata, which contains information needed to archive and preserve a resource.
''

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Simple Dublin Core Elements. 15 DC elements with their (shortened) official definitions and examples.
! DC element name !! DC definition ([https://www.dublincore.org/specifications/dublin-core/dces/ DCMI]) !! Example (Energy Domain) !! Category (determined with the aid of [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO])
|-
! Title
| A name given to the resource || Outdoor monitoring of a hybrid micro-CPV solar panel with integrated micro-tracking and diffuse capture || Descriptive
|-
! Subject
| The topic of the resource || concentrator photovoltaics; CPV; rooftop photovoltaics; integrated tracking || Descriptive
|-
! Description
| An account of the resource || Dataset from the outdoor characterization of a B Series module from Insolight at the rooftop of the Instituto de Energía Solar - Universidad Politécnica de Madrid. …. || Descriptive
|-
! Creator
| An entity primarily responsible for making the resource || Stephen Askins; Gaël Nardin || Descriptive
|-
! Publisher
| An entity responsible for making the resource available || Stephen Askins || Descriptive
|-
! Contributor
| An entity responsible for making contributions to the resource || Stephen Askins || Descriptive
|-
! Date
| A point or period of time associated with an event in the lifecycle of the resource || 2019-07-23; Date will be associated with the creation or availability of the resource. || Administrative
|-
! Type
| The nature or genre of the resource || info:eu-repo/semantics/other; dataset || Descriptive
|-
! Format
| The file format, physical medium, or dimensions of the resource || image
|| Structural
|-
! Identifier
| An unambiguous reference to the resource within a given context || https://zenodo.org/record/3346823; 10.5281/zenodo.3346823; oai:zenodo.org:3346823 || Descriptive
|-
! Source
| A related resource from which the described resource is derived || RC607.A26W574 1996 [where "RC607.A26W574 1996" is the call number of the print version of the resource, from which the present version was scanned] || Descriptive
|-
! Language
| A language of the resource || eng || Descriptive
|-
! Relation
| A related resource || info:eu-repo/grantAgreement/EC/H2020/787289/ || Descriptive
|-
! Coverage
| The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant || 1995-1996; Madrid, Spain || Descriptive
|-
! Rights
| Information about rights held in and over the resource || info:eu-repo/semantics/openAccess; https://creativecommons.org/licenses/by/4.0/legalcode || Administrative
|}

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Non-DC elements

! Non-DC element name !! Definition !! Example !! Category
|-
! Date of availability
| Since when is the data available
||
|| Impact/Exploitation

|-
! Number of users
| Is there any information on the number of accesses or users?
||
|| Impact/Exploitation

|-
! Number of scientific publications where data is cited/used
| Is there any information on the number of citations/uses?
||
|| Impact/Exploitation

|-
! Curation activities (is the versioning ongoing)
| Update, maintainance
||
|| Impact/Exploitation
|}

Attempting to put a value on Impact/Exploitation:
* '''ENTSO-E''': Found 172 results in Advanced Search (ALL) in Web of Science ([https://apps.webofknowledge.com/ WoSc]) (16/06/2020). Citations of all these search result add up to 714. This however does not mean, that all search results really explicitly used data from ENTSO-E. Some (for sure, for some were found) only mentioned it saying they adoped workflow etc. from the site.
* '''SMARD''': Found 82 results in Advanced Search (ALL=(SMARD)) in Web of Science ([https://apps.webofknowledge.com/ WoSc]) (16/06/2020). However: SMARD is also an acronym for ''Spinal muscular atrophy with respiratory distress (SMARD)''. This somewhat complicates results and needs some further discussion. All 82 search results accumumlated 2359 citations themselves.

== Results for SMILES ==

{| class="wikitable"
! DC element name !! DC definition ([https://www.dublincore.org/specifications/dublin-core/dces/ DCMI]) !! Category (determined with the aid of [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO]) !! !! SMILES (ED4) !! Notes
|-
! Title
| A name given to the resource || Descriptive || || A ||
|-
! Subject
| The topic of the resource || Descriptive || || A ||
|-
! Description
| An account of the resource || Descriptive || || PA || The information given is very limited
|-
! Creator
| An entity primarily responsible for making the resource || Descriptive || || A || The metadata file uses the tag "author"
|-
! Publisher
| An entity responsible for making the resource available || Descriptive || || A || This information only available under the URL but not in the metadata file
|-
! Contributor
| An entity responsible for making contributions to the resource || Descriptive || || NA ||
|-
! Date
| A point or period of time associated with an event in the lifecycle of the resource || Administrative || || A || This information only available under the URL but not in the metadata file
|-
! Type
| The nature or genre of the resource || Descriptive || || NA || No explicit mention of the type although it could be derived from the context
|-
! Format
| The file format, physical medium, or dimensions of the resource || Structural || || A || The metadata file uses the tag "datatype" and "file"
|-
! Identifier
| An unambiguous reference to the resource within a given context || Descriptive || || NA ||
|-
! Source
| A related resource from which the described resource is derived || Descriptive || || NA ||
|-
! Language
| A language of the resource || Descriptive || || NA || No explicit mention of the language although it could be derived from the context
|-
! Relation
| A related resource || Descriptive || || NA || This information (EU grant information) only generally available under the URL but not in the metadata file
|-
! Coverage
| The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant || Descriptive || || NA ||
|-
! Rights
| Information about rights held in and over the resource || Administrative || || NA ||
|}

Gap analysis

2020-08-28T09:49:50Z

Christopherbs: /* FAIR Self-Assessment tool */

Gap analysis

2020-08-28T09:43:06Z

Christopherbs: /* FAIR Self-Assessment tool */

<accesscontrol>Administrators,consortium</accesscontrol>

This is the page for the gap analysis.

= Tacit knowledge "FAIRification and opening of low carbon energy research data" =
 Consortium members can add any time, they feel that something is important to note even though it is not mentioned in a standard deliverable. In other words, this page collects uncodified, tacit knowledge. It will help us later to compile suggestions and lessons learned. This kind of knowledge is collected two-fold:
#Anytime if someone feels that this should be noted. Please write down: Issue, Date, Author (could also be "anonymous"), the issue described in a few words or maximal lines>
#During the final day of workshops

=== Learning process: ===

*People are hesitant to adopt new IT technologies, this is even the case among researchers heavily relying on data, algorithms and collaborative online software (e.g., R, platforms, online conferencing, HPC, ...). The effort to encourage change is not to underestimate. Reasons are several, notably, lack of time and uncertainty about potential benefit as well as overall risk aversion preferences. EERAdata is using the online software "Only Office" to facilitate collaboration (in particular also during the Covid-19 period).
 '''FAIR and open criteria:'''
*Consortium members have a fair understanding of what FAIR/O is, but there is little knowledge and/or technical experience on how to approach the FAIRification and opening. All, however, share the view that we are at a critical point in time, where we need to implement these criteria. 
*To deepen knowledge about FAIR/O criteria, it is useful to test the criteria on a database one is familiar with. For this purpose (and to start brainstorming about the platform), AIT has developed a questionnaire for application in the use cases.
'''Metadata:'''
*A good starting point is to think about metadata and to look into existing metadata concepts in one's field. The first step is to understand that also metadata need to adhere to the FAIR/O principles.
*The next step is to increase knowledge on IT specific terms, i.e. to understand what the difference is between different '''[https://energydata.onlyoffice.eu/Products/Files/doceditor.aspx?fileid=5404340 metadata frameworks] (taxonomy, thesaurus, ontology) as well as classification of metadata (high-level, medium-level, low-level OR administrative, structural and descriptive metadata).  '''WP 2 being in charge of aligning approaches between use cases, participates in all use case kickoffs to bring everybody on the same page. The presentation is linked with "metadata frameworks".
*It is useful to supply consortium members with read aheads and watch aheads on metadata to prepare the '''[https://energydata.onlyoffice.eu/Products/Files/doceditor.aspx?fileid=5296590 first EERAdata workshop]'''. The workshop starts applications and discussions in the use cases break out sessions (and bringing insights back to the plenary), using selected databases.

= FAIR Assessment=

== FAIR Guiding Principles ==
{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ This table shows the FAIR guiding principles described by [https://www.doi.org/10.1038/sdata.2016.18 Wilkinson et al., 2016].

! To be Findable
|
* F1. (meta)data are assigned a globally unique and persistent identifier
* F2. data are described with rich metadata (defined by R1 below)
* F3. metadata clearly and explicitly include the identifier of the data it describes
* F4. (meta)data are registered or indexed in a searchable resource

|-
! To be Accessible
|
* A1. (meta)data are retrievable by their identifier using a standardized communications protocol
** A1.1 the protocol is open, free, and universally implementable
** A1.2 the protocol allows for an authentication and authorization procedure, where necessary
* A2. metadata are accessible, even when the data are no longer available

|-
! To be Interoperable
|
* I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
* I2. (meta)data use vocabularies that follow FAIR principles
* I3. (meta)data include qualified references to other (meta)data

|-
! To be Reusable
|
* R1. meta(data) are richly described with a plurality of accurate and relevant attributes
* R1.1. (meta)data are released with a clear and accessible data usage license
* R1.2. (meta)data are associated with detailed provenance
* R1.3. (meta)data meet domain-relevant community standards

|}

== Identified gaps after Workshop 1 (02/06/2020 – 04/06/2020) ==

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ This table shows a summary of the specific issues that were identified in Workshop 1. The aim was to categorise different issues to get a better understanding of how to tackle these challenges.
! FAIR/O !! Specific FAIR/O !! Gaps !! Gaps for energy domain !! Use case specific gaps !! Consequences for researchers
|-
! F
| F2
|| Metadata scope
|| Different fields require different metadata (potentially very specific)
|| UC3: lack of additional metadata for applications of materials
|| Data are less useful for the specific field
|-
! F
|| F1/F3
|| Identifiers
||
* Missing identifiers on websites
* Identifier not in downloaded data files
||
|| Makes research more difficult.
|-
! I
| I2
|| Taxonomy/ontology/common vocabulary and language issues
||
* Lack of standardisation
* Heterogeneous data makes standardisation hard
* No vocabulary documentation on websites
* Words used for same term in other languages may differ
* Databases in other languages than English
||
|| Research costs more time
|-
! I
| I3
|| References to other (meta)data
||
* No linking to source documents and related publications (for contextual knowledge)
* Possible need for links to other fields
|| UC3: link between microscopic and macroscopic materials (e.g., turbine blades)
||
* Data cannot be connected to the source
* Makes research more difficult.

|-
! R
| R1.1
|| Licensing
||
* No licenses available may mean the data is not reusable for the researcher
* Licenses not clear and accessible
* Obtaining licenses may result in more effort and costs
||
|| Data potentially not reusable

|-
! R
| R1.2
|| Reliability and provenance of data
|| When the data is collected from different and high number of sources, its reliability decreases
||
|| Data potentially not reusable

|-
! O
|
|| Privacy concerns and expected disadvantages
|| Not publishing data due to privacy concerns (sensitive data)
|| UC2: In case of distribution network data this is relevant
|| Data not accessible

|-
! Other
|
|| Conducting FAIR assessments
||
* No qualitative assessment of the data itself (which may be limited)
* Discrepancies between results conducted by humans versus machines
* Sometimes not even DC standards are met
* Metadata is not updated
* Lacking encryption of websites (https)
* Problems assessing whether metadata will be available after data is unavailable
* Interface design/layout may be unclear, incomplete or not intuitive for humans
* Authentication details (user registration login / good or bad, clarify)

||
* UC1 lack of data availability for time-series
||
* Assessment is uncertain (human assessment may need more clarification and understanding)
* Makes research more time-intensive

|}

== FAIR Self-Assessment tool ==

To conduct a human FAIR evaluation for different databases and their respective datasets, different free online tools were considered. Some seemed to be quite useful (e.g. [https://satifyd.dans.knaw.nl/ SATISFYD] ) and some were simple self-evaluation PDFs to be printed and filled out.

We decided to chose the [https://ardc.edu.au/resources/working-with-data/fair-data/fair-self-assessment-tool/ self-assessment tool] provided by the Australian Research Data Commons (ARDC). The decision was based manly upon the fact that the tools questions closely matched the FAIR categories specified by [https://www.doi.org/10.1038/sdata.2016.18 Wilkinson et al., 2016] which would make it easily comparable to the existing preferable FAIR conditions and aid with the repeatability of the results, and the half-automated scoring features that show the extent of the "FAIRness" of the analysed sample as a simple bar chart.

The result of the FAIR assessment is provided as a bar chart where an empty bar chart displays 0 % FAIR compatibility, and a full bar chart represents 100 % FAIR compatibility. The score is therefore not returned as a specific value. During the assessment below, the "Total Across FAIR" in Table ... was estimated according to the level of the bar chart from 0 % to 100 %.
Resulting scores where categorised as follows: Low: < 50%; Medium: 50-60%; High: > 60% .

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ All options for each question of the ARDCs self-assessment tool
!colspan="4"|Findable
!colspan="3"|Accessible
!colspan="3"|Interoperable
!colspan="3"|Reusable
|-
| F1. (meta)data are assigned a globally unique and persistent identifier
|| F2. data are described with rich metadata (defined by R1 below)
|| F3. metadata clearly and explicitly include the identifier of the data it describes
|| F4. (meta)data are registered or indexed in a searchable resource
|| A1. (meta)data are retrievable by their identifier using a standardized communications protocol
|| A1.1 the protocol is open, free, and universally implementable and A1.2 the protocol allows for an authentication and authorization procedure, where necessary
|| A2. metadata are accessible, even when the data are no longer available
|| I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
|| I2. (meta)data use vocabularies that follow FAIR principles
|| I3. (meta)data include qualified references to other (meta)data
|| R1.1. (meta)data are released with a clear and accessible data usage license
|| R1.2. (meta)data are associated with detailed provenance
|| R1.3. (meta)data meet domain-relevant community standards
|-
! Does the dataset have any identifiers assigned?
! Is the dataset identifier included in all metadata records/files describing the data?
! How is the data described with metadata?
! What type of repository or registry is the metadata record in?
! How accessible is the data?
! Is the data available online without requiring specialised protocols or tools once access has been approved?
! Will the metadata record be available even if the data is no longer available?
! What (file) format(s) is the data available in?
! What best describes the types of vocabularies/ontologies/tagging schemas used to define the data elements?
! How is the metadata linked to other data and metadata (to enhance context and clearly indicate relationships)?
! Which of the following best describes the license/usage rights attached to the data?
! How much provenance information has been captured to facilitate data reuse?
!
|-
| Globally unique, citable and persistant (e.g. DOI, PURL, ARK or Handle)
|| Yes
|| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
|| Data is in one place but discoverable through several registries
|| Publicly accessible
|| Standard web service API (e.g. OGC)
|| Yes
|| In a structured, open standard, machine-readable format
|| Standardised open and universal using resolvable global identifiers linking to explainations
|| Meadata is represented in a machine readable format, e.g. in a linked data format such as Resource Description Framework (RDF)
|| Standard machine-readable license (e.g. Creative Commons)
|| Fully recorded in a machine-readable format
||
|-
| Web adress (URL)
|| No
|| Comprehensively, but in a text-based, non-standard format
|| Generalist public repository
|| Fully accessible to person who meet explicitly stated conditions, e.g. ethics approval for sensitive data
|| Non-standard web service (e.g. OpenAPI/Swagger/informal API)
|| No
|| In a structured, open standard, non-machine-readable format
|| Standardised vocabularies/ontologies/schema without global identifiers
|| The metadata record includes URI links to related metadata, data and definitions
|| Standard text based license
|| Fully recorded in a text format
||
|-
| Local identifier
||
|| Brief title and description
|| Domain-specific repository
|| A de-identified / modified subset of the data is publicly accessible
|| File download from online location
|| Unsure
|| Mostly in a proprietary format
|| No standards have been applied in the description of data elements
|| There are no links to other metadata
|| Non-standard machine-readably license (clearly indicating under what conditions the data may be reused)
|| Partially recorded
||
|-
| No identifier
||
|| The data is not described
|| Local institutional repository
|| Embaged access after a specific date
|| By individual arrangement
||
||
|| Data elements not described
||
|| Non-standard text-based license
|| No provenance information is recorded
||
|-
|
||
||
|| The data is not described in any repository
|| Unspecified conditional access e.g. contact the data custodian for access
|| No access to data
||
||
||
||
|| No license
||
||
|-
|
||
||
||
|| Access to metadata only
||
||
||
||
||
||
||
||
|-
|
||
||
||
|| No access to data or metadata
||
||
||
||
||
||
||
||
|}

At this point it is important to note that the assessment of databases and/or datasets using this tool is solely considered a human assessment. This may lead to different evaluations by different users. To counteract this phenomenon, it was suggested that the evaluations should be completed using the 'four-eyes principle' to create more verifiable results. Also, it is planned to additionally evaluate the respective databases using an external tool to perform a machine-assessment.

In addition, not all questions are weighted equally and thus a worse result for one question may not have a large impact whereas only choosing the second-best option in another question will bring down the overall FAIR value considerably.

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ The ARDC tool provides detailed information about the requirements for each of the options of its tool. These descriptions are documented here.
|-
! rowspan="5" | F
|| F
|| About Findable
|| Making data Findable includes assigning a persistent identifier (like a DOI or Handle ), having rich metadata to describe the data and making sure it is findable through disciplinary and generalist discovery portals (local and international).

|-
| F1
|| About identifiers
|| Identifiers are essential for identifying, finding, retrieving, linking and citing datasets. A Web address (URL) can be used to specify the online location of a resource but over time URLs tend to change which leads to broken links to the data. To be useful, identifiers need to be persistent and unique. Digital Object Identifiers (DOIs) and other persistent identifiers (PIDs) provide a permanent citable reference to a particular dataset. The DOI is a permanent fixed reference to the dataset no matter where it is located online and enables citation and citation metrics. Services to create a persistent identifier are often offered by your affiliated institution or the repository you are using to describe your data. Talk to your library service for more information.
|-
| F2
|| About identifiers
|| The identifier (preferably a persistent identifier) needs to be clearly stated in the metadata record describing the data collection, and also in any associated data files or metadata.

|-
|F3
|| About metadata descriptions
|| Comprehensive metadata records will include descriptive content that facilitates discovery, access and reuse of the data being described. While there is no 'one size fits all' list, comprehensive metadata should include:
* a globally unique persistent identifier e.g. a DOI
* a title
* related people, i.e. the data creator or custodian
* how to access the data and file formats
* a description of how the data were created and how to interpret the data subject or keywords
* citation information that clearly indicates how the data should be cited
* a machine-readable data licence
* provenance and contextual information such as:
* links to related publications, projects, services and software
* methodology and processes involved in data production
* spatial and temporal coverage (if relevant)
* object-level data description

Providing metadata in a standard schema allows it to be read and used by machines as well as humans.

|-
| F4
|| About metadata repositories
|| A rich metadata description alone does not ensure a dataset’s ‘findability’ on the internet; the dataset needs to be registered or indexed in a searchable resource, such as a generalist (e.g. Figshare, Dryad, Zenodo domain-specific (e.g. PANGAEA, ADA, ICPSR), or institutional (e.g. ANU Research Repository , Sydney eScholarship Repository, data.gov.au) data repository or registry. Ideally these repositories/registries are indexed by search engines such as Google and/or Google Scholar.

|-
! rowspan = "4" | A
| A
|| About Accessible
|| [https://www.ands.org.au/working-with-data/publishing-and-reusing-data/publishing To make data accessible] may include making the data [https://www.ands.org.au/working-with-data/articulating-the-value-of-open-data/open-data open] using a standardised protocol. However the data does not necessarily have to be open. There are sometimes good reasons why data [https://www.ands.org.au/working-with-data/sensitive-data cannot be made open], for example privacy concerns, national security or commercial interests. If it is not open there should be clarity and transparency around the conditions governing access and [https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse reuse].
|-
| A1
|| About data accessibility
|| Not all data that is discoverable can be freely accessed. Often there are embargoes, access controls, and access permissions associated with data due to a variety of issues such as privacy and commercial interests. Even with all these issues much [https://www.ands.org.au/working-with-data/sensitive-data sensitive data] can be shared. Many other issues that could be perceived as blockers to sharing data may be [http://datapub.cdlib.org/closed-data-excuses-excuses overcome].
|-
| A1.1/A1.2
|| About protocols
|| Ideally users would like to retrieve appropriate internet content directly and unhindered once they have located it. Internet protocols (e.g. http and ftp) define rules and conventions for communication between devices, and tools and services are available to facilitate this process, e.g. APIs.

Protocols:
* [https://www.lifewire.com/hypertext-transfer-protocol-817944 HTTP] (Hypertext Transfer Protocol) is the set of rules for transferring files (text, graphic images, sound, video, and other multimedia files) on the [https://whatis.techtarget.com/definition/World-Wide-Web World Wide Web]. As soon as a Web user opens their Web browser, the user is indirectly making use of HTTP.
* [https://www.lifewire.com/file-transfer-protocol-817943 FTP] (File Transfer Protocol) is a standard Internet [http://searchnetworking.techtarget.com/definition/protocol protocol] for transmitting files between computers on the Internet over TCP/IP connections. Read more: [https://www.lifewire.com/definition-of-protocol-network-817949 Web protocols]
* API (Application Programming Interface): When information is made available in any machine readable format, it becomes possible to make that information directly available to programs that request that information over the web. An API is the way this information is made directly available to other machines.
View more: [https://www.youtube.com/watch?v=s7wmiS2mSXY What is an API?]

OGC - http://www.opengeospatial.org/ogc

|-
| A2
|| About metadata availability
|| Effort is often required to maintain data resources online which can often lead to it being neglected especially over long periods of time. This often leads to broken links between the metadata and the data. Having at least a description of the data in the form of a metadata record means there is a record of the data's existence allowing the possibility of someone tracking it down.

|-
! rowspan="4" | I
|| I
|| About Interoperability
|| To be interoperable (i.e. data that is interpretable by a computer, so that they can be automatically combined with other data) the data will need to use community agreed [https://www.ands.org.au/guides/file-formats formats], language and [https://www.ands.org.au/guides/vocabularies-and-research-data vocabularies]. The [https://www.ands.org.au/working-with-data/metadata metadata] will also need to use a community agreed standards and vocabularies, and contain links to related information using [https://www.ands.org.au/working-with-data/citation-and-identifiers identifiers].
|-
|| I1
|| About file formats
|| When selecting file formats for archiving, the formats should ideally be:
* Non-proprietary
* Unencrypted
* Uncompressed
* In common usage by the research community
* Adherent to an open, documented standard, such as described by the State of California (see [http://www.leginfo.ca.gov/pub/07-08/bill/asm/ab_1651-1700/ab_1668_bill_20070524_amended_asm_v96.pdf AB 1668, 2007])
** Interoperable among diverse platforms and applications
** Fully published and available royalty-free
** Fully and independently implementable by multiple software providers on multiple platforms without any intellectual property restrictions for necessary technology
** Developed and maintained by an open standards organization with a well-defined inclusive process for evolution of the standard.

From: [https://library.stanford.edu/research/data-management-services/data-best-practices/best-practices-file-formats Stanford University Library - Best practices for file formats]

File format examples:
* Structured, open standard, machine-readable format e.g. (text) PDF/A, HTML, Plain text, (images) TIFF, JPEG 2000, GIF, (audio) MP3, AIFF, WAVE, (video) MOV, MPEG, AVI, (Tabular data) CSV
* structured, open standard, non-machine-readable format, e.g. PDF, HTML, JPG
* Proprietary format, e.g. doc (Word), .xls (Excel), .ppt (PowerPoint), .sav

Read more:
* [https://www.ands.org.au/__data/assets/pdf_file/0003/731775/File-Formats.pdf ANDS]
* [https://www.europeandataportal.eu/elearning/en/module9/#/id/co-01 European Data Portal]

|-
|| I2
|| About schema standards
|| Schema standards are schemas that have gone through a formal validation process by a standards organisation, such as the International Standards Organisation (ISO) or an equivalent body such as the Dublin Core Metadata Initiative (DCMI); or, commonly used and consistently applied metadata schemas that are well documented, endorsed, and maintained can also become ‘de-facto standards’, e.g. RIF-CS for describing data collections and services used in Research Data Australia.

Read more: [https://www.ands.org.au/guides/metadata-working Metadata schema]

Standardised open and universal schemas, e.g, [https://www.dcc.ac.uk/resources/metadata-standards/datacite-metadata-schema DataCite Metadata Schema], [https://www.dcc.ac.uk/resources/metadata-standards/prov PROV], [https://www.dcc.ac.uk/resources/metadata-standards/dublin-core Dublin Core]

Domain-specific standards, e.g. [http://human-phenotype-ontology.github.io/ HPO - Human Phenotype Ontology], [https://www.nlm.nih.gov/mesh/ MeSH - Medical Subject Headings] , [https://mcp-profile-docs.readthedocs.io/en/stable/ Marine Community Profile], [https://ddialliance.org/ DDI - Data Documentation Initiative].

For more examples of standards: [http://rd-alliance.github.io/metadata-directory/ Here].

Resolvable global identifiers - An identifier is any label used to name something uniquely (whether online or offline). URLs are an example of an identifier. So are serial numbers, and personal names. A persistent identifier is guaranteed to be managed and kept up to date over a defined time period.

Read more: [https://www.ands.org.au/guides/persistent-identifiers-awareness Persistent Identifiers]

|-
||I3
|| About metadata links
|| The goal is to create as many meaningful linkages as possible between (meta)data resources to enrich the contextual knowledge about the data, balanced against the time/energy involved in making a good data model.

'''Machine-readable (meta)data'''

(Meta)data in a format that can be automatically read and processed by a computer, such as [http://opendatahandbook.org/glossary/en/terms/csv/ CSV], [http://opendatahandbook.org/glossary/en/terms/json/ JSON], [http://opendatahandbook.org/glossary/en/terms/xml/ XML] etc. For more information: [http://opendatahandbook.org/glossary/en/terms/machine-readable/ Open Data Handbook]

'''Linked Data'''

Linked Data (also known as Linking Data) can be applied to improve the exploitation of the “Web of data.” The expression refers to the publishing of structured data in a way that typed links are created between data from different sources to provide a higher level of usability. By using Linked Data, it is possible to find other, related data. Structured data should meet four requirements to be called
Linked Data:

* URIs should be assigned to all entities of the dataset.
* HTTP URIs are required to ensure that all entities can be referenced and cited by users and user agents.
* Entities should be described using standard formats such as RDF/XML.
* Links should be created to other, related entity URIs.

All data that fulfil these requirements and are released for the public are called Linked Open Data (LOD). http://www.lesliesikos.com/semantic-web-machine-readable-structured-data-with-meaningful-annotations/

Read more under the heading (meta)data include qualified references to other (meta)data in the following resource: https://www.dtls.nl/fair-data/fair-principles-explained/

For more information on Linked Data standards - RDF - https://www.w3.org/RDF/

Other related linked data standards:
* OWL - https://www.w3.org/OWL/
* SKOS - https://www.w3.org/2004/02/skos/
* SPARQL - https://www.w3.org/TR/rdf-sparql-query/

|-
! rowspan="3" | R
|| R
|| About Reusable
|| Reusable data should maintain its initial richness. For example, it should not be abridged for the purpose of explaining the findings in one particular publication. It needs a clear machine-readable [https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse licence] and [https://www.ands.org.au/partners-and-communities/ands-communities/data-provenance-interest-group provenance] information on how the data was formed. It should also use discipline-specific data and [https://www.ands.org.au/working-with-data/metadata metadata] standards to give it rich contextual information that will allow for accurate interpretation and reuse.

|-
|| R1.1
|| About licenses
|| If data is not licensed no-one else can use it. In Australia, no licence is regarded as the same as 'all rights reserved', confining any reuse to very limited circumstances. Applying a Creative Commons licence to your data is a simple way to ensure that your data can be reused. The less restrictive the licence, the more that can be done with the data.
To make it easy for the Web to know when a work is available under a particular license, a “machine readable” version of the license provides a summary of the key freedoms and obligations written into a format that software systems, search engines, and other kinds of technology can understand.
Example from the [https://creativecommons.org/choose/ Creative Commons License Choser]:

What appears in the metadata:

This work is licensed under a [https://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International License].

Code snippet:

<code>
<a rel="license" href="http://creativecommons.org/licenses/by/4.0/">

<img alt="Creative Commons License" style="border-width:0"

src="https://i.creativecommons.org/l/by/4.0/88x31.png"/>

</a> This work is licensed under a

<a rel="license" href="http://creativecommons.org/licenses/by/4.0/">

Creative Commons Attribution 4.0 International License</a>.
<\code>

Read more: https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse

|-
||R1.2
|| About provenance
|| Data provenance is used to document where a piece of data comes from and the process and methodology by which it is produced. It is important to confirm the authenticity of data enabling trust, credibility and reproducibility. This is becoming increasingly important, especially in the eScience community where research is data intensive and often involves complex data transformations and procedures ([https://www.ands.org.au/partners-and-communities/ands-communities/data-provenance-interest-group Read more]). Provenance vocabularies such as [https://www.w3.org/2005/Incubator/prov/XGR-prov-20101214/#Recommendations Provenir Ontology (PROV-O) and others].

|}

== Results for SMILES and ShareWind ==

{| class="mw-collapsible wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Datasets from the SMILES and ShareWind platforms was analysed using the self-assessment tool provided by ARDC.
! rowspan="5" |
| rowspan="5" |
! Database Code
| rowspan="24" style="background: #ABE" |
| ED4
| ED9
|-
! Database Title
|| SMILES
| ShareWind
|-
! Database Link
|| https://www.ecria-smiles.eu/
| https://sharewind.eu/
|-
! Sample Link
|| [https://www.ecria-smiles.eu/data-files/-/document_library/W32cCAfL2jMX/view_file664466?_com_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX_redirect=https%3A%2F%2Fwww.ecria-smiles.eu%3A443%2Fdata-files%2F-%2Fdocument_library%2FW32cCAfL2jMX%2Fview%2F664423%3F_com_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX_redirect%3Dhttps%253A%252F%252Fwww.ecria-smiles.eu%253A443%252Fdata-files%253Fp_p_id%253Dcom_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX%2526p_p_lifecycle%253D0%2526p_p_state%253Dnormal%2526p_p_mode%253Dview here] (link has 585 characters)
| https://sharewind.eu/records/f9d31abbc2824284b8c1d28894d9619a (data found at https://zenodo.org/record/1162738)
|-
! Sample Size
|| 4 KB
| 37 MB
|-
| colspan="6" style="background: #ABE" |
|-
! rowspan="4" | Findable
| F1. (meta)data are assigned a globally unique and persistent identifier
! Does the dataset have any identifiers assigned?
|| Web adress (URL)
| Globally unique, citable and persistant (e.g. DOI, PURL, ARK or Handle)
|-

| F2. data are described with rich metadata (defined by R1 below)
! Is the dataset identifier included in all metadata records/files describing the data?
|| No
| No
|-

| F3. metadata clearly and explicitly include the identifier of the data it describes
! How is the data described with metadata?
|| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
|-

| F4. (meta)data are registered or indexed in a searchable resource
! What type of repository or registry is the metadata record in?
|| Domain-specific repository
| Data is in one place but discoverable through several registries
|-

! rowspan="3" | Accessible

| A1. (meta)data are retrievable by their identifier using a standardized communications protocol
! How accessible is the data?
|| Publicly accessible
| Publicly accessible
|-
| A1.1 the protocol is open, free, and universally implementable; A1.2 the protocol allows for an authentication and authorization procedure, where necessary
! Is the data available online without requiring specialised protocols or tools once access has been approved?
|| File download from online location
| Standard web service API (e.g. OGC)
|-
| A2. metadata are accessible, even when the data are no longer available
! Will the metadata record be available even if the data is no longer available?
|| Unsure
| Yes
|-

! rowspan="3" | Interoperable
| I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
! What (file) format(s) is the data available in?
|| In a structured, open standard, machine-readable format
|| In a structured, open standard, non-machine-readable format
|-
| I2. (meta)data use vocabularies that follow FAIR principles
! What best describes the types of vocabularies/ontologies/tagging schemas used to define the data elements?
|| Standardised vocabularies/ontologies/schema without global identifiers
|| Standardised vocabularies/ontologies/schema without global identifiers
|-
| I3. (meta)data include qualified references to other (meta)data
! How is the metadata linked to other data and metadata (to enhance context and clearly indicate relationships)?
|| There are no links to other metadata
|| Metadata is represented in a machine readable format, e.g. in a linked data format such as Resource Description Framework (RDF)
|-

! rowspan="3" | Reusable
| R1.1. (meta)data are released with a clear and accessible data usage license
! Which of the following best describes the license/usage rights attached to the data?
|| No license
| Standard machine-readable license (e.g. Creative Commons)
|-
| R1.2. (meta)data are associated with detailed provenance
! How much provenance information has been captured to facilitate data reuse?
|| Partially recorded
|| Partially recorded
|-
| R1.3. (meta)data meet domain-relevant community standards
|
|
|
|-
| colspan="6" style="background: #ABE" |
|-
! Total across F.A.I.R
|
! Low: < 50%; Medium: 50-60%; High: > 60%
|| ~ 45 %
|| ~ 85 %
|-
| colspan="6" style="background: #ABE" |
|-
! Remarks about database
||
||
||
|| The data is provided by the open-source platform Zenodo, not the project website itself. A very positive aspect is that information on different versions of the data as well as the number of views and downloads is available.
|-
! Identified gaps
||
||
||
* No DOI, only URL
* Extremely long URL link spanning several lines with 585 characters, that can easily become corrupted
* No links to other metadata
* No license information given
* Provenance not fully recorded

||
* Dataset identifiers should be included in the files describing the data
* Provenance details are only partially recorded with a note with contact name for further information
|}

= Metadata assessment =

== Dublin Core Elements ==
{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ The DCMI Metadata Terms lists the current set of the Dublin Core vocabulary. Source: [https://dublincore.org/specifications/dublin-core/dcmi-terms/ Dublin Core Metadata Initiative]
| 1 || abstract || 11 || contributor || 21 || extent || 31 || isReplacedBy || 41 || publisher || 51 || tableOfContents
|-
| 2 || accessRights || 12 || coverage || 22 || format || 32 || isRequiredBy || 42 || references || 52 || temporal
|-
| 3 || accrualMethod || 13 || created || 23 || hasFormat || 33 || issued || 43 || relation || 53 || title
|-
| 4 || accrualPeriodicity || 14 || creator || 24 || hasPart || 34 || isVersionOf || 44 || replaces || 54 || type
|-
| 5 || accrualPolicy || 15 || date || 25 || hasVersion || 35 || language || 45 || requires || 55 || valid
|-
| 6 || alternative || 16 || dateAccepted || 26 || identifier || 36 || license || 46 || rights || ||
|-
| 7 || audience || 17 || dateCopyrighted || 27 || instructionalMethod || 37 || mediator || 47 || rightsHolder || ||
|-
| 8 || available || 18 || dateSubmitted || 28 || isFormatOf || 38 || medium || 48 || source || ||
|-
| 9 || bibliographicCitation || 19 || description || 29 || isPartOf || 39 || modified || 49 || spatial || ||
|-
| 10 || conformsTo || 20 || educationLevel || 30 || isReferencedBy || 40 || provenance || 50 || subject || ||
|}

'''Metadata Category Types according to [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO]'''

There are three main types of metadata:
* Descriptive metadata describes a resource for purposes such as discovery and identification. It can include elements such as title, abstract, author, and keywords.
* Structural metadata indicates how compound objects are put together, for example, how pages are ordered to form chapters.
* Administrative metadata provides information to help manage a resource, such as when and how it was created, file type and other technical information, and who can access it. There are several subsets of administrative data; two that sometimes are listed as separate metadata types are:
** Rights management meta-data, which deals with intellectual property rights,and
** Preservation metadata, which contains information needed to archive and preserve a resource.
''

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Simple Dublin Core Elements. 15 DC elements with their (shortened) official definitions and examples.
! DC element name !! DC definition ([https://www.dublincore.org/specifications/dublin-core/dces/ DCMI]) !! Example (Energy Domain) !! Category (determined with the aid of [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO])
|-
! Title
| A name given to the resource || Outdoor monitoring of a hybrid micro-CPV solar panel with integrated micro-tracking and diffuse capture || Descriptive
|-
! Subject
| The topic of the resource || concentrator photovoltaics; CPV; rooftop photovoltaics; integrated tracking || Descriptive
|-
! Description
| An account of the resource || Dataset from the outdoor characterization of a B Series module from Insolight at the rooftop of the Instituto de Energía Solar - Universidad Politécnica de Madrid. …. || Descriptive
|-
! Creator
| An entity primarily responsible for making the resource || Stephen Askins; Gaël Nardin || Descriptive
|-
! Publisher
| An entity responsible for making the resource available || Stephen Askins || Descriptive
|-
! Contributor
| An entity responsible for making contributions to the resource || Stephen Askins || Descriptive
|-
! Date
| A point or period of time associated with an event in the lifecycle of the resource || 2019-07-23; Date will be associated with the creation or availability of the resource. || Administrative
|-
! Type
| The nature or genre of the resource || info:eu-repo/semantics/other; dataset || Descriptive
|-
! Format
| The file format, physical medium, or dimensions of the resource || image
|| Structural
|-
! Identifier
| An unambiguous reference to the resource within a given context || https://zenodo.org/record/3346823; 10.5281/zenodo.3346823; oai:zenodo.org:3346823 || Descriptive
|-
! Source
| A related resource from which the described resource is derived || RC607.A26W574 1996 [where "RC607.A26W574 1996" is the call number of the print version of the resource, from which the present version was scanned] || Descriptive
|-
! Language
| A language of the resource || eng || Descriptive
|-
! Relation
| A related resource || info:eu-repo/grantAgreement/EC/H2020/787289/ || Descriptive
|-
! Coverage
| The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant || 1995-1996; Madrid, Spain || Descriptive
|-
! Rights
| Information about rights held in and over the resource || info:eu-repo/semantics/openAccess; https://creativecommons.org/licenses/by/4.0/legalcode || Administrative
|}

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Non-DC elements

! Non-DC element name !! Definition !! Example !! Category
|-
! Date of availability
| Since when is the data available
||
|| Impact/Exploitation

|-
! Number of users
| Is there any information on the number of accesses or users?
||
|| Impact/Exploitation

|-
! Number of scientific publications where data is cited/used
| Is there any information on the number of citations/uses?
||
|| Impact/Exploitation

|-
! Curation activities (is the versioning ongoing)
| Update, maintainance
||
|| Impact/Exploitation
|}

Attempting to put a value on Impact/Exploitation:
* '''ENTSO-E''': Found 172 results in Advanced Search (ALL) in Web of Science ([https://apps.webofknowledge.com/ WoSc]) (16/06/2020). Citations of all these search result add up to 714. This however does not mean, that all search results really explicitly used data from ENTSO-E. Some (for sure, for some were found) only mentioned it saying they adoped workflow etc. from the site.
* '''SMARD''': Found 82 results in Advanced Search (ALL=(SMARD)) in Web of Science ([https://apps.webofknowledge.com/ WoSc]) (16/06/2020). However: SMARD is also an acronym for ''Spinal muscular atrophy with respiratory distress (SMARD)''. This somewhat complicates results and needs some further discussion. All 82 search results accumumlated 2359 citations themselves.

Gap analysis

2020-08-28T09:39:16Z

Christopherbs: /* FAIR Self-Assessment tool */

<accesscontrol>Administrators,consortium</accesscontrol>

This is the page for the gap analysis.

= Tacit knowledge "FAIRification and opening of low carbon energy research data" =
 Consortium members can add any time, they feel that something is important to note even though it is not mentioned in a standard deliverable. In other words, this page collects uncodified, tacit knowledge. It will help us later to compile suggestions and lessons learned. This kind of knowledge is collected two-fold:
#Anytime if someone feels that this should be noted. Please write down: Issue, Date, Author (could also be "anonymous"), the issue described in a few words or maximal lines>
#During the final day of workshops

=== Learning process: ===

*People are hesitant to adopt new IT technologies, this is even the case among researchers heavily relying on data, algorithms and collaborative online software (e.g., R, platforms, online conferencing, HPC, ...). The effort to encourage change is not to underestimate. Reasons are several, notably, lack of time and uncertainty about potential benefit as well as overall risk aversion preferences. EERAdata is using the online software "Only Office" to facilitate collaboration (in particular also during the Covid-19 period).
 '''FAIR and open criteria:'''
*Consortium members have a fair understanding of what FAIR/O is, but there is little knowledge and/or technical experience on how to approach the FAIRification and opening. All, however, share the view that we are at a critical point in time, where we need to implement these criteria. 
*To deepen knowledge about FAIR/O criteria, it is useful to test the criteria on a database one is familiar with. For this purpose (and to start brainstorming about the platform), AIT has developed a questionnaire for application in the use cases.
'''Metadata:'''
*A good starting point is to think about metadata and to look into existing metadata concepts in one's field. The first step is to understand that also metadata need to adhere to the FAIR/O principles.
*The next step is to increase knowledge on IT specific terms, i.e. to understand what the difference is between different '''[https://energydata.onlyoffice.eu/Products/Files/doceditor.aspx?fileid=5404340 metadata frameworks] (taxonomy, thesaurus, ontology) as well as classification of metadata (high-level, medium-level, low-level OR administrative, structural and descriptive metadata).  '''WP 2 being in charge of aligning approaches between use cases, participates in all use case kickoffs to bring everybody on the same page. The presentation is linked with "metadata frameworks".
*It is useful to supply consortium members with read aheads and watch aheads on metadata to prepare the '''[https://energydata.onlyoffice.eu/Products/Files/doceditor.aspx?fileid=5296590 first EERAdata workshop]'''. The workshop starts applications and discussions in the use cases break out sessions (and bringing insights back to the plenary), using selected databases.

= FAIR Assessment=

== FAIR Guiding Principles ==
{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ This table shows the FAIR guiding principles described by [https://www.doi.org/10.1038/sdata.2016.18 Wilkinson et al., 2016].

! To be Findable
|
* F1. (meta)data are assigned a globally unique and persistent identifier
* F2. data are described with rich metadata (defined by R1 below)
* F3. metadata clearly and explicitly include the identifier of the data it describes
* F4. (meta)data are registered or indexed in a searchable resource

|-
! To be Accessible
|
* A1. (meta)data are retrievable by their identifier using a standardized communications protocol
** A1.1 the protocol is open, free, and universally implementable
** A1.2 the protocol allows for an authentication and authorization procedure, where necessary
* A2. metadata are accessible, even when the data are no longer available

|-
! To be Interoperable
|
* I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
* I2. (meta)data use vocabularies that follow FAIR principles
* I3. (meta)data include qualified references to other (meta)data

|-
! To be Reusable
|
* R1. meta(data) are richly described with a plurality of accurate and relevant attributes
* R1.1. (meta)data are released with a clear and accessible data usage license
* R1.2. (meta)data are associated with detailed provenance
* R1.3. (meta)data meet domain-relevant community standards

|}

== Identified gaps after Workshop 1 (02/06/2020 – 04/06/2020) ==

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ This table shows a summary of the specific issues that were identified in Workshop 1. The aim was to categorise different issues to get a better understanding of how to tackle these challenges.
! FAIR/O !! Specific FAIR/O !! Gaps !! Gaps for energy domain !! Use case specific gaps !! Consequences for researchers
|-
! F
| F2
|| Metadata scope
|| Different fields require different metadata (potentially very specific)
|| UC3: lack of additional metadata for applications of materials
|| Data are less useful for the specific field
|-
! F
|| F1/F3
|| Identifiers
||
* Missing identifiers on websites
* Identifier not in downloaded data files
||
|| Makes research more difficult.
|-
! I
| I2
|| Taxonomy/ontology/common vocabulary and language issues
||
* Lack of standardisation
* Heterogeneous data makes standardisation hard
* No vocabulary documentation on websites
* Words used for same term in other languages may differ
* Databases in other languages than English
||
|| Research costs more time
|-
! I
| I3
|| References to other (meta)data
||
* No linking to source documents and related publications (for contextual knowledge)
* Possible need for links to other fields
|| UC3: link between microscopic and macroscopic materials (e.g., turbine blades)
||
* Data cannot be connected to the source
* Makes research more difficult.

|-
! R
| R1.1
|| Licensing
||
* No licenses available may mean the data is not reusable for the researcher
* Licenses not clear and accessible
* Obtaining licenses may result in more effort and costs
||
|| Data potentially not reusable

|-
! R
| R1.2
|| Reliability and provenance of data
|| When the data is collected from different and high number of sources, its reliability decreases
||
|| Data potentially not reusable

|-
! O
|
|| Privacy concerns and expected disadvantages
|| Not publishing data due to privacy concerns (sensitive data)
|| UC2: In case of distribution network data this is relevant
|| Data not accessible

|-
! Other
|
|| Conducting FAIR assessments
||
* No qualitative assessment of the data itself (which may be limited)
* Discrepancies between results conducted by humans versus machines
* Sometimes not even DC standards are met
* Metadata is not updated
* Lacking encryption of websites (https)
* Problems assessing whether metadata will be available after data is unavailable
* Interface design/layout may be unclear, incomplete or not intuitive for humans
* Authentication details (user registration login / good or bad, clarify)

||
* UC1 lack of data availability for time-series
||
* Assessment is uncertain (human assessment may need more clarification and understanding)
* Makes research more time-intensive

|}

== FAIR Self-Assessment tool ==

To conduct a human FAIR evaluation for different databases and their respective datasets, different free online tools were considered. Some seemed to be quite useful (e.g. [https://satifyd.dans.knaw.nl/ SATISFYD] ) and some were simple self-evaluation PDFs to be printed and filled out.

We decided to chose the [https://ardc.edu.au/resources/working-with-data/fair-data/fair-self-assessment-tool/ self-assessment tool] provided by the Australian Research Data Commons (ARDC). The decision was based manly upon the fact that the tools questions closely matched the FAIR categories specified by [https://www.doi.org/10.1038/sdata.2016.18 Wilkinson et al., 2016] which would make it easily comparable to the existing preferable FAIR conditions and aid with the repeatability of the results, and the half-automated scoring features that show the extent of the "FAIRness" of the analysed sample as a simple bar chart.

The result of the FAIR assessment is provided as a bar chart where an empty bar chart displays 0 % FAIR compatibility, and a full bar chart represents 100 % FAIR compatibility. The score is therefore not returned as a specific value. During the assessment below, the "Total Across FAIR" in Table ... was estimated according to the level of the bar chart from 0 % to 100 %.
Resulting scores where categorised as follows: Low: < 50%; Medium: 50-60%; High: > 60% .

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ All options for each question of the ARDCs self-assessment tool
!colspan="4"|Findable
!colspan="3"|Accessible
!colspan="3"|Interoperable
!colspan="3"|Reusable
|-
| F1. (meta)data are assigned a globally unique and persistent identifier
|| F2. data are described with rich metadata (defined by R1 below)
|| F3. metadata clearly and explicitly include the identifier of the data it describes
|| F4. (meta)data are registered or indexed in a searchable resource
|| A1. (meta)data are retrievable by their identifier using a standardized communications protocol
|| A1.1 the protocol is open, free, and universally implementable and A1.2 the protocol allows for an authentication and authorization procedure, where necessary
|| A2. metadata are accessible, even when the data are no longer available
|| I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
|| I2. (meta)data use vocabularies that follow FAIR principles
|| I3. (meta)data include qualified references to other (meta)data
|| R1.1. (meta)data are released with a clear and accessible data usage license
|| R1.2. (meta)data are associated with detailed provenance
|| R1.3. (meta)data meet domain-relevant community standards
|-
! Does the dataset have any identifiers assigned?
! Is the dataset identifier included in all metadata records/files describing the data?
! How is the data described with metadata?
! What type of repository or registry is the metadata record in?
! How accessible is the data?
! Is the data available online without requiring specialised protocols or tools once access has been approved?
! Will the metadata record be available even if the data is no longer available?
! What (file) format(s) is the data available in?
! What best describes the types of vocabularies/ontologies/tagging schemas used to define the data elements?
! How is the metadata linked to other data and metadata (to enhance context and clearly indicate relationships)?
! Which of the following best describes the license/usage rights attached to the data?
! How much provenance information has been captured to facilitate data reuse?
!
|-
| Globally unique, citable and persistant (e.g. DOI, PURL, ARK or Handle)
|| Yes
|| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
|| Data is in one place but discoverable through several registries
|| Publicly accessible
|| Standard web service API (e.g. OGC)
|| Yes
|| In a structured, open standard, machine-readable format
|| Standardised open and universal using resolvable global identifiers linking to explainations
|| Meadata is represented in a machine readable format, e.g. in a linked data format such as Resource Description Framework (RDF)
|| Standard machine-readable license (e.g. Creative Commons)
|| Fully recorded in a machine-readable format
||
|-
| Web adress (URL)
|| No
|| Comprehensively, but in a text-based, non-standard format
|| Generalist public repository
|| Fully accessible to person who meet explicitly stated conditions, e.g. ethics approval for sensitive data
|| Non-standard web service (e.g. OpenAPI/Swagger/informal API)
|| No
|| In a structured, open standard, non-machine-readable format
|| Standardised vocabularies/ontologies/schema without global identifiers
|| The metadata record includes URI links to related metadata, data and definitions
|| Standard text based license
|| Fully recorded in a text format
||
|-
| Local identifier
||
|| Brief title and description
|| Domain-specific repository
|| A de-identified / modified subset of the data is publicly accessible
|| File download from online location
|| Unsure
|| Mostly in a proprietary format
|| No standards have been applied in the description of data elements
|| There are no links to other metadata
|| Non-standard machine-readably license (clearly indicating under what conditions the data may be reused)
|| Partially recorded
||
|-
| No identifier
||
|| The data is not described
|| Local institutional repository
|| Embaged access after a specific date
|| By individual arrangement
||
||
|| Data elements not described
||
|| Non-standard text-based license
|| No provenance information is recorded
||
|-
|
||
||
|| The data is not described in any repository
|| Unspecified conditional access e.g. contact the data custodian for access
|| No access to data
||
||
||
||
|| No license
||
||
|-
|
||
||
||
|| Access to metadata only
||
||
||
||
||
||
||
||
|-
|
||
||
||
|| No access to data or metadata
||
||
||
||
||
||
||
||
|}

At this point it is important to note that the assessment of databases and/or datasets using this tool is solely considered a human assessment. This may lead to different evaluations by different users. To counteract this phenomenon, it was suggested that the evaluations should be completed using the 'four-eyes principle' to create more verifiable results. Also, it is planned to additionally evaluate the respective databases using an external tool to perform a machine-assessment.

In addition, not all questions are weighted equally and thus a worse result for one question may not have a large impact whereas only choosing the second-best option in another question will bring down the overall FAIR value considerably.

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ The ARDC tool provides detailed information about the requirements for each of the options of its tool. These descriptions are documented here.
|-
! rowspan="5" | F
|| F
|| About Findable
|| Making data Findable includes assigning a persistent identifier (like a DOI or Handle ), having rich metadata to describe the data and making sure it is findable through disciplinary and generalist discovery portals (local and international).

|-
| F1
|| About identifiers
|| Identifiers are essential for identifying, finding, retrieving, linking and citing datasets. A Web address (URL) can be used to specify the online location of a resource but over time URLs tend to change which leads to broken links to the data. To be useful, identifiers need to be persistent and unique. Digital Object Identifiers (DOIs) and other persistent identifiers (PIDs) provide a permanent citable reference to a particular dataset. The DOI is a permanent fixed reference to the dataset no matter where it is located online and enables citation and citation metrics. Services to create a persistent identifier are often offered by your affiliated institution or the repository you are using to describe your data. Talk to your library service for more information.
|-
| F2
|| About identifiers
|| The identifier (preferably a persistent identifier) needs to be clearly stated in the metadata record describing the data collection, and also in any associated data files or metadata.

|-
|F3
|| About metadata descriptions
|| Comprehensive metadata records will include descriptive content that facilitates discovery, access and reuse of the data being described. While there is no 'one size fits all' list, comprehensive metadata should include:
* a globally unique persistent identifier e.g. a DOI
* a title
* related people, i.e. the data creator or custodian
* how to access the data and file formats
* a description of how the data were created and how to interpret the data subject or keywords
* citation information that clearly indicates how the data should be cited
* a machine-readable data licence
* provenance and contextual information such as:
* links to related publications, projects, services and software
* methodology and processes involved in data production
* spatial and temporal coverage (if relevant)
* object-level data description

Providing metadata in a standard schema allows it to be read and used by machines as well as humans.

|-
| F4
|| About metadata repositories
|| A rich metadata description alone does not ensure a dataset’s ‘findability’ on the internet; the dataset needs to be registered or indexed in a searchable resource, such as a generalist (e.g. Figshare, Dryad, Zenodo domain-specific (e.g. PANGAEA, ADA, ICPSR), or institutional (e.g. ANU Research Repository , Sydney eScholarship Repository, data.gov.au) data repository or registry. Ideally these repositories/registries are indexed by search engines such as Google and/or Google Scholar.

|-
! rowspan = "4" | A
| A
|| About Accessible
|| To make data accessible may include making the data open using a standardised protocol. However the data does not necessarily have to be open. There are sometimes good reasons why data cannot be made open, for example privacy concerns, national security or commercial interests. If it is not open there should be clarity and transparency around the conditions governing access and reuse
|-
| A1
|| About data accessibility
|| Not all data that is discoverable can be freely accessed. Often there are embargoes, access controls, and access permissions associated with data due to a variety of issues such as privacy and commercial interests. Even with all these issues much sensitive data can be shared. Many other issues that could be perceived as blockers to sharing data may be overcome.
|-
| A1.1/A1.2
|| About protocols
|| Ideally users would like to retrieve appropriate internet content directly and unhindered once they have located it. Internet protocols (e.g. http and ftp) define rules and conventions for communication between devices, and tools and services are available to facilitate this process, e.g. APIs.

Protocols:
* [https://www.lifewire.com/hypertext-transfer-protocol-817944 HTTP] (Hypertext Transfer Protocol) is the set of rules for transferring files (text, graphic images, sound, video, and other multimedia files) on the [https://whatis.techtarget.com/definition/World-Wide-Web World Wide Web]. As soon as a Web user opens their Web browser, the user is indirectly making use of HTTP.
* [https://www.lifewire.com/file-transfer-protocol-817943 FTP] (File Transfer Protocol) is a standard Internet [http://searchnetworking.techtarget.com/definition/protocol protocol] for transmitting files between computers on the Internet over TCP/IP connections. Read more: [https://www.lifewire.com/definition-of-protocol-network-817949 Web protocols]
* API (Application Programming Interface): When information is made available in any machine readable format, it becomes possible to make that information directly available to programs that request that information over the web. An API is the way this information is made directly available to other machines.
View more: [https://www.youtube.com/watch?v=s7wmiS2mSXY What is an API?]

OGC - http://www.opengeospatial.org/ogc

|-
| A2
|| About metadata availability
|| Effort is often required to maintain data resources online which can often lead to it being neglected especially over long periods of time. This often leads to broken links between the metadata and the data. Having at least a description of the data in the form of a metadata record means there is a record of the data's existence allowing the possibility of someone tracking it down.

|-
! rowspan="4" | I
|| I
|| About Interoperability
|| To be interoperable (i.e. data that is interpretable by a computer, so that they can be automatically combined with other data) the data will need to use community agreed [https://www.ands.org.au/guides/file-formats formats], language and [https://www.ands.org.au/guides/vocabularies-and-research-data vocabularies]. The [https://www.ands.org.au/working-with-data/metadata metadata] will also need to use a community agreed standards and vocabularies, and contain links to related information using [https://www.ands.org.au/working-with-data/citation-and-identifiers identifiers].
|-
|| I1
|| About file formats
|| When selecting file formats for archiving, the formats should ideally be:
* Non-proprietary
* Unencrypted
* Uncompressed
* In common usage by the research community
* Adherent to an open, documented standard, such as described by the State of California (see [http://www.leginfo.ca.gov/pub/07-08/bill/asm/ab_1651-1700/ab_1668_bill_20070524_amended_asm_v96.pdf AB 1668, 2007])
** Interoperable among diverse platforms and applications
** Fully published and available royalty-free
** Fully and independently implementable by multiple software providers on multiple platforms without any intellectual property restrictions for necessary technology
** Developed and maintained by an open standards organization with a well-defined inclusive process for evolution of the standard.

From: [https://library.stanford.edu/research/data-management-services/data-best-practices/best-practices-file-formats Stanford University Library - Best practices for file formats]

File format examples:
* Structured, open standard, machine-readable format e.g. (text) PDF/A, HTML, Plain text, (images) TIFF, JPEG 2000, GIF, (audio) MP3, AIFF, WAVE, (video) MOV, MPEG, AVI, (Tabular data) CSV
* structured, open standard, non-machine-readable format, e.g. PDF, HTML, JPG
* Proprietary format, e.g. doc (Word), .xls (Excel), .ppt (PowerPoint), .sav

Read more:
* [https://www.ands.org.au/__data/assets/pdf_file/0003/731775/File-Formats.pdf ANDS]
* [https://www.europeandataportal.eu/elearning/en/module9/#/id/co-01 European Data Portal]

|-
|| I2
|| About schema standards
|| Schema standards are schemas that have gone through a formal validation process by a standards organisation, such as the International Standards Organisation (ISO) or an equivalent body such as the Dublin Core Metadata Initiative (DCMI); or, commonly used and consistently applied metadata schemas that are well documented, endorsed, and maintained can also become ‘de-facto standards’, e.g. RIF-CS for describing data collections and services used in Research Data Australia.

Read more: [https://www.ands.org.au/guides/metadata-working Metadata schema]

Standardised open and universal schemas, e.g, [https://www.dcc.ac.uk/resources/metadata-standards/datacite-metadata-schema DataCite Metadata Schema], [https://www.dcc.ac.uk/resources/metadata-standards/prov PROV], [https://www.dcc.ac.uk/resources/metadata-standards/dublin-core Dublin Core]

Domain-specific standards, e.g. [http://human-phenotype-ontology.github.io/ HPO - Human Phenotype Ontology], [https://www.nlm.nih.gov/mesh/ MeSH - Medical Subject Headings] , [https://mcp-profile-docs.readthedocs.io/en/stable/ Marine Community Profile], [https://ddialliance.org/ DDI - Data Documentation Initiative].

For more examples of standards: [http://rd-alliance.github.io/metadata-directory/ Here].

Resolvable global identifiers - An identifier is any label used to name something uniquely (whether online or offline). URLs are an example of an identifier. So are serial numbers, and personal names. A persistent identifier is guaranteed to be managed and kept up to date over a defined time period.

Read more: [https://www.ands.org.au/guides/persistent-identifiers-awareness Persistent Identifiers]

|-
||I3
|| About metadata links
|| The goal is to create as many meaningful linkages as possible between (meta)data resources to enrich the contextual knowledge about the data, balanced against the time/energy involved in making a good data model.

'''Machine-readable (meta)data'''

(Meta)data in a format that can be automatically read and processed by a computer, such as [http://opendatahandbook.org/glossary/en/terms/csv/ CSV], [http://opendatahandbook.org/glossary/en/terms/json/ JSON], [http://opendatahandbook.org/glossary/en/terms/xml/ XML] etc. For more information: [http://opendatahandbook.org/glossary/en/terms/machine-readable/ Open Data Handbook]

'''Linked Data'''

Linked Data (also known as Linking Data) can be applied to improve the exploitation of the “Web of data.” The expression refers to the publishing of structured data in a way that typed links are created between data from different sources to provide a higher level of usability. By using Linked Data, it is possible to find other, related data. Structured data should meet four requirements to be called
Linked Data:

* URIs should be assigned to all entities of the dataset.
* HTTP URIs are required to ensure that all entities can be referenced and cited by users and user agents.
* Entities should be described using standard formats such as RDF/XML.
* Links should be created to other, related entity URIs.

All data that fulfil these requirements and are released for the public are called Linked Open Data (LOD). http://www.lesliesikos.com/semantic-web-machine-readable-structured-data-with-meaningful-annotations/

Read more under the heading (meta)data include qualified references to other (meta)data in the following resource: https://www.dtls.nl/fair-data/fair-principles-explained/

For more information on Linked Data standards - RDF - https://www.w3.org/RDF/

Other related linked data standards:
* OWL - https://www.w3.org/OWL/
* SKOS - https://www.w3.org/2004/02/skos/
* SPARQL - https://www.w3.org/TR/rdf-sparql-query/

|-
! rowspan="3" | R
|| R
|| About Reusable
|| Reusable data should maintain its initial richness. For example, it should not be abridged for the purpose of explaining the findings in one particular publication. It needs a clear machine-readable [https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse licence] and [https://www.ands.org.au/partners-and-communities/ands-communities/data-provenance-interest-group provenance] information on how the data was formed. It should also use discipline-specific data and [https://www.ands.org.au/working-with-data/metadata metadata] standards to give it rich contextual information that will allow for accurate interpretation and reuse.

|-
|| R1.1
|| About licenses
|| If data is not licensed no-one else can use it. In Australia, no licence is regarded as the same as 'all rights reserved', confining any reuse to very limited circumstances. Applying a Creative Commons licence to your data is a simple way to ensure that your data can be reused. The less restrictive the licence, the more that can be done with the data.
To make it easy for the Web to know when a work is available under a particular license, a “machine readable” version of the license provides a summary of the key freedoms and obligations written into a format that software systems, search engines, and other kinds of technology can understand.
Example from the [https://creativecommons.org/choose/ Creative Commons License Choser]:

What appears in the metadata:

This work is licensed under a [https://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International License].

Code snippet:

<code>
<a rel="license" href="http://creativecommons.org/licenses/by/4.0/">

<img alt="Creative Commons License" style="border-width:0"

src="https://i.creativecommons.org/l/by/4.0/88x31.png"/>

</a> This work is licensed under a

<a rel="license" href="http://creativecommons.org/licenses/by/4.0/">

Creative Commons Attribution 4.0 International License</a>.
<\code>

Read more: https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse

|-
||R1.2
|| About provenance
|| Data provenance is used to document where a piece of data comes from and the process and methodology by which it is produced. It is important to confirm the authenticity of data enabling trust, credibility and reproducibility. This is becoming increasingly important, especially in the eScience community where research is data intensive and often involves complex data transformations and procedures ([https://www.ands.org.au/partners-and-communities/ands-communities/data-provenance-interest-group Read more]). Provenance vocabularies such as [https://www.w3.org/2005/Incubator/prov/XGR-prov-20101214/#Recommendations Provenir Ontology (PROV-O) and others].

|}

== Results for SMILES and ShareWind ==

{| class="mw-collapsible wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Datasets from the SMILES and ShareWind platforms was analysed using the self-assessment tool provided by ARDC.
! rowspan="5" |
| rowspan="5" |
! Database Code
| rowspan="24" style="background: #ABE" |
| ED4
| ED9
|-
! Database Title
|| SMILES
| ShareWind
|-
! Database Link
|| https://www.ecria-smiles.eu/
| https://sharewind.eu/
|-
! Sample Link
|| [https://www.ecria-smiles.eu/data-files/-/document_library/W32cCAfL2jMX/view_file664466?_com_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX_redirect=https%3A%2F%2Fwww.ecria-smiles.eu%3A443%2Fdata-files%2F-%2Fdocument_library%2FW32cCAfL2jMX%2Fview%2F664423%3F_com_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX_redirect%3Dhttps%253A%252F%252Fwww.ecria-smiles.eu%253A443%252Fdata-files%253Fp_p_id%253Dcom_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX%2526p_p_lifecycle%253D0%2526p_p_state%253Dnormal%2526p_p_mode%253Dview here] (link has 585 characters)
| https://sharewind.eu/records/f9d31abbc2824284b8c1d28894d9619a (data found at https://zenodo.org/record/1162738)
|-
! Sample Size
|| 4 KB
| 37 MB
|-
| colspan="6" style="background: #ABE" |
|-
! rowspan="4" | Findable
| F1. (meta)data are assigned a globally unique and persistent identifier
! Does the dataset have any identifiers assigned?
|| Web adress (URL)
| Globally unique, citable and persistant (e.g. DOI, PURL, ARK or Handle)
|-

| F2. data are described with rich metadata (defined by R1 below)
! Is the dataset identifier included in all metadata records/files describing the data?
|| No
| No
|-

| F3. metadata clearly and explicitly include the identifier of the data it describes
! How is the data described with metadata?
|| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
|-

| F4. (meta)data are registered or indexed in a searchable resource
! What type of repository or registry is the metadata record in?
|| Domain-specific repository
| Data is in one place but discoverable through several registries
|-

! rowspan="3" | Accessible

| A1. (meta)data are retrievable by their identifier using a standardized communications protocol
! How accessible is the data?
|| Publicly accessible
| Publicly accessible
|-
| A1.1 the protocol is open, free, and universally implementable; A1.2 the protocol allows for an authentication and authorization procedure, where necessary
! Is the data available online without requiring specialised protocols or tools once access has been approved?
|| File download from online location
| Standard web service API (e.g. OGC)
|-
| A2. metadata are accessible, even when the data are no longer available
! Will the metadata record be available even if the data is no longer available?
|| Unsure
| Yes
|-

! rowspan="3" | Interoperable
| I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
! What (file) format(s) is the data available in?
|| In a structured, open standard, machine-readable format
|| In a structured, open standard, non-machine-readable format
|-
| I2. (meta)data use vocabularies that follow FAIR principles
! What best describes the types of vocabularies/ontologies/tagging schemas used to define the data elements?
|| Standardised vocabularies/ontologies/schema without global identifiers
|| Standardised vocabularies/ontologies/schema without global identifiers
|-
| I3. (meta)data include qualified references to other (meta)data
! How is the metadata linked to other data and metadata (to enhance context and clearly indicate relationships)?
|| There are no links to other metadata
|| Metadata is represented in a machine readable format, e.g. in a linked data format such as Resource Description Framework (RDF)
|-

! rowspan="3" | Reusable
| R1.1. (meta)data are released with a clear and accessible data usage license
! Which of the following best describes the license/usage rights attached to the data?
|| No license
| Standard machine-readable license (e.g. Creative Commons)
|-
| R1.2. (meta)data are associated with detailed provenance
! How much provenance information has been captured to facilitate data reuse?
|| Partially recorded
|| Partially recorded
|-
| R1.3. (meta)data meet domain-relevant community standards
|
|
|
|-
| colspan="6" style="background: #ABE" |
|-
! Total across F.A.I.R
|
! Low: < 50%; Medium: 50-60%; High: > 60%
|| ~ 45 %
|| ~ 85 %
|-
| colspan="6" style="background: #ABE" |
|-
! Remarks about database
||
||
||
|| The data is provided by the open-source platform Zenodo, not the project website itself. A very positive aspect is that information on different versions of the data as well as the number of views and downloads is available.
|-
! Identified gaps
||
||
||
* No DOI, only URL
* Extremely long URL link spanning several lines with 585 characters, that can easily become corrupted
* No links to other metadata
* No license information given
* Provenance not fully recorded

||
* Dataset identifiers should be included in the files describing the data
* Provenance details are only partially recorded with a note with contact name for further information
|}

= Metadata assessment =

== Dublin Core Elements ==
{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ The DCMI Metadata Terms lists the current set of the Dublin Core vocabulary. Source: [https://dublincore.org/specifications/dublin-core/dcmi-terms/ Dublin Core Metadata Initiative]
| 1 || abstract || 11 || contributor || 21 || extent || 31 || isReplacedBy || 41 || publisher || 51 || tableOfContents
|-
| 2 || accessRights || 12 || coverage || 22 || format || 32 || isRequiredBy || 42 || references || 52 || temporal
|-
| 3 || accrualMethod || 13 || created || 23 || hasFormat || 33 || issued || 43 || relation || 53 || title
|-
| 4 || accrualPeriodicity || 14 || creator || 24 || hasPart || 34 || isVersionOf || 44 || replaces || 54 || type
|-
| 5 || accrualPolicy || 15 || date || 25 || hasVersion || 35 || language || 45 || requires || 55 || valid
|-
| 6 || alternative || 16 || dateAccepted || 26 || identifier || 36 || license || 46 || rights || ||
|-
| 7 || audience || 17 || dateCopyrighted || 27 || instructionalMethod || 37 || mediator || 47 || rightsHolder || ||
|-
| 8 || available || 18 || dateSubmitted || 28 || isFormatOf || 38 || medium || 48 || source || ||
|-
| 9 || bibliographicCitation || 19 || description || 29 || isPartOf || 39 || modified || 49 || spatial || ||
|-
| 10 || conformsTo || 20 || educationLevel || 30 || isReferencedBy || 40 || provenance || 50 || subject || ||
|}

'''Metadata Category Types according to [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO]'''

There are three main types of metadata:
* Descriptive metadata describes a resource for purposes such as discovery and identification. It can include elements such as title, abstract, author, and keywords.
* Structural metadata indicates how compound objects are put together, for example, how pages are ordered to form chapters.
* Administrative metadata provides information to help manage a resource, such as when and how it was created, file type and other technical information, and who can access it. There are several subsets of administrative data; two that sometimes are listed as separate metadata types are:
** Rights management meta-data, which deals with intellectual property rights,and
** Preservation metadata, which contains information needed to archive and preserve a resource.
''

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Simple Dublin Core Elements. 15 DC elements with their (shortened) official definitions and examples.
! DC element name !! DC definition ([https://www.dublincore.org/specifications/dublin-core/dces/ DCMI]) !! Example (Energy Domain) !! Category (determined with the aid of [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO])
|-
! Title
| A name given to the resource || Outdoor monitoring of a hybrid micro-CPV solar panel with integrated micro-tracking and diffuse capture || Descriptive
|-
! Subject
| The topic of the resource || concentrator photovoltaics; CPV; rooftop photovoltaics; integrated tracking || Descriptive
|-
! Description
| An account of the resource || Dataset from the outdoor characterization of a B Series module from Insolight at the rooftop of the Instituto de Energía Solar - Universidad Politécnica de Madrid. …. || Descriptive
|-
! Creator
| An entity primarily responsible for making the resource || Stephen Askins; Gaël Nardin || Descriptive
|-
! Publisher
| An entity responsible for making the resource available || Stephen Askins || Descriptive
|-
! Contributor
| An entity responsible for making contributions to the resource || Stephen Askins || Descriptive
|-
! Date
| A point or period of time associated with an event in the lifecycle of the resource || 2019-07-23; Date will be associated with the creation or availability of the resource. || Administrative
|-
! Type
| The nature or genre of the resource || info:eu-repo/semantics/other; dataset || Descriptive
|-
! Format
| The file format, physical medium, or dimensions of the resource || image
|| Structural
|-
! Identifier
| An unambiguous reference to the resource within a given context || https://zenodo.org/record/3346823; 10.5281/zenodo.3346823; oai:zenodo.org:3346823 || Descriptive
|-
! Source
| A related resource from which the described resource is derived || RC607.A26W574 1996 [where "RC607.A26W574 1996" is the call number of the print version of the resource, from which the present version was scanned] || Descriptive
|-
! Language
| A language of the resource || eng || Descriptive
|-
! Relation
| A related resource || info:eu-repo/grantAgreement/EC/H2020/787289/ || Descriptive
|-
! Coverage
| The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant || 1995-1996; Madrid, Spain || Descriptive
|-
! Rights
| Information about rights held in and over the resource || info:eu-repo/semantics/openAccess; https://creativecommons.org/licenses/by/4.0/legalcode || Administrative
|}

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Non-DC elements

! Non-DC element name !! Definition !! Example !! Category
|-
! Date of availability
| Since when is the data available
||
|| Impact/Exploitation

|-
! Number of users
| Is there any information on the number of accesses or users?
||
|| Impact/Exploitation

|-
! Number of scientific publications where data is cited/used
| Is there any information on the number of citations/uses?
||
|| Impact/Exploitation

|-
! Curation activities (is the versioning ongoing)
| Update, maintainance
||
|| Impact/Exploitation
|}

Attempting to put a value on Impact/Exploitation:
* '''ENTSO-E''': Found 172 results in Advanced Search (ALL) in Web of Science ([https://apps.webofknowledge.com/ WoSc]) (16/06/2020). Citations of all these search result add up to 714. This however does not mean, that all search results really explicitly used data from ENTSO-E. Some (for sure, for some were found) only mentioned it saying they adoped workflow etc. from the site.
* '''SMARD''': Found 82 results in Advanced Search (ALL=(SMARD)) in Web of Science ([https://apps.webofknowledge.com/ WoSc]) (16/06/2020). However: SMARD is also an acronym for ''Spinal muscular atrophy with respiratory distress (SMARD)''. This somewhat complicates results and needs some further discussion. All 82 search results accumumlated 2359 citations themselves.

Gap analysis

2020-08-28T09:37:58Z

Christopherbs: /* FAIR Self-Assessment tool */

<accesscontrol>Administrators,consortium</accesscontrol>

This is the page for the gap analysis.

= Tacit knowledge "FAIRification and opening of low carbon energy research data" =
 Consortium members can add any time, they feel that something is important to note even though it is not mentioned in a standard deliverable. In other words, this page collects uncodified, tacit knowledge. It will help us later to compile suggestions and lessons learned. This kind of knowledge is collected two-fold:
#Anytime if someone feels that this should be noted. Please write down: Issue, Date, Author (could also be "anonymous"), the issue described in a few words or maximal lines>
#During the final day of workshops

=== Learning process: ===

*People are hesitant to adopt new IT technologies, this is even the case among researchers heavily relying on data, algorithms and collaborative online software (e.g., R, platforms, online conferencing, HPC, ...). The effort to encourage change is not to underestimate. Reasons are several, notably, lack of time and uncertainty about potential benefit as well as overall risk aversion preferences. EERAdata is using the online software "Only Office" to facilitate collaboration (in particular also during the Covid-19 period).
 '''FAIR and open criteria:'''
*Consortium members have a fair understanding of what FAIR/O is, but there is little knowledge and/or technical experience on how to approach the FAIRification and opening. All, however, share the view that we are at a critical point in time, where we need to implement these criteria. 
*To deepen knowledge about FAIR/O criteria, it is useful to test the criteria on a database one is familiar with. For this purpose (and to start brainstorming about the platform), AIT has developed a questionnaire for application in the use cases.
'''Metadata:'''
*A good starting point is to think about metadata and to look into existing metadata concepts in one's field. The first step is to understand that also metadata need to adhere to the FAIR/O principles.
*The next step is to increase knowledge on IT specific terms, i.e. to understand what the difference is between different '''[https://energydata.onlyoffice.eu/Products/Files/doceditor.aspx?fileid=5404340 metadata frameworks] (taxonomy, thesaurus, ontology) as well as classification of metadata (high-level, medium-level, low-level OR administrative, structural and descriptive metadata).  '''WP 2 being in charge of aligning approaches between use cases, participates in all use case kickoffs to bring everybody on the same page. The presentation is linked with "metadata frameworks".
*It is useful to supply consortium members with read aheads and watch aheads on metadata to prepare the '''[https://energydata.onlyoffice.eu/Products/Files/doceditor.aspx?fileid=5296590 first EERAdata workshop]'''. The workshop starts applications and discussions in the use cases break out sessions (and bringing insights back to the plenary), using selected databases.

= FAIR Assessment=

== FAIR Guiding Principles ==
{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ This table shows the FAIR guiding principles described by [https://www.doi.org/10.1038/sdata.2016.18 Wilkinson et al., 2016].

! To be Findable
|
* F1. (meta)data are assigned a globally unique and persistent identifier
* F2. data are described with rich metadata (defined by R1 below)
* F3. metadata clearly and explicitly include the identifier of the data it describes
* F4. (meta)data are registered or indexed in a searchable resource

|-
! To be Accessible
|
* A1. (meta)data are retrievable by their identifier using a standardized communications protocol
** A1.1 the protocol is open, free, and universally implementable
** A1.2 the protocol allows for an authentication and authorization procedure, where necessary
* A2. metadata are accessible, even when the data are no longer available

|-
! To be Interoperable
|
* I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
* I2. (meta)data use vocabularies that follow FAIR principles
* I3. (meta)data include qualified references to other (meta)data

|-
! To be Reusable
|
* R1. meta(data) are richly described with a plurality of accurate and relevant attributes
* R1.1. (meta)data are released with a clear and accessible data usage license
* R1.2. (meta)data are associated with detailed provenance
* R1.3. (meta)data meet domain-relevant community standards

|}

== Identified gaps after Workshop 1 (02/06/2020 – 04/06/2020) ==

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ This table shows a summary of the specific issues that were identified in Workshop 1. The aim was to categorise different issues to get a better understanding of how to tackle these challenges.
! FAIR/O !! Specific FAIR/O !! Gaps !! Gaps for energy domain !! Use case specific gaps !! Consequences for researchers
|-
! F
| F2
|| Metadata scope
|| Different fields require different metadata (potentially very specific)
|| UC3: lack of additional metadata for applications of materials
|| Data are less useful for the specific field
|-
! F
|| F1/F3
|| Identifiers
||
* Missing identifiers on websites
* Identifier not in downloaded data files
||
|| Makes research more difficult.
|-
! I
| I2
|| Taxonomy/ontology/common vocabulary and language issues
||
* Lack of standardisation
* Heterogeneous data makes standardisation hard
* No vocabulary documentation on websites
* Words used for same term in other languages may differ
* Databases in other languages than English
||
|| Research costs more time
|-
! I
| I3
|| References to other (meta)data
||
* No linking to source documents and related publications (for contextual knowledge)
* Possible need for links to other fields
|| UC3: link between microscopic and macroscopic materials (e.g., turbine blades)
||
* Data cannot be connected to the source
* Makes research more difficult.

|-
! R
| R1.1
|| Licensing
||
* No licenses available may mean the data is not reusable for the researcher
* Licenses not clear and accessible
* Obtaining licenses may result in more effort and costs
||
|| Data potentially not reusable

|-
! R
| R1.2
|| Reliability and provenance of data
|| When the data is collected from different and high number of sources, its reliability decreases
||
|| Data potentially not reusable

|-
! O
|
|| Privacy concerns and expected disadvantages
|| Not publishing data due to privacy concerns (sensitive data)
|| UC2: In case of distribution network data this is relevant
|| Data not accessible

|-
! Other
|
|| Conducting FAIR assessments
||
* No qualitative assessment of the data itself (which may be limited)
* Discrepancies between results conducted by humans versus machines
* Sometimes not even DC standards are met
* Metadata is not updated
* Lacking encryption of websites (https)
* Problems assessing whether metadata will be available after data is unavailable
* Interface design/layout may be unclear, incomplete or not intuitive for humans
* Authentication details (user registration login / good or bad, clarify)

||
* UC1 lack of data availability for time-series
||
* Assessment is uncertain (human assessment may need more clarification and understanding)
* Makes research more time-intensive

|}

== FAIR Self-Assessment tool ==

To conduct a human FAIR evaluation for different databases and their respective datasets, different free online tools were considered. Some seemed to be quite useful (e.g. [https://satifyd.dans.knaw.nl/ SATISFYD] ) and some were simple self-evaluation PDFs to be printed and filled out.

We decided to chose the [https://ardc.edu.au/resources/working-with-data/fair-data/fair-self-assessment-tool/ self-assessment tool] provided by the Australian Research Data Commons (ARDC). The decision was based manly upon the fact that the tools questions closely matched the FAIR categories specified by [https://www.doi.org/10.1038/sdata.2016.18 Wilkinson et al., 2016] which would make it easily comparable to the existing preferable FAIR conditions and aid with the repeatability of the results, and the half-automated scoring features that show the extent of the "FAIRness" of the analysed sample as a simple bar chart.

The result of the FAIR assessment is provided as a bar chart where an empty bar chart displays 0 % FAIR compatibility, and a full bar chart represents 100 % FAIR compatibility. The score is therefore not returned as a specific value. During the assessment below, the "Total Across FAIR" in Table ... was estimated according to the level of the bar chart from 0 % to 100 %.
Resulting scores where categorised as follows: Low: < 50%; Medium: 50-60%; High: > 60% .

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ All options for each question of the ARDCs self-assessment tool
!colspan="4"|Findable
!colspan="3"|Accessible
!colspan="3"|Interoperable
!colspan="3"|Reusable
|-
| F1. (meta)data are assigned a globally unique and persistent identifier
|| F2. data are described with rich metadata (defined by R1 below)
|| F3. metadata clearly and explicitly include the identifier of the data it describes
|| F4. (meta)data are registered or indexed in a searchable resource
|| A1. (meta)data are retrievable by their identifier using a standardized communications protocol
|| A1.1 the protocol is open, free, and universally implementable and A1.2 the protocol allows for an authentication and authorization procedure, where necessary
|| A2. metadata are accessible, even when the data are no longer available
|| I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
|| I2. (meta)data use vocabularies that follow FAIR principles
|| I3. (meta)data include qualified references to other (meta)data
|| R1.1. (meta)data are released with a clear and accessible data usage license
|| R1.2. (meta)data are associated with detailed provenance
|| R1.3. (meta)data meet domain-relevant community standards
|-
! Does the dataset have any identifiers assigned?
! Is the dataset identifier included in all metadata records/files describing the data?
! How is the data described with metadata?
! What type of repository or registry is the metadata record in?
! How accessible is the data?
! Is the data available online without requiring specialised protocols or tools once access has been approved?
! Will the metadata record be available even if the data is no longer available?
! What (file) format(s) is the data available in?
! What best describes the types of vocabularies/ontologies/tagging schemas used to define the data elements?
! How is the metadata linked to other data and metadata (to enhance context and clearly indicate relationships)?
! Which of the following best describes the license/usage rights attached to the data?
! How much provenance information has been captured to facilitate data reuse?
!
|-
| Globally unique, citable and persistant (e.g. DOI, PURL, ARK or Handle)
|| Yes
|| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
|| Data is in one place but discoverable through several registries
|| Publicly accessible
|| Standard web service API (e.g. OGC)
|| Yes
|| In a structured, open standard, machine-readable format
|| Standardised open and universal using resolvable global identifiers linking to explainations
|| Meadata is represented in a machine readable format, e.g. in a linked data format such as Resource Description Framework (RDF)
|| Standard machine-readable license (e.g. Creative Commons)
|| Fully recorded in a machine-readable format
||
|-
| Web adress (URL)
|| No
|| Comprehensively, but in a text-based, non-standard format
|| Generalist public repository
|| Fully accessible to person who meet explicitly stated conditions, e.g. ethics approval for sensitive data
|| Non-standard web service (e.g. OpenAPI/Swagger/informal API)
|| No
|| In a structured, open standard, non-machine-readable format
|| Standardised vocabularies/ontologies/schema without global identifiers
|| The metadata record includes URI links to related metadata, data and definitions
|| Standard text based license
|| Fully recorded in a text format
||
|-
| Local identifier
||
|| Brief title and description
|| Domain-specific repository
|| A de-identified / modified subset of the data is publicly accessible
|| File download from online location
|| Unsure
|| Mostly in a proprietary format
|| No standards have been applied in the description of data elements
|| There are no links to other metadata
|| Non-standard machine-readably license (clearly indicating under what conditions the data may be reused)
|| Partially recorded
||
|-
| No identifier
||
|| The data is not described
|| Local institutional repository
|| Embaged access after a specific date
|| By individual arrangement
||
||
|| Data elements not described
||
|| Non-standard text-based license
|| No provenance information is recorded
||
|-
|
||
||
|| The data is not described in any repository
|| Unspecified conditional access e.g. contact the data custodian for access
|| No access to data
||
||
||
||
|| No license
||
||
|-
|
||
||
||
|| Access to metadata only
||
||
||
||
||
||
||
||
|-
|
||
||
||
|| No access to data or metadata
||
||
||
||
||
||
||
||
|}

At this point it is important to note that the assessment of databases and/or datasets using this tool is solely considered a human assessment. This may lead to different evaluations by different users. To counteract this phenomenon, it was suggested that the evaluations should be completed using the 'four-eyes principle' to create more verifiable results. Also, it is planned to additionally evaluate the respective databases using an external tool to perform a machine-assessment.

In addition, not all questions are weighted equally and thus a worse result for one question may not have a large impact whereas only choosing the second-best option in another question will bring down the overall FAIR value considerably.

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ The ARDC tool provides detailed information about the requirements for each of the options of its tool. These descriptions are documented here.
|-
! rowspan="5" | F
|| F
|| About Findable
|| Making data Findable includes assigning a persistent identifier (like a DOI or Handle ), having rich metadata to describe the data and making sure it is findable through disciplinary and generalist discovery portals (local and international).

|-
| F1
|| About identifiers
|| Identifiers are essential for identifying, finding, retrieving, linking and citing datasets. A Web address (URL) can be used to specify the online location of a resource but over time URLs tend to change which leads to broken links to the data. To be useful, identifiers need to be persistent and unique. Digital Object Identifiers (DOIs) and other persistent identifiers (PIDs) provide a permanent citable reference to a particular dataset. The DOI is a permanent fixed reference to the dataset no matter where it is located online and enables citation and citation metrics. Services to create a persistent identifier are often offered by your affiliated institution or the repository you are using to describe your data. Talk to your library service for more information.
|-
| F2
|| About identifiers
|| The identifier (preferably a persistent identifier) needs to be clearly stated in the metadata record describing the data collection, and also in any associated data files or metadata.

|-
|F3
|| About metadata descriptions
|| Comprehensive metadata records will include descriptive content that facilitates discovery, access and reuse of the data being described. While there is no 'one size fits all' list, comprehensive metadata should include:
* a globally unique persistent identifier e.g. a DOI
* a title
* related people, i.e. the data creator or custodian
* how to access the data and file formats
* a description of how the data were created and how to interpret the data subject or keywords
* citation information that clearly indicates how the data should be cited
* a machine-readable data licence
* provenance and contextual information such as:
* links to related publications, projects, services and software
* methodology and processes involved in data production
* spatial and temporal coverage (if relevant)
* object-level data description

Providing metadata in a standard schema allows it to be read and used by machines as well as humans.

|-
| F4
|| About metadata repositories
|| A rich metadata description alone does not ensure a dataset’s ‘findability’ on the internet; the dataset needs to be registered or indexed in a searchable resource, such as a generalist (e.g. Figshare, Dryad, Zenodo domain-specific (e.g. PANGAEA, ADA, ICPSR), or institutional (e.g. ANU Research Repository , Sydney eScholarship Repository, data.gov.au) data repository or registry. Ideally these repositories/registries are indexed by search engines such as Google and/or Google Scholar.

|-
! rowspan = "4" | A
| A
|| About Accessible
|| To make data accessible may include making the data open using a standardised protocol. However the data does not necessarily have to be open. There are sometimes good reasons why data cannot be made open, for example privacy concerns, national security or commercial interests. If it is not open there should be clarity and transparency around the conditions governing access and reuse
|-
| A1
|| About data accessibility
|| Not all data that is discoverable can be freely accessed. Often there are embargoes, access controls, and access permissions associated with data due to a variety of issues such as privacy and commercial interests. Even with all these issues much sensitive data can be shared. Many other issues that could be perceived as blockers to sharing data may be overcome.
|-
| A1.1/A1.2
|| About protocols
|| Ideally users would like to retrieve appropriate internet content directly and unhindered once they have located it. Internet protocols (e.g. http and ftp) define rules and conventions for communication between devices, and tools and services are available to facilitate this process, e.g. APIs.

Protocols:
* [https://www.lifewire.com/hypertext-transfer-protocol-817944 HTTP] (Hypertext Transfer Protocol) is the set of rules for transferring files (text, graphic images, sound, video, and other multimedia files) on the [https://whatis.techtarget.com/definition/World-Wide-Web World Wide Web]. As soon as a Web user opens their Web browser, the user is indirectly making use of HTTP.
* [https://www.lifewire.com/file-transfer-protocol-817943 FTP] (File Transfer Protocol) is a standard Internet [http://searchnetworking.techtarget.com/definition/protocol protocol] for transmitting files between computers on the Internet over TCP/IP connections. Read more: [https://www.lifewire.com/definition-of-protocol-network-817949 Web protocols]
* API (Application Programming Interface): When information is made available in any machine readable format, it becomes possible to make that information directly available to programs that request that information over the web. An API is the way this information is made directly available to other machines.
View more: [https://youtube.com/s7wmiS2mSXY What is an API?]

OGC - http://www.opengeospatial.org/ogc

|-
| A2
|| About metadata availability
|| Effort is often required to maintain data resources online which can often lead to it being neglected especially over long periods of time. This often leads to broken links between the metadata and the data. Having at least a description of the data in the form of a metadata record means there is a record of the data's existence allowing the possibility of someone tracking it down.

|-
! rowspan="4" | I
|| I
|| About Interoperability
|| To be interoperable (i.e. data that is interpretable by a computer, so that they can be automatically combined with other data) the data will need to use community agreed [https://www.ands.org.au/guides/file-formats formats], language and [https://www.ands.org.au/guides/vocabularies-and-research-data vocabularies]. The [https://www.ands.org.au/working-with-data/metadata metadata] will also need to use a community agreed standards and vocabularies, and contain links to related information using [https://www.ands.org.au/working-with-data/citation-and-identifiers identifiers].
|-
|| I1
|| About file formats
|| When selecting file formats for archiving, the formats should ideally be:
* Non-proprietary
* Unencrypted
* Uncompressed
* In common usage by the research community
* Adherent to an open, documented standard, such as described by the State of California (see [http://www.leginfo.ca.gov/pub/07-08/bill/asm/ab_1651-1700/ab_1668_bill_20070524_amended_asm_v96.pdf AB 1668, 2007])
** Interoperable among diverse platforms and applications
** Fully published and available royalty-free
** Fully and independently implementable by multiple software providers on multiple platforms without any intellectual property restrictions for necessary technology
** Developed and maintained by an open standards organization with a well-defined inclusive process for evolution of the standard.

From: [https://library.stanford.edu/research/data-management-services/data-best-practices/best-practices-file-formats Stanford University Library - Best practices for file formats]

File format examples:
* Structured, open standard, machine-readable format e.g. (text) PDF/A, HTML, Plain text, (images) TIFF, JPEG 2000, GIF, (audio) MP3, AIFF, WAVE, (video) MOV, MPEG, AVI, (Tabular data) CSV
* structured, open standard, non-machine-readable format, e.g. PDF, HTML, JPG
* Proprietary format, e.g. doc (Word), .xls (Excel), .ppt (PowerPoint), .sav

Read more:
* [https://www.ands.org.au/__data/assets/pdf_file/0003/731775/File-Formats.pdf ANDS]
* [https://www.europeandataportal.eu/elearning/en/module9/#/id/co-01 European Data Portal]

|-
|| I2
|| About schema standards
|| Schema standards are schemas that have gone through a formal validation process by a standards organisation, such as the International Standards Organisation (ISO) or an equivalent body such as the Dublin Core Metadata Initiative (DCMI); or, commonly used and consistently applied metadata schemas that are well documented, endorsed, and maintained can also become ‘de-facto standards’, e.g. RIF-CS for describing data collections and services used in Research Data Australia.

Read more: [https://www.ands.org.au/guides/metadata-working Metadata schema]

Standardised open and universal schemas, e.g, [https://www.dcc.ac.uk/resources/metadata-standards/datacite-metadata-schema DataCite Metadata Schema], [https://www.dcc.ac.uk/resources/metadata-standards/prov PROV], [https://www.dcc.ac.uk/resources/metadata-standards/dublin-core Dublin Core]

Domain-specific standards, e.g. [http://human-phenotype-ontology.github.io/ HPO - Human Phenotype Ontology], [https://www.nlm.nih.gov/mesh/ MeSH - Medical Subject Headings] , [https://mcp-profile-docs.readthedocs.io/en/stable/ Marine Community Profile], [https://ddialliance.org/ DDI - Data Documentation Initiative].

For more examples of standards: [http://rd-alliance.github.io/metadata-directory/ Here].

Resolvable global identifiers - An identifier is any label used to name something uniquely (whether online or offline). URLs are an example of an identifier. So are serial numbers, and personal names. A persistent identifier is guaranteed to be managed and kept up to date over a defined time period.

Read more: [https://www.ands.org.au/guides/persistent-identifiers-awareness Persistent Identifiers]

|-
||I3
|| About metadata links
|| The goal is to create as many meaningful linkages as possible between (meta)data resources to enrich the contextual knowledge about the data, balanced against the time/energy involved in making a good data model.

'''Machine-readable (meta)data'''

(Meta)data in a format that can be automatically read and processed by a computer, such as [http://opendatahandbook.org/glossary/en/terms/csv/ CSV], [http://opendatahandbook.org/glossary/en/terms/json/ JSON], [http://opendatahandbook.org/glossary/en/terms/xml/ XML] etc. For more information: [http://opendatahandbook.org/glossary/en/terms/machine-readable/ Open Data Handbook]

'''Linked Data'''

Linked Data (also known as Linking Data) can be applied to improve the exploitation of the “Web of data.” The expression refers to the publishing of structured data in a way that typed links are created between data from different sources to provide a higher level of usability. By using Linked Data, it is possible to find other, related data. Structured data should meet four requirements to be called
Linked Data:

* URIs should be assigned to all entities of the dataset.
* HTTP URIs are required to ensure that all entities can be referenced and cited by users and user agents.
* Entities should be described using standard formats such as RDF/XML.
* Links should be created to other, related entity URIs.

All data that fulfil these requirements and are released for the public are called Linked Open Data (LOD). http://www.lesliesikos.com/semantic-web-machine-readable-structured-data-with-meaningful-annotations/

Read more under the heading (meta)data include qualified references to other (meta)data in the following resource: https://www.dtls.nl/fair-data/fair-principles-explained/

For more information on Linked Data standards - RDF - https://www.w3.org/RDF/

Other related linked data standards:
* OWL - https://www.w3.org/OWL/
* SKOS - https://www.w3.org/2004/02/skos/
* SPARQL - https://www.w3.org/TR/rdf-sparql-query/

|-
! rowspan="3" | R
|| R
|| About Reusable
|| Reusable data should maintain its initial richness. For example, it should not be abridged for the purpose of explaining the findings in one particular publication. It needs a clear machine-readable [https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse licence] and [https://www.ands.org.au/partners-and-communities/ands-communities/data-provenance-interest-group provenance] information on how the data was formed. It should also use discipline-specific data and [https://www.ands.org.au/working-with-data/metadata metadata] standards to give it rich contextual information that will allow for accurate interpretation and reuse.

|-
|| R1.1
|| About licenses
|| If data is not licensed no-one else can use it. In Australia, no licence is regarded as the same as 'all rights reserved', confining any reuse to very limited circumstances. Applying a Creative Commons licence to your data is a simple way to ensure that your data can be reused. The less restrictive the licence, the more that can be done with the data.
To make it easy for the Web to know when a work is available under a particular license, a “machine readable” version of the license provides a summary of the key freedoms and obligations written into a format that software systems, search engines, and other kinds of technology can understand.
Example from the [https://creativecommons.org/choose/ Creative Commons License Choser]:

What appears in the metadata:

This work is licensed under a [https://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International License].

Code snippet:

<code>
<a rel="license" href="http://creativecommons.org/licenses/by/4.0/">

<img alt="Creative Commons License" style="border-width:0"

src="https://i.creativecommons.org/l/by/4.0/88x31.png"/>

</a> This work is licensed under a

<a rel="license" href="http://creativecommons.org/licenses/by/4.0/">

Creative Commons Attribution 4.0 International License</a>.
<\code>

Read more: https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse

|-
||R1.2
|| About provenance
|| Data provenance is used to document where a piece of data comes from and the process and methodology by which it is produced. It is important to confirm the authenticity of data enabling trust, credibility and reproducibility. This is becoming increasingly important, especially in the eScience community where research is data intensive and often involves complex data transformations and procedures ([https://www.ands.org.au/partners-and-communities/ands-communities/data-provenance-interest-group Read more]). Provenance vocabularies such as [https://www.w3.org/2005/Incubator/prov/XGR-prov-20101214/#Recommendations Provenir Ontology (PROV-O) and others].

|}

== Results for SMILES and ShareWind ==

{| class="mw-collapsible wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Datasets from the SMILES and ShareWind platforms was analysed using the self-assessment tool provided by ARDC.
! rowspan="5" |
| rowspan="5" |
! Database Code
| rowspan="24" style="background: #ABE" |
| ED4
| ED9
|-
! Database Title
|| SMILES
| ShareWind
|-
! Database Link
|| https://www.ecria-smiles.eu/
| https://sharewind.eu/
|-
! Sample Link
|| [https://www.ecria-smiles.eu/data-files/-/document_library/W32cCAfL2jMX/view_file664466?_com_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX_redirect=https%3A%2F%2Fwww.ecria-smiles.eu%3A443%2Fdata-files%2F-%2Fdocument_library%2FW32cCAfL2jMX%2Fview%2F664423%3F_com_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX_redirect%3Dhttps%253A%252F%252Fwww.ecria-smiles.eu%253A443%252Fdata-files%253Fp_p_id%253Dcom_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX%2526p_p_lifecycle%253D0%2526p_p_state%253Dnormal%2526p_p_mode%253Dview here] (link has 585 characters)
| https://sharewind.eu/records/f9d31abbc2824284b8c1d28894d9619a (data found at https://zenodo.org/record/1162738)
|-
! Sample Size
|| 4 KB
| 37 MB
|-
| colspan="6" style="background: #ABE" |
|-
! rowspan="4" | Findable
| F1. (meta)data are assigned a globally unique and persistent identifier
! Does the dataset have any identifiers assigned?
|| Web adress (URL)
| Globally unique, citable and persistant (e.g. DOI, PURL, ARK or Handle)
|-

| F2. data are described with rich metadata (defined by R1 below)
! Is the dataset identifier included in all metadata records/files describing the data?
|| No
| No
|-

| F3. metadata clearly and explicitly include the identifier of the data it describes
! How is the data described with metadata?
|| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
|-

| F4. (meta)data are registered or indexed in a searchable resource
! What type of repository or registry is the metadata record in?
|| Domain-specific repository
| Data is in one place but discoverable through several registries
|-

! rowspan="3" | Accessible

| A1. (meta)data are retrievable by their identifier using a standardized communications protocol
! How accessible is the data?
|| Publicly accessible
| Publicly accessible
|-
| A1.1 the protocol is open, free, and universally implementable; A1.2 the protocol allows for an authentication and authorization procedure, where necessary
! Is the data available online without requiring specialised protocols or tools once access has been approved?
|| File download from online location
| Standard web service API (e.g. OGC)
|-
| A2. metadata are accessible, even when the data are no longer available
! Will the metadata record be available even if the data is no longer available?
|| Unsure
| Yes
|-

! rowspan="3" | Interoperable
| I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
! What (file) format(s) is the data available in?
|| In a structured, open standard, machine-readable format
|| In a structured, open standard, non-machine-readable format
|-
| I2. (meta)data use vocabularies that follow FAIR principles
! What best describes the types of vocabularies/ontologies/tagging schemas used to define the data elements?
|| Standardised vocabularies/ontologies/schema without global identifiers
|| Standardised vocabularies/ontologies/schema without global identifiers
|-
| I3. (meta)data include qualified references to other (meta)data
! How is the metadata linked to other data and metadata (to enhance context and clearly indicate relationships)?
|| There are no links to other metadata
|| Metadata is represented in a machine readable format, e.g. in a linked data format such as Resource Description Framework (RDF)
|-

! rowspan="3" | Reusable
| R1.1. (meta)data are released with a clear and accessible data usage license
! Which of the following best describes the license/usage rights attached to the data?
|| No license
| Standard machine-readable license (e.g. Creative Commons)
|-
| R1.2. (meta)data are associated with detailed provenance
! How much provenance information has been captured to facilitate data reuse?
|| Partially recorded
|| Partially recorded
|-
| R1.3. (meta)data meet domain-relevant community standards
|
|
|
|-
| colspan="6" style="background: #ABE" |
|-
! Total across F.A.I.R
|
! Low: < 50%; Medium: 50-60%; High: > 60%
|| ~ 45 %
|| ~ 85 %
|-
| colspan="6" style="background: #ABE" |
|-
! Remarks about database
||
||
||
|| The data is provided by the open-source platform Zenodo, not the project website itself. A very positive aspect is that information on different versions of the data as well as the number of views and downloads is available.
|-
! Identified gaps
||
||
||
* No DOI, only URL
* Extremely long URL link spanning several lines with 585 characters, that can easily become corrupted
* No links to other metadata
* No license information given
* Provenance not fully recorded

||
* Dataset identifiers should be included in the files describing the data
* Provenance details are only partially recorded with a note with contact name for further information
|}

= Metadata assessment =

== Dublin Core Elements ==
{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ The DCMI Metadata Terms lists the current set of the Dublin Core vocabulary. Source: [https://dublincore.org/specifications/dublin-core/dcmi-terms/ Dublin Core Metadata Initiative]
| 1 || abstract || 11 || contributor || 21 || extent || 31 || isReplacedBy || 41 || publisher || 51 || tableOfContents
|-
| 2 || accessRights || 12 || coverage || 22 || format || 32 || isRequiredBy || 42 || references || 52 || temporal
|-
| 3 || accrualMethod || 13 || created || 23 || hasFormat || 33 || issued || 43 || relation || 53 || title
|-
| 4 || accrualPeriodicity || 14 || creator || 24 || hasPart || 34 || isVersionOf || 44 || replaces || 54 || type
|-
| 5 || accrualPolicy || 15 || date || 25 || hasVersion || 35 || language || 45 || requires || 55 || valid
|-
| 6 || alternative || 16 || dateAccepted || 26 || identifier || 36 || license || 46 || rights || ||
|-
| 7 || audience || 17 || dateCopyrighted || 27 || instructionalMethod || 37 || mediator || 47 || rightsHolder || ||
|-
| 8 || available || 18 || dateSubmitted || 28 || isFormatOf || 38 || medium || 48 || source || ||
|-
| 9 || bibliographicCitation || 19 || description || 29 || isPartOf || 39 || modified || 49 || spatial || ||
|-
| 10 || conformsTo || 20 || educationLevel || 30 || isReferencedBy || 40 || provenance || 50 || subject || ||
|}

'''Metadata Category Types according to [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO]'''

There are three main types of metadata:
* Descriptive metadata describes a resource for purposes such as discovery and identification. It can include elements such as title, abstract, author, and keywords.
* Structural metadata indicates how compound objects are put together, for example, how pages are ordered to form chapters.
* Administrative metadata provides information to help manage a resource, such as when and how it was created, file type and other technical information, and who can access it. There are several subsets of administrative data; two that sometimes are listed as separate metadata types are:
** Rights management meta-data, which deals with intellectual property rights,and
** Preservation metadata, which contains information needed to archive and preserve a resource.
''

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Simple Dublin Core Elements. 15 DC elements with their (shortened) official definitions and examples.
! DC element name !! DC definition ([https://www.dublincore.org/specifications/dublin-core/dces/ DCMI]) !! Example (Energy Domain) !! Category (determined with the aid of [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO])
|-
! Title
| A name given to the resource || Outdoor monitoring of a hybrid micro-CPV solar panel with integrated micro-tracking and diffuse capture || Descriptive
|-
! Subject
| The topic of the resource || concentrator photovoltaics; CPV; rooftop photovoltaics; integrated tracking || Descriptive
|-
! Description
| An account of the resource || Dataset from the outdoor characterization of a B Series module from Insolight at the rooftop of the Instituto de Energía Solar - Universidad Politécnica de Madrid. …. || Descriptive
|-
! Creator
| An entity primarily responsible for making the resource || Stephen Askins; Gaël Nardin || Descriptive
|-
! Publisher
| An entity responsible for making the resource available || Stephen Askins || Descriptive
|-
! Contributor
| An entity responsible for making contributions to the resource || Stephen Askins || Descriptive
|-
! Date
| A point or period of time associated with an event in the lifecycle of the resource || 2019-07-23; Date will be associated with the creation or availability of the resource. || Administrative
|-
! Type
| The nature or genre of the resource || info:eu-repo/semantics/other; dataset || Descriptive
|-
! Format
| The file format, physical medium, or dimensions of the resource || image
|| Structural
|-
! Identifier
| An unambiguous reference to the resource within a given context || https://zenodo.org/record/3346823; 10.5281/zenodo.3346823; oai:zenodo.org:3346823 || Descriptive
|-
! Source
| A related resource from which the described resource is derived || RC607.A26W574 1996 [where "RC607.A26W574 1996" is the call number of the print version of the resource, from which the present version was scanned] || Descriptive
|-
! Language
| A language of the resource || eng || Descriptive
|-
! Relation
| A related resource || info:eu-repo/grantAgreement/EC/H2020/787289/ || Descriptive
|-
! Coverage
| The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant || 1995-1996; Madrid, Spain || Descriptive
|-
! Rights
| Information about rights held in and over the resource || info:eu-repo/semantics/openAccess; https://creativecommons.org/licenses/by/4.0/legalcode || Administrative
|}

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Non-DC elements

! Non-DC element name !! Definition !! Example !! Category
|-
! Date of availability
| Since when is the data available
||
|| Impact/Exploitation

|-
! Number of users
| Is there any information on the number of accesses or users?
||
|| Impact/Exploitation

|-
! Number of scientific publications where data is cited/used
| Is there any information on the number of citations/uses?
||
|| Impact/Exploitation

|-
! Curation activities (is the versioning ongoing)
| Update, maintainance
||
|| Impact/Exploitation
|}

Attempting to put a value on Impact/Exploitation:
* '''ENTSO-E''': Found 172 results in Advanced Search (ALL) in Web of Science ([https://apps.webofknowledge.com/ WoSc]) (16/06/2020). Citations of all these search result add up to 714. This however does not mean, that all search results really explicitly used data from ENTSO-E. Some (for sure, for some were found) only mentioned it saying they adoped workflow etc. from the site.
* '''SMARD''': Found 82 results in Advanced Search (ALL=(SMARD)) in Web of Science ([https://apps.webofknowledge.com/ WoSc]) (16/06/2020). However: SMARD is also an acronym for ''Spinal muscular atrophy with respiratory distress (SMARD)''. This somewhat complicates results and needs some further discussion. All 82 search results accumumlated 2359 citations themselves.

Gap analysis

2020-08-28T09:32:34Z

Christopherbs: /* FAIR Self-Assessment tool */

<accesscontrol>Administrators,consortium</accesscontrol>

This is the page for the gap analysis.

= Tacit knowledge "FAIRification and opening of low carbon energy research data" =
 Consortium members can add any time, they feel that something is important to note even though it is not mentioned in a standard deliverable. In other words, this page collects uncodified, tacit knowledge. It will help us later to compile suggestions and lessons learned. This kind of knowledge is collected two-fold:
#Anytime if someone feels that this should be noted. Please write down: Issue, Date, Author (could also be "anonymous"), the issue described in a few words or maximal lines>
#During the final day of workshops

=== Learning process: ===

*People are hesitant to adopt new IT technologies, this is even the case among researchers heavily relying on data, algorithms and collaborative online software (e.g., R, platforms, online conferencing, HPC, ...). The effort to encourage change is not to underestimate. Reasons are several, notably, lack of time and uncertainty about potential benefit as well as overall risk aversion preferences. EERAdata is using the online software "Only Office" to facilitate collaboration (in particular also during the Covid-19 period).
 '''FAIR and open criteria:'''
*Consortium members have a fair understanding of what FAIR/O is, but there is little knowledge and/or technical experience on how to approach the FAIRification and opening. All, however, share the view that we are at a critical point in time, where we need to implement these criteria. 
*To deepen knowledge about FAIR/O criteria, it is useful to test the criteria on a database one is familiar with. For this purpose (and to start brainstorming about the platform), AIT has developed a questionnaire for application in the use cases.
'''Metadata:'''
*A good starting point is to think about metadata and to look into existing metadata concepts in one's field. The first step is to understand that also metadata need to adhere to the FAIR/O principles.
*The next step is to increase knowledge on IT specific terms, i.e. to understand what the difference is between different '''[https://energydata.onlyoffice.eu/Products/Files/doceditor.aspx?fileid=5404340 metadata frameworks] (taxonomy, thesaurus, ontology) as well as classification of metadata (high-level, medium-level, low-level OR administrative, structural and descriptive metadata).  '''WP 2 being in charge of aligning approaches between use cases, participates in all use case kickoffs to bring everybody on the same page. The presentation is linked with "metadata frameworks".
*It is useful to supply consortium members with read aheads and watch aheads on metadata to prepare the '''[https://energydata.onlyoffice.eu/Products/Files/doceditor.aspx?fileid=5296590 first EERAdata workshop]'''. The workshop starts applications and discussions in the use cases break out sessions (and bringing insights back to the plenary), using selected databases.

= FAIR Assessment=

== FAIR Guiding Principles ==
{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ This table shows the FAIR guiding principles described by [https://www.doi.org/10.1038/sdata.2016.18 Wilkinson et al., 2016].

! To be Findable
|
* F1. (meta)data are assigned a globally unique and persistent identifier
* F2. data are described with rich metadata (defined by R1 below)
* F3. metadata clearly and explicitly include the identifier of the data it describes
* F4. (meta)data are registered or indexed in a searchable resource

|-
! To be Accessible
|
* A1. (meta)data are retrievable by their identifier using a standardized communications protocol
** A1.1 the protocol is open, free, and universally implementable
** A1.2 the protocol allows for an authentication and authorization procedure, where necessary
* A2. metadata are accessible, even when the data are no longer available

|-
! To be Interoperable
|
* I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
* I2. (meta)data use vocabularies that follow FAIR principles
* I3. (meta)data include qualified references to other (meta)data

|-
! To be Reusable
|
* R1. meta(data) are richly described with a plurality of accurate and relevant attributes
* R1.1. (meta)data are released with a clear and accessible data usage license
* R1.2. (meta)data are associated with detailed provenance
* R1.3. (meta)data meet domain-relevant community standards

|}

== Identified gaps after Workshop 1 (02/06/2020 – 04/06/2020) ==

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ This table shows a summary of the specific issues that were identified in Workshop 1. The aim was to categorise different issues to get a better understanding of how to tackle these challenges.
! FAIR/O !! Specific FAIR/O !! Gaps !! Gaps for energy domain !! Use case specific gaps !! Consequences for researchers
|-
! F
| F2
|| Metadata scope
|| Different fields require different metadata (potentially very specific)
|| UC3: lack of additional metadata for applications of materials
|| Data are less useful for the specific field
|-
! F
|| F1/F3
|| Identifiers
||
* Missing identifiers on websites
* Identifier not in downloaded data files
||
|| Makes research more difficult.
|-
! I
| I2
|| Taxonomy/ontology/common vocabulary and language issues
||
* Lack of standardisation
* Heterogeneous data makes standardisation hard
* No vocabulary documentation on websites
* Words used for same term in other languages may differ
* Databases in other languages than English
||
|| Research costs more time
|-
! I
| I3
|| References to other (meta)data
||
* No linking to source documents and related publications (for contextual knowledge)
* Possible need for links to other fields
|| UC3: link between microscopic and macroscopic materials (e.g., turbine blades)
||
* Data cannot be connected to the source
* Makes research more difficult.

|-
! R
| R1.1
|| Licensing
||
* No licenses available may mean the data is not reusable for the researcher
* Licenses not clear and accessible
* Obtaining licenses may result in more effort and costs
||
|| Data potentially not reusable

|-
! R
| R1.2
|| Reliability and provenance of data
|| When the data is collected from different and high number of sources, its reliability decreases
||
|| Data potentially not reusable

|-
! O
|
|| Privacy concerns and expected disadvantages
|| Not publishing data due to privacy concerns (sensitive data)
|| UC2: In case of distribution network data this is relevant
|| Data not accessible

|-
! Other
|
|| Conducting FAIR assessments
||
* No qualitative assessment of the data itself (which may be limited)
* Discrepancies between results conducted by humans versus machines
* Sometimes not even DC standards are met
* Metadata is not updated
* Lacking encryption of websites (https)
* Problems assessing whether metadata will be available after data is unavailable
* Interface design/layout may be unclear, incomplete or not intuitive for humans
* Authentication details (user registration login / good or bad, clarify)

||
* UC1 lack of data availability for time-series
||
* Assessment is uncertain (human assessment may need more clarification and understanding)
* Makes research more time-intensive

|}

== FAIR Self-Assessment tool ==

To conduct a human FAIR evaluation for different databases and their respective datasets, different free online tools were considered. Some seemed to be quite useful (e.g. [https://satifyd.dans.knaw.nl/ SATISFYD] ) and some were simple self-evaluation PDFs to be printed and filled out.

We decided to chose the [https://ardc.edu.au/resources/working-with-data/fair-data/fair-self-assessment-tool/ self-assessment tool] provided by the Australian Research Data Commons (ARDC). The decision was based manly upon the fact that the tools questions closely matched the FAIR categories specified by [https://www.doi.org/10.1038/sdata.2016.18 Wilkinson et al., 2016] which would make it easily comparable to the existing preferable FAIR conditions and aid with the repeatability of the results, and the half-automated scoring features that show the extent of the "FAIRness" of the analysed sample as a simple bar chart.

The result of the FAIR assessment is provided as a bar chart where an empty bar chart displays 0 % FAIR compatibility, and a full bar chart represents 100 % FAIR compatibility. The score is therefore not returned as a specific value. During the assessment below, the "Total Across FAIR" in Table ... was estimated according to the level of the bar chart from 0 % to 100 %.
Resulting scores where categorised as follows: Low: < 50%; Medium: 50-60%; High: > 60% .

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ All options for each question of the ARDCs self-assessment tool
!colspan="4"|Findable
!colspan="3"|Accessible
!colspan="3"|Interoperable
!colspan="3"|Reusable
|-
| F1. (meta)data are assigned a globally unique and persistent identifier
|| F2. data are described with rich metadata (defined by R1 below)
|| F3. metadata clearly and explicitly include the identifier of the data it describes
|| F4. (meta)data are registered or indexed in a searchable resource
|| A1. (meta)data are retrievable by their identifier using a standardized communications protocol
|| A1.1 the protocol is open, free, and universally implementable and A1.2 the protocol allows for an authentication and authorization procedure, where necessary
|| A2. metadata are accessible, even when the data are no longer available
|| I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
|| I2. (meta)data use vocabularies that follow FAIR principles
|| I3. (meta)data include qualified references to other (meta)data
|| R1.1. (meta)data are released with a clear and accessible data usage license
|| R1.2. (meta)data are associated with detailed provenance
|| R1.3. (meta)data meet domain-relevant community standards
|-
! Does the dataset have any identifiers assigned?
! Is the dataset identifier included in all metadata records/files describing the data?
! How is the data described with metadata?
! What type of repository or registry is the metadata record in?
! How accessible is the data?
! Is the data available online without requiring specialised protocols or tools once access has been approved?
! Will the metadata record be available even if the data is no longer available?
! What (file) format(s) is the data available in?
! What best describes the types of vocabularies/ontologies/tagging schemas used to define the data elements?
! How is the metadata linked to other data and metadata (to enhance context and clearly indicate relationships)?
! Which of the following best describes the license/usage rights attached to the data?
! How much provenance information has been captured to facilitate data reuse?
!
|-
| Globally unique, citable and persistant (e.g. DOI, PURL, ARK or Handle)
|| Yes
|| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
|| Data is in one place but discoverable through several registries
|| Publicly accessible
|| Standard web service API (e.g. OGC)
|| Yes
|| In a structured, open standard, machine-readable format
|| Standardised open and universal using resolvable global identifiers linking to explainations
|| Meadata is represented in a machine readable format, e.g. in a linked data format such as Resource Description Framework (RDF)
|| Standard machine-readable license (e.g. Creative Commons)
|| Fully recorded in a machine-readable format
||
|-
| Web adress (URL)
|| No
|| Comprehensively, but in a text-based, non-standard format
|| Generalist public repository
|| Fully accessible to person who meet explicitly stated conditions, e.g. ethics approval for sensitive data
|| Non-standard web service (e.g. OpenAPI/Swagger/informal API)
|| No
|| In a structured, open standard, non-machine-readable format
|| Standardised vocabularies/ontologies/schema without global identifiers
|| The metadata record includes URI links to related metadata, data and definitions
|| Standard text based license
|| Fully recorded in a text format
||
|-
| Local identifier
||
|| Brief title and description
|| Domain-specific repository
|| A de-identified / modified subset of the data is publicly accessible
|| File download from online location
|| Unsure
|| Mostly in a proprietary format
|| No standards have been applied in the description of data elements
|| There are no links to other metadata
|| Non-standard machine-readably license (clearly indicating under what conditions the data may be reused)
|| Partially recorded
||
|-
| No identifier
||
|| The data is not described
|| Local institutional repository
|| Embaged access after a specific date
|| By individual arrangement
||
||
|| Data elements not described
||
|| Non-standard text-based license
|| No provenance information is recorded
||
|-
|
||
||
|| The data is not described in any repository
|| Unspecified conditional access e.g. contact the data custodian for access
|| No access to data
||
||
||
||
|| No license
||
||
|-
|
||
||
||
|| Access to metadata only
||
||
||
||
||
||
||
||
|-
|
||
||
||
|| No access to data or metadata
||
||
||
||
||
||
||
||
|}

At this point it is important to note that the assessment of databases and/or datasets using this tool is solely considered a human assessment. This may lead to different evaluations by different users. To counteract this phenomenon, it was suggested that the evaluations should be completed using the 'four-eyes principle' to create more verifiable results. Also, it is planned to additionally evaluate the respective databases using an external tool to perform a machine-assessment.

In addition, not all questions are weighted equally and thus a worse result for one question may not have a large impact whereas only choosing the second-best option in another question will bring down the overall FAIR value considerably.

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ The ARDC tool provides detailed information about the requirements for each of the options of its tool. These descriptions are documented here.
|-
! rowspan="5" | F
|| F
|| About Findable
|| Making data Findable includes assigning a persistent identifier (like a DOI or Handle ), having rich metadata to describe the data and making sure it is findable through disciplinary and generalist discovery portals (local and international).

|-
| F1
|| About identifiers
|| Identifiers are essential for identifying, finding, retrieving, linking and citing datasets. A Web address (URL) can be used to specify the online location of a resource but over time URLs tend to change which leads to broken links to the data. To be useful, identifiers need to be persistent and unique. Digital Object Identifiers (DOIs) and other persistent identifiers (PIDs) provide a permanent citable reference to a particular dataset. The DOI is a permanent fixed reference to the dataset no matter where it is located online and enables citation and citation metrics. Services to create a persistent identifier are often offered by your affiliated institution or the repository you are using to describe your data. Talk to your library service for more information.
|-
| F2
|| About identifiers
|| The identifier (preferably a persistent identifier) needs to be clearly stated in the metadata record describing the data collection, and also in any associated data files or metadata.

|-
|F3
|| About metadata descriptions
|| Comprehensive metadata records will include descriptive content that facilitates discovery, access and reuse of the data being described. While there is no 'one size fits all' list, comprehensive metadata should include:
* a globally unique persistent identifier e.g. a DOI
* a title
* related people, i.e. the data creator or custodian
* how to access the data and file formats
* a description of how the data were created and how to interpret the data subject or keywords
* citation information that clearly indicates how the data should be cited
* a machine-readable data licence
* provenance and contextual information such as:
* links to related publications, projects, services and software
* methodology and processes involved in data production
* spatial and temporal coverage (if relevant)
* object-level data description

Providing metadata in a standard schema allows it to be read and used by machines as well as humans.

|-
| F4
|| About metadata repositories
|| A rich metadata description alone does not ensure a dataset’s ‘findability’ on the internet; the dataset needs to be registered or indexed in a searchable resource, such as a generalist (e.g. Figshare, Dryad, Zenodo domain-specific (e.g. PANGAEA, ADA, ICPSR), or institutional (e.g. ANU Research Repository , Sydney eScholarship Repository, data.gov.au) data repository or registry. Ideally these repositories/registries are indexed by search engines such as Google and/or Google Scholar.

|-
! rowspan = "4" | A
| A
|| About Accessible
|| To make data accessible may include making the data open using a standardised protocol. However the data does not necessarily have to be open. There are sometimes good reasons why data cannot be made open, for example privacy concerns, national security or commercial interests. If it is not open there should be clarity and transparency around the conditions governing access and reuse
|-
| A1
|| About data accessibility
|| Not all data that is discoverable can be freely accessed. Often there are embargoes, access controls, and access permissions associated with data due to a variety of issues such as privacy and commercial interests. Even with all these issues much sensitive data can be shared. Many other issues that could be perceived as blockers to sharing data may be overcome.
|-
| A1.1/A1.2
|| About protocols
|| Ideally users would like to retrieve appropriate internet content directly and unhindered once they have located it. Internet protocols (e.g. http and ftp) define rules and conventions for communication between devices, and tools and services are available to facilitate this process, e.g. APIs.

Protocols:
* HTTP (Hypertext Transfer Protocol) is the set of rules for transferring files (text, graphic images, sound, video, and other multimedia files) on the World Wide Web. As soon as a Web user opens their Web browser, the user is indirectly making use of HTTP.
* FTP (File Transfer Protocol) is a standard Internet protocol for transmitting files between computers on the Internet over TCP/IP connections. Read more: Web protocols
* API (Application Programming Interface): When information is made available in any machine readable format, it becomes possible to make that information directly available to programs that request that information over the web. An API is the way this information is made directly available to other machines.

|-
| A2
|| About metadata availability
|| Effort is often required to maintain data resources online which can often lead to it being neglected especially over long periods of time. This often leads to broken links between the metadata and the data. Having at least a description of the data in the form of a metadata record means there is a record of the data's existence allowing the possibility of someone tracking it down.

|-
! rowspan="4" | I
|| I
|| About Interoperability
|| To be interoperable (i.e. data that is interpretable by a computer, so that they can be automatically combined with other data) the data will need to use community agreed [https://www.ands.org.au/guides/file-formats formats], language and [https://www.ands.org.au/guides/vocabularies-and-research-data vocabularies]. The [https://www.ands.org.au/working-with-data/metadata metadata] will also need to use a community agreed standards and vocabularies, and contain links to related information using [https://www.ands.org.au/working-with-data/citation-and-identifiers identifiers].
|-
|| I1
|| About file formats
|| When selecting file formats for archiving, the formats should ideally be:
* Non-proprietary
* Unencrypted
* Uncompressed
* In common usage by the research community
* Adherent to an open, documented standard, such as described by the State of California (see [http://www.leginfo.ca.gov/pub/07-08/bill/asm/ab_1651-1700/ab_1668_bill_20070524_amended_asm_v96.pdf AB 1668, 2007])
** Interoperable among diverse platforms and applications
** Fully published and available royalty-free
** Fully and independently implementable by multiple software providers on multiple platforms without any intellectual property restrictions for necessary technology
** Developed and maintained by an open standards organization with a well-defined inclusive process for evolution of the standard.

From: [https://library.stanford.edu/research/data-management-services/data-best-practices/best-practices-file-formats Stanford University Library - Best practices for file formats]

File format examples:
* Structured, open standard, machine-readable format e.g. (text) PDF/A, HTML, Plain text, (images) TIFF, JPEG 2000, GIF, (audio) MP3, AIFF, WAVE, (video) MOV, MPEG, AVI, (Tabular data) CSV
* structured, open standard, non-machine-readable format, e.g. PDF, HTML, JPG
* Proprietary format, e.g. doc (Word), .xls (Excel), .ppt (PowerPoint), .sav

Read more:
* [https://www.ands.org.au/__data/assets/pdf_file/0003/731775/File-Formats.pdf ANDS]
* [https://www.europeandataportal.eu/elearning/en/module9/#/id/co-01 European Data Portal]

|-
|| I2
|| About schema standards
|| Schema standards are schemas that have gone through a formal validation process by a standards organisation, such as the International Standards Organisation (ISO) or an equivalent body such as the Dublin Core Metadata Initiative (DCMI); or, commonly used and consistently applied metadata schemas that are well documented, endorsed, and maintained can also become ‘de-facto standards’, e.g. RIF-CS for describing data collections and services used in Research Data Australia.

Read more: [https://www.ands.org.au/guides/metadata-working Metadata schema]

Standardised open and universal schemas, e.g, [https://www.dcc.ac.uk/resources/metadata-standards/datacite-metadata-schema DataCite Metadata Schema], [https://www.dcc.ac.uk/resources/metadata-standards/prov PROV], [https://www.dcc.ac.uk/resources/metadata-standards/dublin-core Dublin Core]

Domain-specific standards, e.g. [http://human-phenotype-ontology.github.io/ HPO - Human Phenotype Ontology], [https://www.nlm.nih.gov/mesh/ MeSH - Medical Subject Headings] , [https://mcp-profile-docs.readthedocs.io/en/stable/ Marine Community Profile], [https://ddialliance.org/ DDI - Data Documentation Initiative].

For more examples of standards: [http://rd-alliance.github.io/metadata-directory/ Here].

Resolvable global identifiers - An identifier is any label used to name something uniquely (whether online or offline). URLs are an example of an identifier. So are serial numbers, and personal names. A persistent identifier is guaranteed to be managed and kept up to date over a defined time period.

Read more: [https://www.ands.org.au/guides/persistent-identifiers-awareness Persistent Identifiers]

|-
||I3
|| About metadata links
|| The goal is to create as many meaningful linkages as possible between (meta)data resources to enrich the contextual knowledge about the data, balanced against the time/energy involved in making a good data model.

'''Machine-readable (meta)data'''

(Meta)data in a format that can be automatically read and processed by a computer, such as [http://opendatahandbook.org/glossary/en/terms/csv/ CSV], [http://opendatahandbook.org/glossary/en/terms/json/ JSON], [http://opendatahandbook.org/glossary/en/terms/xml/ XML] etc. For more information: [http://opendatahandbook.org/glossary/en/terms/machine-readable/ Open Data Handbook]

'''Linked Data'''

Linked Data (also known as Linking Data) can be applied to improve the exploitation of the “Web of data.” The expression refers to the publishing of structured data in a way that typed links are created between data from different sources to provide a higher level of usability. By using Linked Data, it is possible to find other, related data. Structured data should meet four requirements to be called
Linked Data:

* URIs should be assigned to all entities of the dataset.
* HTTP URIs are required to ensure that all entities can be referenced and cited by users and user agents.
* Entities should be described using standard formats such as RDF/XML.
* Links should be created to other, related entity URIs.

All data that fulfil these requirements and are released for the public are called Linked Open Data (LOD). http://www.lesliesikos.com/semantic-web-machine-readable-structured-data-with-meaningful-annotations/

Read more under the heading (meta)data include qualified references to other (meta)data in the following resource: https://www.dtls.nl/fair-data/fair-principles-explained/

For more information on Linked Data standards - RDF - https://www.w3.org/RDF/

Other related linked data standards:
* OWL - https://www.w3.org/OWL/
* SKOS - https://www.w3.org/2004/02/skos/
* SPARQL - https://www.w3.org/TR/rdf-sparql-query/

|-
! rowspan="3" | R
|| R
|| About Reusable
|| Reusable data should maintain its initial richness. For example, it should not be abridged for the purpose of explaining the findings in one particular publication. It needs a clear machine-readable [https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse licence] and [https://www.ands.org.au/partners-and-communities/ands-communities/data-provenance-interest-group provenance] information on how the data was formed. It should also use discipline-specific data and [https://www.ands.org.au/working-with-data/metadata metadata] standards to give it rich contextual information that will allow for accurate interpretation and reuse.

|-
|| R1.1
|| About licenses
|| If data is not licensed no-one else can use it. In Australia, no licence is regarded as the same as 'all rights reserved', confining any reuse to very limited circumstances. Applying a Creative Commons licence to your data is a simple way to ensure that your data can be reused. The less restrictive the licence, the more that can be done with the data.
To make it easy for the Web to know when a work is available under a particular license, a “machine readable” version of the license provides a summary of the key freedoms and obligations written into a format that software systems, search engines, and other kinds of technology can understand.
Example from the [https://creativecommons.org/choose/ Creative Commons License Choser]:

What appears in the metadata:

This work is licensed under a [https://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International License].

Code snippet:

<code>
<a rel="license" href="http://creativecommons.org/licenses/by/4.0/">

<img alt="Creative Commons License" style="border-width:0"

src="https://i.creativecommons.org/l/by/4.0/88x31.png"/>

</a> This work is licensed under a

<a rel="license" href="http://creativecommons.org/licenses/by/4.0/">

Creative Commons Attribution 4.0 International License</a>.
<\code>

Read more: https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse

|-
||R1.2
|| About provenance
|| Data provenance is used to document where a piece of data comes from and the process and methodology by which it is produced. It is important to confirm the authenticity of data enabling trust, credibility and reproducibility. This is becoming increasingly important, especially in the eScience community where research is data intensive and often involves complex data transformations and procedures ([https://www.ands.org.au/partners-and-communities/ands-communities/data-provenance-interest-group Read more]). Provenance vocabularies such as [https://www.w3.org/2005/Incubator/prov/XGR-prov-20101214/#Recommendations Provenir Ontology (PROV-O) and others].

|}

== Results for SMILES and ShareWind ==

{| class="mw-collapsible wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Datasets from the SMILES and ShareWind platforms was analysed using the self-assessment tool provided by ARDC.
! rowspan="5" |
| rowspan="5" |
! Database Code
| rowspan="24" style="background: #ABE" |
| ED4
| ED9
|-
! Database Title
|| SMILES
| ShareWind
|-
! Database Link
|| https://www.ecria-smiles.eu/
| https://sharewind.eu/
|-
! Sample Link
|| [https://www.ecria-smiles.eu/data-files/-/document_library/W32cCAfL2jMX/view_file664466?_com_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX_redirect=https%3A%2F%2Fwww.ecria-smiles.eu%3A443%2Fdata-files%2F-%2Fdocument_library%2FW32cCAfL2jMX%2Fview%2F664423%3F_com_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX_redirect%3Dhttps%253A%252F%252Fwww.ecria-smiles.eu%253A443%252Fdata-files%253Fp_p_id%253Dcom_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX%2526p_p_lifecycle%253D0%2526p_p_state%253Dnormal%2526p_p_mode%253Dview here] (link has 585 characters)
| https://sharewind.eu/records/f9d31abbc2824284b8c1d28894d9619a (data found at https://zenodo.org/record/1162738)
|-
! Sample Size
|| 4 KB
| 37 MB
|-
| colspan="6" style="background: #ABE" |
|-
! rowspan="4" | Findable
| F1. (meta)data are assigned a globally unique and persistent identifier
! Does the dataset have any identifiers assigned?
|| Web adress (URL)
| Globally unique, citable and persistant (e.g. DOI, PURL, ARK or Handle)
|-

| F2. data are described with rich metadata (defined by R1 below)
! Is the dataset identifier included in all metadata records/files describing the data?
|| No
| No
|-

| F3. metadata clearly and explicitly include the identifier of the data it describes
! How is the data described with metadata?
|| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
|-

| F4. (meta)data are registered or indexed in a searchable resource
! What type of repository or registry is the metadata record in?
|| Domain-specific repository
| Data is in one place but discoverable through several registries
|-

! rowspan="3" | Accessible

| A1. (meta)data are retrievable by their identifier using a standardized communications protocol
! How accessible is the data?
|| Publicly accessible
| Publicly accessible
|-
| A1.1 the protocol is open, free, and universally implementable; A1.2 the protocol allows for an authentication and authorization procedure, where necessary
! Is the data available online without requiring specialised protocols or tools once access has been approved?
|| File download from online location
| Standard web service API (e.g. OGC)
|-
| A2. metadata are accessible, even when the data are no longer available
! Will the metadata record be available even if the data is no longer available?
|| Unsure
| Yes
|-

! rowspan="3" | Interoperable
| I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
! What (file) format(s) is the data available in?
|| In a structured, open standard, machine-readable format
|| In a structured, open standard, non-machine-readable format
|-
| I2. (meta)data use vocabularies that follow FAIR principles
! What best describes the types of vocabularies/ontologies/tagging schemas used to define the data elements?
|| Standardised vocabularies/ontologies/schema without global identifiers
|| Standardised vocabularies/ontologies/schema without global identifiers
|-
| I3. (meta)data include qualified references to other (meta)data
! How is the metadata linked to other data and metadata (to enhance context and clearly indicate relationships)?
|| There are no links to other metadata
|| Metadata is represented in a machine readable format, e.g. in a linked data format such as Resource Description Framework (RDF)
|-

! rowspan="3" | Reusable
| R1.1. (meta)data are released with a clear and accessible data usage license
! Which of the following best describes the license/usage rights attached to the data?
|| No license
| Standard machine-readable license (e.g. Creative Commons)
|-
| R1.2. (meta)data are associated with detailed provenance
! How much provenance information has been captured to facilitate data reuse?
|| Partially recorded
|| Partially recorded
|-
| R1.3. (meta)data meet domain-relevant community standards
|
|
|
|-
| colspan="6" style="background: #ABE" |
|-
! Total across F.A.I.R
|
! Low: < 50%; Medium: 50-60%; High: > 60%
|| ~ 45 %
|| ~ 85 %
|-
| colspan="6" style="background: #ABE" |
|-
! Remarks about database
||
||
||
|| The data is provided by the open-source platform Zenodo, not the project website itself. A very positive aspect is that information on different versions of the data as well as the number of views and downloads is available.
|-
! Identified gaps
||
||
||
* No DOI, only URL
* Extremely long URL link spanning several lines with 585 characters, that can easily become corrupted
* No links to other metadata
* No license information given
* Provenance not fully recorded

||
* Dataset identifiers should be included in the files describing the data
* Provenance details are only partially recorded with a note with contact name for further information
|}

= Metadata assessment =

== Dublin Core Elements ==
{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ The DCMI Metadata Terms lists the current set of the Dublin Core vocabulary. Source: [https://dublincore.org/specifications/dublin-core/dcmi-terms/ Dublin Core Metadata Initiative]
| 1 || abstract || 11 || contributor || 21 || extent || 31 || isReplacedBy || 41 || publisher || 51 || tableOfContents
|-
| 2 || accessRights || 12 || coverage || 22 || format || 32 || isRequiredBy || 42 || references || 52 || temporal
|-
| 3 || accrualMethod || 13 || created || 23 || hasFormat || 33 || issued || 43 || relation || 53 || title
|-
| 4 || accrualPeriodicity || 14 || creator || 24 || hasPart || 34 || isVersionOf || 44 || replaces || 54 || type
|-
| 5 || accrualPolicy || 15 || date || 25 || hasVersion || 35 || language || 45 || requires || 55 || valid
|-
| 6 || alternative || 16 || dateAccepted || 26 || identifier || 36 || license || 46 || rights || ||
|-
| 7 || audience || 17 || dateCopyrighted || 27 || instructionalMethod || 37 || mediator || 47 || rightsHolder || ||
|-
| 8 || available || 18 || dateSubmitted || 28 || isFormatOf || 38 || medium || 48 || source || ||
|-
| 9 || bibliographicCitation || 19 || description || 29 || isPartOf || 39 || modified || 49 || spatial || ||
|-
| 10 || conformsTo || 20 || educationLevel || 30 || isReferencedBy || 40 || provenance || 50 || subject || ||
|}

'''Metadata Category Types according to [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO]'''

There are three main types of metadata:
* Descriptive metadata describes a resource for purposes such as discovery and identification. It can include elements such as title, abstract, author, and keywords.
* Structural metadata indicates how compound objects are put together, for example, how pages are ordered to form chapters.
* Administrative metadata provides information to help manage a resource, such as when and how it was created, file type and other technical information, and who can access it. There are several subsets of administrative data; two that sometimes are listed as separate metadata types are:
** Rights management meta-data, which deals with intellectual property rights,and
** Preservation metadata, which contains information needed to archive and preserve a resource.
''

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Simple Dublin Core Elements. 15 DC elements with their (shortened) official definitions and examples.
! DC element name !! DC definition ([https://www.dublincore.org/specifications/dublin-core/dces/ DCMI]) !! Example (Energy Domain) !! Category (determined with the aid of [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO])
|-
! Title
| A name given to the resource || Outdoor monitoring of a hybrid micro-CPV solar panel with integrated micro-tracking and diffuse capture || Descriptive
|-
! Subject
| The topic of the resource || concentrator photovoltaics; CPV; rooftop photovoltaics; integrated tracking || Descriptive
|-
! Description
| An account of the resource || Dataset from the outdoor characterization of a B Series module from Insolight at the rooftop of the Instituto de Energía Solar - Universidad Politécnica de Madrid. …. || Descriptive
|-
! Creator
| An entity primarily responsible for making the resource || Stephen Askins; Gaël Nardin || Descriptive
|-
! Publisher
| An entity responsible for making the resource available || Stephen Askins || Descriptive
|-
! Contributor
| An entity responsible for making contributions to the resource || Stephen Askins || Descriptive
|-
! Date
| A point or period of time associated with an event in the lifecycle of the resource || 2019-07-23; Date will be associated with the creation or availability of the resource. || Administrative
|-
! Type
| The nature or genre of the resource || info:eu-repo/semantics/other; dataset || Descriptive
|-
! Format
| The file format, physical medium, or dimensions of the resource || image
|| Structural
|-
! Identifier
| An unambiguous reference to the resource within a given context || https://zenodo.org/record/3346823; 10.5281/zenodo.3346823; oai:zenodo.org:3346823 || Descriptive
|-
! Source
| A related resource from which the described resource is derived || RC607.A26W574 1996 [where "RC607.A26W574 1996" is the call number of the print version of the resource, from which the present version was scanned] || Descriptive
|-
! Language
| A language of the resource || eng || Descriptive
|-
! Relation
| A related resource || info:eu-repo/grantAgreement/EC/H2020/787289/ || Descriptive
|-
! Coverage
| The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant || 1995-1996; Madrid, Spain || Descriptive
|-
! Rights
| Information about rights held in and over the resource || info:eu-repo/semantics/openAccess; https://creativecommons.org/licenses/by/4.0/legalcode || Administrative
|}

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Non-DC elements

! Non-DC element name !! Definition !! Example !! Category
|-
! Date of availability
| Since when is the data available
||
|| Impact/Exploitation

|-
! Number of users
| Is there any information on the number of accesses or users?
||
|| Impact/Exploitation

|-
! Number of scientific publications where data is cited/used
| Is there any information on the number of citations/uses?
||
|| Impact/Exploitation

|-
! Curation activities (is the versioning ongoing)
| Update, maintainance
||
|| Impact/Exploitation
|}

Attempting to put a value on Impact/Exploitation:
* '''ENTSO-E''': Found 172 results in Advanced Search (ALL) in Web of Science ([https://apps.webofknowledge.com/ WoSc]) (16/06/2020). Citations of all these search result add up to 714. This however does not mean, that all search results really explicitly used data from ENTSO-E. Some (for sure, for some were found) only mentioned it saying they adoped workflow etc. from the site.
* '''SMARD''': Found 82 results in Advanced Search (ALL=(SMARD)) in Web of Science ([https://apps.webofknowledge.com/ WoSc]) (16/06/2020). However: SMARD is also an acronym for ''Spinal muscular atrophy with respiratory distress (SMARD)''. This somewhat complicates results and needs some further discussion. All 82 search results accumumlated 2359 citations themselves.

Gap analysis

2020-08-28T09:30:11Z

Christopherbs: /* FAIR Self-Assessment tool */

<accesscontrol>Administrators,consortium</accesscontrol>

This is the page for the gap analysis.

= Tacit knowledge "FAIRification and opening of low carbon energy research data" =
 Consortium members can add any time, they feel that something is important to note even though it is not mentioned in a standard deliverable. In other words, this page collects uncodified, tacit knowledge. It will help us later to compile suggestions and lessons learned. This kind of knowledge is collected two-fold:
#Anytime if someone feels that this should be noted. Please write down: Issue, Date, Author (could also be "anonymous"), the issue described in a few words or maximal lines>
#During the final day of workshops

=== Learning process: ===

*People are hesitant to adopt new IT technologies, this is even the case among researchers heavily relying on data, algorithms and collaborative online software (e.g., R, platforms, online conferencing, HPC, ...). The effort to encourage change is not to underestimate. Reasons are several, notably, lack of time and uncertainty about potential benefit as well as overall risk aversion preferences. EERAdata is using the online software "Only Office" to facilitate collaboration (in particular also during the Covid-19 period).
 '''FAIR and open criteria:'''
*Consortium members have a fair understanding of what FAIR/O is, but there is little knowledge and/or technical experience on how to approach the FAIRification and opening. All, however, share the view that we are at a critical point in time, where we need to implement these criteria. 
*To deepen knowledge about FAIR/O criteria, it is useful to test the criteria on a database one is familiar with. For this purpose (and to start brainstorming about the platform), AIT has developed a questionnaire for application in the use cases.
'''Metadata:'''
*A good starting point is to think about metadata and to look into existing metadata concepts in one's field. The first step is to understand that also metadata need to adhere to the FAIR/O principles.
*The next step is to increase knowledge on IT specific terms, i.e. to understand what the difference is between different '''[https://energydata.onlyoffice.eu/Products/Files/doceditor.aspx?fileid=5404340 metadata frameworks] (taxonomy, thesaurus, ontology) as well as classification of metadata (high-level, medium-level, low-level OR administrative, structural and descriptive metadata).  '''WP 2 being in charge of aligning approaches between use cases, participates in all use case kickoffs to bring everybody on the same page. The presentation is linked with "metadata frameworks".
*It is useful to supply consortium members with read aheads and watch aheads on metadata to prepare the '''[https://energydata.onlyoffice.eu/Products/Files/doceditor.aspx?fileid=5296590 first EERAdata workshop]'''. The workshop starts applications and discussions in the use cases break out sessions (and bringing insights back to the plenary), using selected databases.

= FAIR Assessment=

== FAIR Guiding Principles ==
{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ This table shows the FAIR guiding principles described by [https://www.doi.org/10.1038/sdata.2016.18 Wilkinson et al., 2016].

! To be Findable
|
* F1. (meta)data are assigned a globally unique and persistent identifier
* F2. data are described with rich metadata (defined by R1 below)
* F3. metadata clearly and explicitly include the identifier of the data it describes
* F4. (meta)data are registered or indexed in a searchable resource

|-
! To be Accessible
|
* A1. (meta)data are retrievable by their identifier using a standardized communications protocol
** A1.1 the protocol is open, free, and universally implementable
** A1.2 the protocol allows for an authentication and authorization procedure, where necessary
* A2. metadata are accessible, even when the data are no longer available

|-
! To be Interoperable
|
* I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
* I2. (meta)data use vocabularies that follow FAIR principles
* I3. (meta)data include qualified references to other (meta)data

|-
! To be Reusable
|
* R1. meta(data) are richly described with a plurality of accurate and relevant attributes
* R1.1. (meta)data are released with a clear and accessible data usage license
* R1.2. (meta)data are associated with detailed provenance
* R1.3. (meta)data meet domain-relevant community standards

|}

== Identified gaps after Workshop 1 (02/06/2020 – 04/06/2020) ==

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ This table shows a summary of the specific issues that were identified in Workshop 1. The aim was to categorise different issues to get a better understanding of how to tackle these challenges.
! FAIR/O !! Specific FAIR/O !! Gaps !! Gaps for energy domain !! Use case specific gaps !! Consequences for researchers
|-
! F
| F2
|| Metadata scope
|| Different fields require different metadata (potentially very specific)
|| UC3: lack of additional metadata for applications of materials
|| Data are less useful for the specific field
|-
! F
|| F1/F3
|| Identifiers
||
* Missing identifiers on websites
* Identifier not in downloaded data files
||
|| Makes research more difficult.
|-
! I
| I2
|| Taxonomy/ontology/common vocabulary and language issues
||
* Lack of standardisation
* Heterogeneous data makes standardisation hard
* No vocabulary documentation on websites
* Words used for same term in other languages may differ
* Databases in other languages than English
||
|| Research costs more time
|-
! I
| I3
|| References to other (meta)data
||
* No linking to source documents and related publications (for contextual knowledge)
* Possible need for links to other fields
|| UC3: link between microscopic and macroscopic materials (e.g., turbine blades)
||
* Data cannot be connected to the source
* Makes research more difficult.

|-
! R
| R1.1
|| Licensing
||
* No licenses available may mean the data is not reusable for the researcher
* Licenses not clear and accessible
* Obtaining licenses may result in more effort and costs
||
|| Data potentially not reusable

|-
! R
| R1.2
|| Reliability and provenance of data
|| When the data is collected from different and high number of sources, its reliability decreases
||
|| Data potentially not reusable

|-
! O
|
|| Privacy concerns and expected disadvantages
|| Not publishing data due to privacy concerns (sensitive data)
|| UC2: In case of distribution network data this is relevant
|| Data not accessible

|-
! Other
|
|| Conducting FAIR assessments
||
* No qualitative assessment of the data itself (which may be limited)
* Discrepancies between results conducted by humans versus machines
* Sometimes not even DC standards are met
* Metadata is not updated
* Lacking encryption of websites (https)
* Problems assessing whether metadata will be available after data is unavailable
* Interface design/layout may be unclear, incomplete or not intuitive for humans
* Authentication details (user registration login / good or bad, clarify)

||
* UC1 lack of data availability for time-series
||
* Assessment is uncertain (human assessment may need more clarification and understanding)
* Makes research more time-intensive

|}

== FAIR Self-Assessment tool ==

To conduct a human FAIR evaluation for different databases and their respective datasets, different free online tools were considered. Some seemed to be quite useful (e.g. [https://satifyd.dans.knaw.nl/ SATISFYD] ) and some were simple self-evaluation PDFs to be printed and filled out.

We decided to chose the [https://ardc.edu.au/resources/working-with-data/fair-data/fair-self-assessment-tool/ self-assessment tool] provided by the Australian Research Data Commons (ARDC). The decision was based manly upon the fact that the tools questions closely matched the FAIR categories specified by [https://www.doi.org/10.1038/sdata.2016.18 Wilkinson et al., 2016] which would make it easily comparable to the existing preferable FAIR conditions and aid with the repeatability of the results, and the half-automated scoring features that show the extent of the "FAIRness" of the analysed sample as a simple bar chart.

The result of the FAIR assessment is provided as a bar chart where an empty bar chart displays 0 % FAIR compatibility, and a full bar chart represents 100 % FAIR compatibility. The score is therefore not returned as a specific value. During the assessment below, the "Total Across FAIR" in Table ... was estimated according to the level of the bar chart from 0 % to 100 %.
Resulting scores where categorised as follows: Low: < 50%; Medium: 50-60%; High: > 60% .

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ All options for each question of the ARDCs self-assessment tool
!colspan="4"|Findable
!colspan="3"|Accessible
!colspan="3"|Interoperable
!colspan="3"|Reusable
|-
| F1. (meta)data are assigned a globally unique and persistent identifier
|| F2. data are described with rich metadata (defined by R1 below)
|| F3. metadata clearly and explicitly include the identifier of the data it describes
|| F4. (meta)data are registered or indexed in a searchable resource
|| A1. (meta)data are retrievable by their identifier using a standardized communications protocol
|| A1.1 the protocol is open, free, and universally implementable and A1.2 the protocol allows for an authentication and authorization procedure, where necessary
|| A2. metadata are accessible, even when the data are no longer available
|| I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
|| I2. (meta)data use vocabularies that follow FAIR principles
|| I3. (meta)data include qualified references to other (meta)data
|| R1.1. (meta)data are released with a clear and accessible data usage license
|| R1.2. (meta)data are associated with detailed provenance
|| R1.3. (meta)data meet domain-relevant community standards
|-
! Does the dataset have any identifiers assigned?
! Is the dataset identifier included in all metadata records/files describing the data?
! How is the data described with metadata?
! What type of repository or registry is the metadata record in?
! How accessible is the data?
! Is the data available online without requiring specialised protocols or tools once access has been approved?
! Will the metadata record be available even if the data is no longer available?
! What (file) format(s) is the data available in?
! What best describes the types of vocabularies/ontologies/tagging schemas used to define the data elements?
! How is the metadata linked to other data and metadata (to enhance context and clearly indicate relationships)?
! Which of the following best describes the license/usage rights attached to the data?
! How much provenance information has been captured to facilitate data reuse?
!
|-
| Globally unique, citable and persistant (e.g. DOI, PURL, ARK or Handle)
|| Yes
|| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
|| Data is in one place but discoverable through several registries
|| Publicly accessible
|| Standard web service API (e.g. OGC)
|| Yes
|| In a structured, open standard, machine-readable format
|| Standardised open and universal using resolvable global identifiers linking to explainations
|| Meadata is represented in a machine readable format, e.g. in a linked data format such as Resource Description Framework (RDF)
|| Standard machine-readable license (e.g. Creative Commons)
|| Fully recorded in a machine-readable format
||
|-
| Web adress (URL)
|| No
|| Comprehensively, but in a text-based, non-standard format
|| Generalist public repository
|| Fully accessible to person who meet explicitly stated conditions, e.g. ethics approval for sensitive data
|| Non-standard web service (e.g. OpenAPI/Swagger/informal API)
|| No
|| In a structured, open standard, non-machine-readable format
|| Standardised vocabularies/ontologies/schema without global identifiers
|| The metadata record includes URI links to related metadata, data and definitions
|| Standard text based license
|| Fully recorded in a text format
||
|-
| Local identifier
||
|| Brief title and description
|| Domain-specific repository
|| A de-identified / modified subset of the data is publicly accessible
|| File download from online location
|| Unsure
|| Mostly in a proprietary format
|| No standards have been applied in the description of data elements
|| There are no links to other metadata
|| Non-standard machine-readably license (clearly indicating under what conditions the data may be reused)
|| Partially recorded
||
|-
| No identifier
||
|| The data is not described
|| Local institutional repository
|| Embaged access after a specific date
|| By individual arrangement
||
||
|| Data elements not described
||
|| Non-standard text-based license
|| No provenance information is recorded
||
|-
|
||
||
|| The data is not described in any repository
|| Unspecified conditional access e.g. contact the data custodian for access
|| No access to data
||
||
||
||
|| No license
||
||
|-
|
||
||
||
|| Access to metadata only
||
||
||
||
||
||
||
||
|-
|
||
||
||
|| No access to data or metadata
||
||
||
||
||
||
||
||
|}

At this point it is important to note that the assessment of databases and/or datasets using this tool is solely considered a human assessment. This may lead to different evaluations by different users. To counteract this phenomenon, it was suggested that the evaluations should be completed using the 'four-eyes principle' to create more verifiable results. Also, it is planned to additionally evaluate the respective databases using an external tool to perform a machine-assessment.

In addition, not all questions are weighted equally and thus a worse result for one question may not have a large impact whereas only choosing the second-best option in another question will bring down the overall FAIR value considerably.

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ The ARDC tool provides detailed information about the requirements for each of the options of its tool. These descriptions are documented here.
|-
! rowspan="5" | F
|| F
|| About Findable
|| Making data Findable includes assigning a persistent identifier (like a DOI or Handle ), having rich metadata to describe the data and making sure it is findable through disciplinary and generalist discovery portals (local and international).

|-
| F1
|| About identifiers
|| Identifiers are essential for identifying, finding, retrieving, linking and citing datasets. A Web address (URL) can be used to specify the online location of a resource but over time URLs tend to change which leads to broken links to the data. To be useful, identifiers need to be persistent and unique. Digital Object Identifiers (DOIs) and other persistent identifiers (PIDs) provide a permanent citable reference to a particular dataset. The DOI is a permanent fixed reference to the dataset no matter where it is located online and enables citation and citation metrics. Services to create a persistent identifier are often offered by your affiliated institution or the repository you are using to describe your data. Talk to your library service for more information.
|-
| F2
|| About identifiers
|| The identifier (preferably a persistent identifier) needs to be clearly stated in the metadata record describing the data collection, and also in any associated data files or metadata.

|-
|F3
|| About metadata descriptions
|| Comprehensive metadata records will include descriptive content that facilitates discovery, access and reuse of the data being described. While there is no 'one size fits all' list, comprehensive metadata should include:
* a globally unique persistent identifier e.g. a DOI
* a title
* related people, i.e. the data creator or custodian
* how to access the data and file formats
* a description of how the data were created and how to interpret the data subject or keywords
* citation information that clearly indicates how the data should be cited
* a machine-readable data licence
* provenance and contextual information such as:
* links to related publications, projects, services and software
* methodology and processes involved in data production
* spatial and temporal coverage (if relevant)
* object-level data description

Providing metadata in a standard schema allows it to be read and used by machines as well as humans.

|-
| F4
|| About metadata repositories
|| A rich metadata description alone does not ensure a dataset’s ‘findability’ on the internet; the dataset needs to be registered or indexed in a searchable resource, such as a generalist (e.g. Figshare, Dryad, Zenodo domain-specific (e.g. PANGAEA, ADA, ICPSR), or institutional (e.g. ANU Research Repository , Sydney eScholarship Repository, data.gov.au) data repository or registry. Ideally these repositories/registries are indexed by search engines such as Google and/or Google Scholar.

|-
! rowspan = "4" | A
| A
|| About Accessible
|| To make data accessible may include making the data open using a standardised protocol. However the data does not necessarily have to be open. There are sometimes good reasons why data cannot be made open, for example privacy concerns, national security or commercial interests. If it is not open there should be clarity and transparency around the conditions governing access and reuse
|-
| A1
|| About data accessibility
|| Not all data that is discoverable can be freely accessed. Often there are embargoes, access controls, and access permissions associated with data due to a variety of issues such as privacy and commercial interests. Even with all these issues much sensitive data can be shared. Many other issues that could be perceived as blockers to sharing data may be overcome.
|-
| A1.1/A1.2
|| About protocols
|| Ideally users would like to retrieve appropriate internet content directly and unhindered once they have located it. Internet protocols (e.g. http and ftp) define rules and conventions for communication between devices, and tools and services are available to facilitate this process, e.g. APIs.

Protocols:
* HTTP (Hypertext Transfer Protocol) is the set of rules for transferring files (text, graphic images, sound, video, and other multimedia files) on the World Wide Web. As soon as a Web user opens their Web browser, the user is indirectly making use of HTTP.
* FTP (File Transfer Protocol) is a standard Internet protocol for transmitting files between computers on the Internet over TCP/IP connections. Read more: Web protocols
* API (Application Programming Interface): When information is made available in any machine readable format, it becomes possible to make that information directly available to programs that request that information over the web. An API is the way this information is made directly available to other machines.

|-
| A2
|| About metadata availability
|| Effort is often required to maintain data resources online which can often lead to it being neglected especially over long periods of time. This often leads to broken links between the metadata and the data. Having at least a description of the data in the form of a metadata record means there is a record of the data's existence allowing the possibility of someone tracking it down.

|-
! rowspan="4" | I
|| I
|| About Interoperability
|| To be interoperable (i.e. data that is interpretable by a computer, so that they can be automatically combined with other data) the data will need to use community agreed formats, language and vocabularies. The metadata will also need to use a community agreed standards and vocabularies, and contain links to related information using identifiers.
|-
|| I1
|| About file formats
|| When selecting file formats for archiving, the formats should ideally be:
* Non-proprietary
* Unencrypted
* Uncompressed
* In common usage by the research community
* Adherent to an open, documented standard, such as described by the State of California (see [http://www.leginfo.ca.gov/pub/07-08/bill/asm/ab_1651-1700/ab_1668_bill_20070524_amended_asm_v96.pdf AB 1668, 2007])
** Interoperable among diverse platforms and applications
** Fully published and available royalty-free
** Fully and independently implementable by multiple software providers on multiple platforms without any intellectual property restrictions for necessary technology
** Developed and maintained by an open standards organization with a well-defined inclusive process for evolution of the standard.

From: [https://library.stanford.edu/research/data-management-services/data-best-practices/best-practices-file-formats Stanford University Library - Best practices for file formats]

File format examples:
* Structured, open standard, machine-readable format e.g. (text) PDF/A, HTML, Plain text, (images) TIFF, JPEG 2000, GIF, (audio) MP3, AIFF, WAVE, (video) MOV, MPEG, AVI, (Tabular data) CSV
* structured, open standard, non-machine-readable format, e.g. PDF, HTML, JPG
* Proprietary format, e.g. doc (Word), .xls (Excel), .ppt (PowerPoint), .sav

Read more:
* [https://www.ands.org.au/__data/assets/pdf_file/0003/731775/File-Formats.pdf ANDS]
* [https://www.europeandataportal.eu/elearning/en/module9/#/id/co-01 European Data Portal]

|-
|| I2
|| About schema standards
|| Schema standards are schemas that have gone through a formal validation process by a standards organisation, such as the International Standards Organisation (ISO) or an equivalent body such as the Dublin Core Metadata Initiative (DCMI); or, commonly used and consistently applied metadata schemas that are well documented, endorsed, and maintained can also become ‘de-facto standards’, e.g. RIF-CS for describing data collections and services used in Research Data Australia.

Read more: [https://www.ands.org.au/guides/metadata-working Metadata schema]

Standardised open and universal schemas, e.g, [https://www.dcc.ac.uk/resources/metadata-standards/datacite-metadata-schema DataCite Metadata Schema], [https://www.dcc.ac.uk/resources/metadata-standards/prov PROV], [https://www.dcc.ac.uk/resources/metadata-standards/dublin-core Dublin Core]

Domain-specific standards, e.g. [http://human-phenotype-ontology.github.io/ HPO - Human Phenotype Ontology], [https://www.nlm.nih.gov/mesh/ MeSH - Medical Subject Headings] , [https://mcp-profile-docs.readthedocs.io/en/stable/ Marine Community Profile], [https://ddialliance.org/ DDI - Data Documentation Initiative].

For more examples of standards: [http://rd-alliance.github.io/metadata-directory/ Here].

Resolvable global identifiers - An identifier is any label used to name something uniquely (whether online or offline). URLs are an example of an identifier. So are serial numbers, and personal names. A persistent identifier is guaranteed to be managed and kept up to date over a defined time period.

Read more: [https://www.ands.org.au/guides/persistent-identifiers-awareness Persistent Identifiers]

|-
||I3
|| About metadata links
|| The goal is to create as many meaningful linkages as possible between (meta)data resources to enrich the contextual knowledge about the data, balanced against the time/energy involved in making a good data model.

'''Machine-readable (meta)data'''

(Meta)data in a format that can be automatically read and processed by a computer, such as [http://opendatahandbook.org/glossary/en/terms/csv/ CSV], [http://opendatahandbook.org/glossary/en/terms/json/ JSON], [http://opendatahandbook.org/glossary/en/terms/xml/ XML] etc. For more information: [http://opendatahandbook.org/glossary/en/terms/machine-readable/ Open Data Handbook]

'''Linked Data'''

Linked Data (also known as Linking Data) can be applied to improve the exploitation of the “Web of data.” The expression refers to the publishing of structured data in a way that typed links are created between data from different sources to provide a higher level of usability. By using Linked Data, it is possible to find other, related data. Structured data should meet four requirements to be called
Linked Data:

* URIs should be assigned to all entities of the dataset.
* HTTP URIs are required to ensure that all entities can be referenced and cited by users and user agents.
* Entities should be described using standard formats such as RDF/XML.
* Links should be created to other, related entity URIs.

All data that fulfil these requirements and are released for the public are called Linked Open Data (LOD). http://www.lesliesikos.com/semantic-web-machine-readable-structured-data-with-meaningful-annotations/

Read more under the heading (meta)data include qualified references to other (meta)data in the following resource: https://www.dtls.nl/fair-data/fair-principles-explained/

For more information on Linked Data standards - RDF - https://www.w3.org/RDF/

Other related linked data standards:
* OWL - https://www.w3.org/OWL/
* SKOS - https://www.w3.org/2004/02/skos/
* SPARQL - https://www.w3.org/TR/rdf-sparql-query/

|-
! rowspan="3" | R
|| R
|| About Reusable
|| Reusable data should maintain its initial richness. For example, it should not be abridged for the purpose of explaining the findings in one particular publication. It needs a clear machine-readable [https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse licence] and [https://www.ands.org.au/partners-and-communities/ands-communities/data-provenance-interest-group provenance] information on how the data was formed. It should also use discipline-specific data and [https://www.ands.org.au/working-with-data/metadata metadata] standards to give it rich contextual information that will allow for accurate interpretation and reuse.

|-
|| R1.1
|| About licenses
|| If data is not licensed no-one else can use it. In Australia, no licence is regarded as the same as 'all rights reserved', confining any reuse to very limited circumstances. Applying a Creative Commons licence to your data is a simple way to ensure that your data can be reused. The less restrictive the licence, the more that can be done with the data.
To make it easy for the Web to know when a work is available under a particular license, a “machine readable” version of the license provides a summary of the key freedoms and obligations written into a format that software systems, search engines, and other kinds of technology can understand.
Example from the [https://creativecommons.org/choose/ Creative Commons License Choser]:

What appears in the metadata:

This work is licensed under a [https://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International License].

Code snippet:

<code>
<a rel="license" href="http://creativecommons.org/licenses/by/4.0/">

<img alt="Creative Commons License" style="border-width:0"

src="https://i.creativecommons.org/l/by/4.0/88x31.png"/>

</a> This work is licensed under a

<a rel="license" href="http://creativecommons.org/licenses/by/4.0/">

Creative Commons Attribution 4.0 International License</a>.
<\code>

Read more: https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse

|-
||R1.2
|| About provenance
|| Data provenance is used to document where a piece of data comes from and the process and methodology by which it is produced. It is important to confirm the authenticity of data enabling trust, credibility and reproducibility. This is becoming increasingly important, especially in the eScience community where research is data intensive and often involves complex data transformations and procedures ([https://www.ands.org.au/partners-and-communities/ands-communities/data-provenance-interest-group Read more]). Provenance vocabularies such as [https://www.w3.org/2005/Incubator/prov/XGR-prov-20101214/#Recommendations Provenir Ontology (PROV-O) and others].

|}

== Results for SMILES and ShareWind ==

{| class="mw-collapsible wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Datasets from the SMILES and ShareWind platforms was analysed using the self-assessment tool provided by ARDC.
! rowspan="5" |
| rowspan="5" |
! Database Code
| rowspan="24" style="background: #ABE" |
| ED4
| ED9
|-
! Database Title
|| SMILES
| ShareWind
|-
! Database Link
|| https://www.ecria-smiles.eu/
| https://sharewind.eu/
|-
! Sample Link
|| [https://www.ecria-smiles.eu/data-files/-/document_library/W32cCAfL2jMX/view_file664466?_com_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX_redirect=https%3A%2F%2Fwww.ecria-smiles.eu%3A443%2Fdata-files%2F-%2Fdocument_library%2FW32cCAfL2jMX%2Fview%2F664423%3F_com_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX_redirect%3Dhttps%253A%252F%252Fwww.ecria-smiles.eu%253A443%252Fdata-files%253Fp_p_id%253Dcom_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX%2526p_p_lifecycle%253D0%2526p_p_state%253Dnormal%2526p_p_mode%253Dview here] (link has 585 characters)
| https://sharewind.eu/records/f9d31abbc2824284b8c1d28894d9619a (data found at https://zenodo.org/record/1162738)
|-
! Sample Size
|| 4 KB
| 37 MB
|-
| colspan="6" style="background: #ABE" |
|-
! rowspan="4" | Findable
| F1. (meta)data are assigned a globally unique and persistent identifier
! Does the dataset have any identifiers assigned?
|| Web adress (URL)
| Globally unique, citable and persistant (e.g. DOI, PURL, ARK or Handle)
|-

| F2. data are described with rich metadata (defined by R1 below)
! Is the dataset identifier included in all metadata records/files describing the data?
|| No
| No
|-

| F3. metadata clearly and explicitly include the identifier of the data it describes
! How is the data described with metadata?
|| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
|-

| F4. (meta)data are registered or indexed in a searchable resource
! What type of repository or registry is the metadata record in?
|| Domain-specific repository
| Data is in one place but discoverable through several registries
|-

! rowspan="3" | Accessible

| A1. (meta)data are retrievable by their identifier using a standardized communications protocol
! How accessible is the data?
|| Publicly accessible
| Publicly accessible
|-
| A1.1 the protocol is open, free, and universally implementable; A1.2 the protocol allows for an authentication and authorization procedure, where necessary
! Is the data available online without requiring specialised protocols or tools once access has been approved?
|| File download from online location
| Standard web service API (e.g. OGC)
|-
| A2. metadata are accessible, even when the data are no longer available
! Will the metadata record be available even if the data is no longer available?
|| Unsure
| Yes
|-

! rowspan="3" | Interoperable
| I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
! What (file) format(s) is the data available in?
|| In a structured, open standard, machine-readable format
|| In a structured, open standard, non-machine-readable format
|-
| I2. (meta)data use vocabularies that follow FAIR principles
! What best describes the types of vocabularies/ontologies/tagging schemas used to define the data elements?
|| Standardised vocabularies/ontologies/schema without global identifiers
|| Standardised vocabularies/ontologies/schema without global identifiers
|-
| I3. (meta)data include qualified references to other (meta)data
! How is the metadata linked to other data and metadata (to enhance context and clearly indicate relationships)?
|| There are no links to other metadata
|| Metadata is represented in a machine readable format, e.g. in a linked data format such as Resource Description Framework (RDF)
|-

! rowspan="3" | Reusable
| R1.1. (meta)data are released with a clear and accessible data usage license
! Which of the following best describes the license/usage rights attached to the data?
|| No license
| Standard machine-readable license (e.g. Creative Commons)
|-
| R1.2. (meta)data are associated with detailed provenance
! How much provenance information has been captured to facilitate data reuse?
|| Partially recorded
|| Partially recorded
|-
| R1.3. (meta)data meet domain-relevant community standards
|
|
|
|-
| colspan="6" style="background: #ABE" |
|-
! Total across F.A.I.R
|
! Low: < 50%; Medium: 50-60%; High: > 60%
|| ~ 45 %
|| ~ 85 %
|-
| colspan="6" style="background: #ABE" |
|-
! Remarks about database
||
||
||
|| The data is provided by the open-source platform Zenodo, not the project website itself. A very positive aspect is that information on different versions of the data as well as the number of views and downloads is available.
|-
! Identified gaps
||
||
||
* No DOI, only URL
* Extremely long URL link spanning several lines with 585 characters, that can easily become corrupted
* No links to other metadata
* No license information given
* Provenance not fully recorded

||
* Dataset identifiers should be included in the files describing the data
* Provenance details are only partially recorded with a note with contact name for further information
|}

= Metadata assessment =

== Dublin Core Elements ==
{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ The DCMI Metadata Terms lists the current set of the Dublin Core vocabulary. Source: [https://dublincore.org/specifications/dublin-core/dcmi-terms/ Dublin Core Metadata Initiative]
| 1 || abstract || 11 || contributor || 21 || extent || 31 || isReplacedBy || 41 || publisher || 51 || tableOfContents
|-
| 2 || accessRights || 12 || coverage || 22 || format || 32 || isRequiredBy || 42 || references || 52 || temporal
|-
| 3 || accrualMethod || 13 || created || 23 || hasFormat || 33 || issued || 43 || relation || 53 || title
|-
| 4 || accrualPeriodicity || 14 || creator || 24 || hasPart || 34 || isVersionOf || 44 || replaces || 54 || type
|-
| 5 || accrualPolicy || 15 || date || 25 || hasVersion || 35 || language || 45 || requires || 55 || valid
|-
| 6 || alternative || 16 || dateAccepted || 26 || identifier || 36 || license || 46 || rights || ||
|-
| 7 || audience || 17 || dateCopyrighted || 27 || instructionalMethod || 37 || mediator || 47 || rightsHolder || ||
|-
| 8 || available || 18 || dateSubmitted || 28 || isFormatOf || 38 || medium || 48 || source || ||
|-
| 9 || bibliographicCitation || 19 || description || 29 || isPartOf || 39 || modified || 49 || spatial || ||
|-
| 10 || conformsTo || 20 || educationLevel || 30 || isReferencedBy || 40 || provenance || 50 || subject || ||
|}

'''Metadata Category Types according to [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO]'''

There are three main types of metadata:
* Descriptive metadata describes a resource for purposes such as discovery and identification. It can include elements such as title, abstract, author, and keywords.
* Structural metadata indicates how compound objects are put together, for example, how pages are ordered to form chapters.
* Administrative metadata provides information to help manage a resource, such as when and how it was created, file type and other technical information, and who can access it. There are several subsets of administrative data; two that sometimes are listed as separate metadata types are:
** Rights management meta-data, which deals with intellectual property rights,and
** Preservation metadata, which contains information needed to archive and preserve a resource.
''

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Simple Dublin Core Elements. 15 DC elements with their (shortened) official definitions and examples.
! DC element name !! DC definition ([https://www.dublincore.org/specifications/dublin-core/dces/ DCMI]) !! Example (Energy Domain) !! Category (determined with the aid of [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO])
|-
! Title
| A name given to the resource || Outdoor monitoring of a hybrid micro-CPV solar panel with integrated micro-tracking and diffuse capture || Descriptive
|-
! Subject
| The topic of the resource || concentrator photovoltaics; CPV; rooftop photovoltaics; integrated tracking || Descriptive
|-
! Description
| An account of the resource || Dataset from the outdoor characterization of a B Series module from Insolight at the rooftop of the Instituto de Energía Solar - Universidad Politécnica de Madrid. …. || Descriptive
|-
! Creator
| An entity primarily responsible for making the resource || Stephen Askins; Gaël Nardin || Descriptive
|-
! Publisher
| An entity responsible for making the resource available || Stephen Askins || Descriptive
|-
! Contributor
| An entity responsible for making contributions to the resource || Stephen Askins || Descriptive
|-
! Date
| A point or period of time associated with an event in the lifecycle of the resource || 2019-07-23; Date will be associated with the creation or availability of the resource. || Administrative
|-
! Type
| The nature or genre of the resource || info:eu-repo/semantics/other; dataset || Descriptive
|-
! Format
| The file format, physical medium, or dimensions of the resource || image
|| Structural
|-
! Identifier
| An unambiguous reference to the resource within a given context || https://zenodo.org/record/3346823; 10.5281/zenodo.3346823; oai:zenodo.org:3346823 || Descriptive
|-
! Source
| A related resource from which the described resource is derived || RC607.A26W574 1996 [where "RC607.A26W574 1996" is the call number of the print version of the resource, from which the present version was scanned] || Descriptive
|-
! Language
| A language of the resource || eng || Descriptive
|-
! Relation
| A related resource || info:eu-repo/grantAgreement/EC/H2020/787289/ || Descriptive
|-
! Coverage
| The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant || 1995-1996; Madrid, Spain || Descriptive
|-
! Rights
| Information about rights held in and over the resource || info:eu-repo/semantics/openAccess; https://creativecommons.org/licenses/by/4.0/legalcode || Administrative
|}

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Non-DC elements

! Non-DC element name !! Definition !! Example !! Category
|-
! Date of availability
| Since when is the data available
||
|| Impact/Exploitation

|-
! Number of users
| Is there any information on the number of accesses or users?
||
|| Impact/Exploitation

|-
! Number of scientific publications where data is cited/used
| Is there any information on the number of citations/uses?
||
|| Impact/Exploitation

|-
! Curation activities (is the versioning ongoing)
| Update, maintainance
||
|| Impact/Exploitation
|}

Attempting to put a value on Impact/Exploitation:
* '''ENTSO-E''': Found 172 results in Advanced Search (ALL) in Web of Science ([https://apps.webofknowledge.com/ WoSc]) (16/06/2020). Citations of all these search result add up to 714. This however does not mean, that all search results really explicitly used data from ENTSO-E. Some (for sure, for some were found) only mentioned it saying they adoped workflow etc. from the site.
* '''SMARD''': Found 82 results in Advanced Search (ALL=(SMARD)) in Web of Science ([https://apps.webofknowledge.com/ WoSc]) (16/06/2020). However: SMARD is also an acronym for ''Spinal muscular atrophy with respiratory distress (SMARD)''. This somewhat complicates results and needs some further discussion. All 82 search results accumumlated 2359 citations themselves.

Gap analysis

2020-08-28T09:24:37Z

Christopherbs: /* Results for SMILES and ShareWind */

<accesscontrol>Administrators,consortium</accesscontrol>

This is the page for the gap analysis.

= Tacit knowledge "FAIRification and opening of low carbon energy research data" =
 Consortium members can add any time, they feel that something is important to note even though it is not mentioned in a standard deliverable. In other words, this page collects uncodified, tacit knowledge. It will help us later to compile suggestions and lessons learned. This kind of knowledge is collected two-fold:
#Anytime if someone feels that this should be noted. Please write down: Issue, Date, Author (could also be "anonymous"), the issue described in a few words or maximal lines>
#During the final day of workshops

=== Learning process: ===

*People are hesitant to adopt new IT technologies, this is even the case among researchers heavily relying on data, algorithms and collaborative online software (e.g., R, platforms, online conferencing, HPC, ...). The effort to encourage change is not to underestimate. Reasons are several, notably, lack of time and uncertainty about potential benefit as well as overall risk aversion preferences. EERAdata is using the online software "Only Office" to facilitate collaboration (in particular also during the Covid-19 period).
 '''FAIR and open criteria:'''
*Consortium members have a fair understanding of what FAIR/O is, but there is little knowledge and/or technical experience on how to approach the FAIRification and opening. All, however, share the view that we are at a critical point in time, where we need to implement these criteria. 
*To deepen knowledge about FAIR/O criteria, it is useful to test the criteria on a database one is familiar with. For this purpose (and to start brainstorming about the platform), AIT has developed a questionnaire for application in the use cases.
'''Metadata:'''
*A good starting point is to think about metadata and to look into existing metadata concepts in one's field. The first step is to understand that also metadata need to adhere to the FAIR/O principles.
*The next step is to increase knowledge on IT specific terms, i.e. to understand what the difference is between different '''[https://energydata.onlyoffice.eu/Products/Files/doceditor.aspx?fileid=5404340 metadata frameworks] (taxonomy, thesaurus, ontology) as well as classification of metadata (high-level, medium-level, low-level OR administrative, structural and descriptive metadata).  '''WP 2 being in charge of aligning approaches between use cases, participates in all use case kickoffs to bring everybody on the same page. The presentation is linked with "metadata frameworks".
*It is useful to supply consortium members with read aheads and watch aheads on metadata to prepare the '''[https://energydata.onlyoffice.eu/Products/Files/doceditor.aspx?fileid=5296590 first EERAdata workshop]'''. The workshop starts applications and discussions in the use cases break out sessions (and bringing insights back to the plenary), using selected databases.

= FAIR Assessment=

== FAIR Guiding Principles ==
{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ This table shows the FAIR guiding principles described by [https://www.doi.org/10.1038/sdata.2016.18 Wilkinson et al., 2016].

! To be Findable
|
* F1. (meta)data are assigned a globally unique and persistent identifier
* F2. data are described with rich metadata (defined by R1 below)
* F3. metadata clearly and explicitly include the identifier of the data it describes
* F4. (meta)data are registered or indexed in a searchable resource

|-
! To be Accessible
|
* A1. (meta)data are retrievable by their identifier using a standardized communications protocol
** A1.1 the protocol is open, free, and universally implementable
** A1.2 the protocol allows for an authentication and authorization procedure, where necessary
* A2. metadata are accessible, even when the data are no longer available

|-
! To be Interoperable
|
* I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
* I2. (meta)data use vocabularies that follow FAIR principles
* I3. (meta)data include qualified references to other (meta)data

|-
! To be Reusable
|
* R1. meta(data) are richly described with a plurality of accurate and relevant attributes
* R1.1. (meta)data are released with a clear and accessible data usage license
* R1.2. (meta)data are associated with detailed provenance
* R1.3. (meta)data meet domain-relevant community standards

|}

== Identified gaps after Workshop 1 (02/06/2020 – 04/06/2020) ==

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ This table shows a summary of the specific issues that were identified in Workshop 1. The aim was to categorise different issues to get a better understanding of how to tackle these challenges.
! FAIR/O !! Specific FAIR/O !! Gaps !! Gaps for energy domain !! Use case specific gaps !! Consequences for researchers
|-
! F
| F2
|| Metadata scope
|| Different fields require different metadata (potentially very specific)
|| UC3: lack of additional metadata for applications of materials
|| Data are less useful for the specific field
|-
! F
|| F1/F3
|| Identifiers
||
* Missing identifiers on websites
* Identifier not in downloaded data files
||
|| Makes research more difficult.
|-
! I
| I2
|| Taxonomy/ontology/common vocabulary and language issues
||
* Lack of standardisation
* Heterogeneous data makes standardisation hard
* No vocabulary documentation on websites
* Words used for same term in other languages may differ
* Databases in other languages than English
||
|| Research costs more time
|-
! I
| I3
|| References to other (meta)data
||
* No linking to source documents and related publications (for contextual knowledge)
* Possible need for links to other fields
|| UC3: link between microscopic and macroscopic materials (e.g., turbine blades)
||
* Data cannot be connected to the source
* Makes research more difficult.

|-
! R
| R1.1
|| Licensing
||
* No licenses available may mean the data is not reusable for the researcher
* Licenses not clear and accessible
* Obtaining licenses may result in more effort and costs
||
|| Data potentially not reusable

|-
! R
| R1.2
|| Reliability and provenance of data
|| When the data is collected from different and high number of sources, its reliability decreases
||
|| Data potentially not reusable

|-
! O
|
|| Privacy concerns and expected disadvantages
|| Not publishing data due to privacy concerns (sensitive data)
|| UC2: In case of distribution network data this is relevant
|| Data not accessible

|-
! Other
|
|| Conducting FAIR assessments
||
* No qualitative assessment of the data itself (which may be limited)
* Discrepancies between results conducted by humans versus machines
* Sometimes not even DC standards are met
* Metadata is not updated
* Lacking encryption of websites (https)
* Problems assessing whether metadata will be available after data is unavailable
* Interface design/layout may be unclear, incomplete or not intuitive for humans
* Authentication details (user registration login / good or bad, clarify)

||
* UC1 lack of data availability for time-series
||
* Assessment is uncertain (human assessment may need more clarification and understanding)
* Makes research more time-intensive

|}

== FAIR Self-Assessment tool ==

To conduct a human FAIR evaluation for different databases and their respective datasets, different free online tools were considered. Some seemed to be quite useful (e.g. [https://satifyd.dans.knaw.nl/ SATISFYD] ) and some were simple self-evaluation PDFs to be printed and filled out.

We decided to chose the [https://ardc.edu.au/resources/working-with-data/fair-data/fair-self-assessment-tool/ self-assessment tool] provided by the Australian Research Data Commons (ARDC). The decision was based manly upon the fact that the tools questions closely matched the FAIR categories specified by [https://www.doi.org/10.1038/sdata.2016.18 Wilkinson et al., 2016] which would make it easily comparable to the existing preferable FAIR conditions and aid with the repeatability of the results, and the half-automated scoring features that show the extent of the "FAIRness" of the analysed sample as a simple bar chart.

The result of the FAIR assessment is provided as a bar chart where an empty bar chart displays 0 % FAIR compatibility, and a full bar chart represents 100 % FAIR compatibility. The score is therefore not returned as a specific value. During the assessment below, the "Total Across FAIR" in Table ... was estimated according to the level of the bar chart from 0 % to 100 %.
Resulting scores where categorised as follows: Low: < 50%; Medium: 50-60%; High: > 60% .

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ All options for each question of the ARDCs self-assessment tool
!colspan="4"|Findable
!colspan="3"|Accessible
!colspan="3"|Interoperable
!colspan="3"|Reusable
|-
| F1. (meta)data are assigned a globally unique and persistent identifier
|| F2. data are described with rich metadata (defined by R1 below)
|| F3. metadata clearly and explicitly include the identifier of the data it describes
|| F4. (meta)data are registered or indexed in a searchable resource
|| A1. (meta)data are retrievable by their identifier using a standardized communications protocol
|| A1.1 the protocol is open, free, and universally implementable and A1.2 the protocol allows for an authentication and authorization procedure, where necessary
|| A2. metadata are accessible, even when the data are no longer available
|| I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
|| I2. (meta)data use vocabularies that follow FAIR principles
|| I3. (meta)data include qualified references to other (meta)data
|| R1.1. (meta)data are released with a clear and accessible data usage license
|| R1.2. (meta)data are associated with detailed provenance
|| R1.3. (meta)data meet domain-relevant community standards
|-
! Does the dataset have any identifiers assigned?
! Is the dataset identifier included in all metadata records/files describing the data?
! How is the data described with metadata?
! What type of repository or registry is the metadata record in?
! How accessible is the data?
! Is the data available online without requiring specialised protocols or tools once access has been approved?
! Will the metadata record be available even if the data is no longer available?
! What (file) format(s) is the data available in?
! What best describes the types of vocabularies/ontologies/tagging schemas used to define the data elements?
! How is the metadata linked to other data and metadata (to enhance context and clearly indicate relationships)?
! Which of the following best describes the license/usage rights attached to the data?
! How much provenance information has been captured to facilitate data reuse?
!
|-
| Globally unique, citable and persistant (e.g. DOI, PURL, ARK or Handle)
|| Yes
|| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
|| Data is in one place but discoverable through several registries
|| Publicly accessible
|| Standard web service API (e.g. OGC)
|| Yes
|| In a structured, open standard, machine-readable format
|| Standardised open and universal using resolvable global identifiers linking to explainations
|| Meadata is represented in a machine readable format, e.g. in a linked data format such as Resource Description Framework (RDF)
|| Standard machine-readable license (e.g. Creative Commons)
|| Fully recorded in a machine-readable format
||
|-
| Web adress (URL)
|| No
|| Comprehensively, but in a text-based, non-standard format
|| Generalist public repository
|| Fully accessible to person who meet explicitly stated conditions, e.g. ethics approval for sensitive data
|| Non-standard web service (e.g. OpenAPI/Swagger/informal API)
|| No
|| In a structured, open standard, non-machine-readable format
|| Standardised vocabularies/ontologies/schema without global identifiers
|| The metadata record includes URI links to related metadata, data and definitions
|| Standard text based license
|| Fully recorded in a text format
||
|-
| Local identifier
||
|| Brief title and description
|| Domain-specific repository
|| A de-identified / modified subset of the data is publicly accessible
|| File download from online location
|| Unsure
|| Mostly in a proprietary format
|| No standards have been applied in the description of data elements
|| There are no links to other metadata
|| Non-standard machine-readably license (clearly indicating under what conditions the data may be reused)
|| Partially recorded
||
|-
| No identifier
||
|| The data is not described
|| Local institutional repository
|| Embaged access after a specific date
|| By individual arrangement
||
||
|| Data elements not described
||
|| Non-standard text-based license
|| No provenance information is recorded
||
|-
|
||
||
|| The data is not described in any repository
|| Unspecified conditional access e.g. contact the data custodian for access
|| No access to data
||
||
||
||
|| No license
||
||
|-
|
||
||
||
|| Access to metadata only
||
||
||
||
||
||
||
||
|-
|
||
||
||
|| No access to data or metadata
||
||
||
||
||
||
||
||
|}

At this point it is important to note that the assessment of databases and/or datasets using this tool is solely considered a human assessment. This may lead to different evaluations by different users. To counteract this phenomenon, it was suggested that the evaluations should be completed using the 'four-eyes principle' to create more verifiable results. Also, it is planned to additionally evaluate the respective databases using an external tool to perform a machine-assessment.

In addition, not all questions are weighted equally and thus a worse result for one question may not have a large impact whereas only choosing the second-best option in another question will bring down the overall FAIR value considerably.

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ The ARDC tool provides detailed information about the requirements for each of the options of its tool. These descriptions are documented here.
|-
! rowspan="5" | F
|| F
|| About Findable
|| Making data Findable includes assigning a persistent identifier (like a DOI or Handle ), having rich metadata to describe the data and making sure it is findable through disciplinary and generalist discovery portals (local and international).

|-
| F1
|| About identifiers
|| Identifiers are essential for identifying, finding, retrieving, linking and citing datasets. A Web address (URL) can be used to specify the online location of a resource but over time URLs tend to change which leads to broken links to the data. To be useful, identifiers need to be persistent and unique. Digital Object Identifiers (DOIs) and other persistent identifiers (PIDs) provide a permanent citable reference to a particular dataset. The DOI is a permanent fixed reference to the dataset no matter where it is located online and enables citation and citation metrics. Services to create a persistent identifier are often offered by your affiliated institution or the repository you are using to describe your data. Talk to your library service for more information.
|-
| F2
|| About identifiers
|| The identifier (preferably a persistent identifier) needs to be clearly stated in the metadata record describing the data collection, and also in any associated data files or metadata.

|-
|F3
|| About metadata descriptions
|| Comprehensive metadata records will include descriptive content that facilitates discovery, access and reuse of the data being described. While there is no 'one size fits all' list, comprehensive metadata should include:
* a globally unique persistent identifier e.g. a DOI
* a title
* related people, i.e. the data creator or custodian
* how to access the data and file formats
* a description of how the data were created and how to interpret the data subject or keywords
* citation information that clearly indicates how the data should be cited
* a machine-readable data licence
* provenance and contextual information such as:
* links to related publications, projects, services and software
* methodology and processes involved in data production
* spatial and temporal coverage (if relevant)
* object-level data description

Providing metadata in a standard schema allows it to be read and used by machines as well as humans.

|-
| F4
|| About metadata repositories
|| A rich metadata description alone does not ensure a dataset’s ‘findability’ on the internet; the dataset needs to be registered or indexed in a searchable resource, such as a generalist (e.g. Figshare, Dryad, Zenodo domain-specific (e.g. PANGAEA, ADA, ICPSR), or institutional (e.g. ANU Research Repository , Sydney eScholarship Repository, data.gov.au) data repository or registry. Ideally these repositories/registries are indexed by search engines such as Google and/or Google Scholar.

|-
! rowspan = "4" | A
| A
|| About Accessible
|| To make data accessible may include making the data open using a standardised protocol. However the data does not necessarily have to be open. There are sometimes good reasons why data cannot be made open, for example privacy concerns, national security or commercial interests. If it is not open there should be clarity and transparency around the conditions governing access and reuse
|-
| A1
|| About data accessibility
|| Not all data that is discoverable can be freely accessed. Often there are embargoes, access controls, and access permissions associated with data due to a variety of issues such as privacy and commercial interests. Even with all these issues much sensitive data can be shared. Many other issues that could be perceived as blockers to sharing data may be overcome.
|-
| A1.1/A1.2
|| About protocols
|| Ideally users would like to retrieve appropriate internet content directly and unhindered once they have located it. Internet protocols (e.g. http and ftp) define rules and conventions for communication between devices, and tools and services are available to facilitate this process, e.g. APIs.

Protocols:
* HTTP (Hypertext Transfer Protocol) is the set of rules for transferring files (text, graphic images, sound, video, and other multimedia files) on the World Wide Web. As soon as a Web user opens their Web browser, the user is indirectly making use of HTTP.
* FTP (File Transfer Protocol) is a standard Internet protocol for transmitting files between computers on the Internet over TCP/IP connections. Read more: Web protocols
* API (Application Programming Interface): When information is made available in any machine readable format, it becomes possible to make that information directly available to programs that request that information over the web. An API is the way this information is made directly available to other machines.

|-
| A2
|| About metadata availability
|| Effort is often required to maintain data resources online which can often lead to it being neglected especially over long periods of time. This often leads to broken links between the metadata and the data. Having at least a description of the data in the form of a metadata record means there is a record of the data's existence allowing the possibility of someone tracking it down.

|-
! rowspan="4" | I
|| I
|| About Interoperability
|| To be interoperable (i.e. data that is interpretable by a computer, so that they can be automatically combined with other data) the data will need to use community agreed formats, language and vocabularies. The metadata will also need to use a community agreed standards and vocabularies, and contain links to related information using identifiers.
|-
|| I1
|| About file formats
|| When selecting file formats for archiving, the formats should ideally be:
* Non-proprietary
* Unencrypted
* Uncompressed
* In common usage by the research community
* Adherent to an open, documented standard, such as described by the State of California (see AB 1668, 2007)
* Interoperable among diverse platforms and applications
* Fully published and available royalty-free
* Fully and independently implementable by multiple software providers on multiple platforms without any intellectual property restrictions for necessary technology
* Developed and maintained by an open standards organization with a well-defined inclusive process for evolution of the standard.

From: Stanford University Library - Best practices for file formats
File format examples:
* Structured, open standard, machine-readable format e.g. (text) PDF/A, HTML, Plain text, (images) TIFF, JPEG 2000, GIF, (audio) MP3, AIFF, WAVE, (video) MOV, MPEG, AVI, (Tabular data) CSV
* structured, open standard, non-machine-readable format, e.g. PDF, HTML, JPG
* Proprietary format, e.g. doc (Word), .xls (Excel), .ppt (PowerPoint), .sav

Read more:
* ANDS
* European Data Portal

|-
|| I2
|| About schema standards
|| Schema standards are schemas that have gone through a formal validation process by a standards organisation, such as the International Standards Organisation (ISO) or an equivalent body such as the Dublin Core Metadata Initiative (DCMI); or, commonly used and consistently applied metadata schemas that are well documented, endorsed, and maintained can also become ‘de-facto standards’, e.g. RIF-CS for describing data collections and services used in Research Data Australia.

Read more: [https://www.ands.org.au/guides/metadata-working Metadata schema]

Standardised open and universal schemas, e.g, [https://www.dcc.ac.uk/resources/metadata-standards/datacite-metadata-schema DataCite Metadata Schema], [https://www.dcc.ac.uk/resources/metadata-standards/prov PROV], [https://www.dcc.ac.uk/resources/metadata-standards/dublin-core Dublin Core]

Domain-specific standards, e.g. [http://human-phenotype-ontology.github.io/ HPO - Human Phenotype Ontology], [https://www.nlm.nih.gov/mesh/ MeSH - Medical Subject Headings] , [https://mcp-profile-docs.readthedocs.io/en/stable/ Marine Community Profile], [https://ddialliance.org/ DDI - Data Documentation Initiative].

For more examples of standards: [http://rd-alliance.github.io/metadata-directory/ Here].

Resolvable global identifiers - An identifier is any label used to name something uniquely (whether online or offline). URLs are an example of an identifier. So are serial numbers, and personal names. A persistent identifier is guaranteed to be managed and kept up to date over a defined time period.

Read more: [https://www.ands.org.au/guides/persistent-identifiers-awareness Persistent Identifiers]

|-
||I3
|| About metadata links
|| The goal is to create as many meaningful linkages as possible between (meta)data resources to enrich the contextual knowledge about the data, balanced against the time/energy involved in making a good data model.

'''Machine-readable (meta)data'''

(Meta)data in a format that can be automatically read and processed by a computer, such as [http://opendatahandbook.org/glossary/en/terms/csv/ CSV], [http://opendatahandbook.org/glossary/en/terms/json/ JSON], [http://opendatahandbook.org/glossary/en/terms/xml/ XML] etc. For more information: [http://opendatahandbook.org/glossary/en/terms/machine-readable/ Open Data Handbook]

'''Linked Data'''

Linked Data (also known as Linking Data) can be applied to improve the exploitation of the “Web of data.” The expression refers to the publishing of structured data in a way that typed links are created between data from different sources to provide a higher level of usability. By using Linked Data, it is possible to find other, related data. Structured data should meet four requirements to be called
Linked Data:

* URIs should be assigned to all entities of the dataset.
* HTTP URIs are required to ensure that all entities can be referenced and cited by users and user agents.
* Entities should be described using standard formats such as RDF/XML.
* Links should be created to other, related entity URIs.

All data that fulfil these requirements and are released for the public are called Linked Open Data (LOD). http://www.lesliesikos.com/semantic-web-machine-readable-structured-data-with-meaningful-annotations/

Read more under the heading (meta)data include qualified references to other (meta)data in the following resource: https://www.dtls.nl/fair-data/fair-principles-explained/

For more information on Linked Data standards - RDF - https://www.w3.org/RDF/

Other related linked data standards:
* OWL - https://www.w3.org/OWL/
* SKOS - https://www.w3.org/2004/02/skos/
* SPARQL - https://www.w3.org/TR/rdf-sparql-query/

|-
! rowspan="3" | R
|| R
|| About Reusable
|| Reusable data should maintain its initial richness. For example, it should not be abridged for the purpose of explaining the findings in one particular publication. It needs a clear machine-readable [https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse licence] and [https://www.ands.org.au/partners-and-communities/ands-communities/data-provenance-interest-group provenance] information on how the data was formed. It should also use discipline-specific data and [https://www.ands.org.au/working-with-data/metadata metadata] standards to give it rich contextual information that will allow for accurate interpretation and reuse.

|-
|| R1.1
|| About licenses
|| If data is not licensed no-one else can use it. In Australia, no licence is regarded as the same as 'all rights reserved', confining any reuse to very limited circumstances. Applying a Creative Commons licence to your data is a simple way to ensure that your data can be reused. The less restrictive the licence, the more that can be done with the data.
To make it easy for the Web to know when a work is available under a particular license, a “machine readable” version of the license provides a summary of the key freedoms and obligations written into a format that software systems, search engines, and other kinds of technology can understand.
Example from the [https://creativecommons.org/choose/ Creative Commons License Choser]:

What appears in the metadata:

This work is licensed under a [https://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International License].

Code snippet:

<code>
<a rel="license" href="http://creativecommons.org/licenses/by/4.0/">

<img alt="Creative Commons License" style="border-width:0"

src="https://i.creativecommons.org/l/by/4.0/88x31.png"/>

</a> This work is licensed under a

<a rel="license" href="http://creativecommons.org/licenses/by/4.0/">

Creative Commons Attribution 4.0 International License</a>.
<\code>

Read more: https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse

|-
||R1.2
|| About provenance
|| Data provenance is used to document where a piece of data comes from and the process and methodology by which it is produced. It is important to confirm the authenticity of data enabling trust, credibility and reproducibility. This is becoming increasingly important, especially in the eScience community where research is data intensive and often involves complex data transformations and procedures ([https://www.ands.org.au/partners-and-communities/ands-communities/data-provenance-interest-group Read more]). Provenance vocabularies such as [https://www.w3.org/2005/Incubator/prov/XGR-prov-20101214/#Recommendations Provenir Ontology (PROV-O) and others].

|}

== Results for SMILES and ShareWind ==

{| class="mw-collapsible wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Datasets from the SMILES and ShareWind platforms was analysed using the self-assessment tool provided by ARDC.
! rowspan="5" |
| rowspan="5" |
! Database Code
| rowspan="24" style="background: #ABE" |
| ED4
| ED9
|-
! Database Title
|| SMILES
| ShareWind
|-
! Database Link
|| https://www.ecria-smiles.eu/
| https://sharewind.eu/
|-
! Sample Link
|| [https://www.ecria-smiles.eu/data-files/-/document_library/W32cCAfL2jMX/view_file664466?_com_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX_redirect=https%3A%2F%2Fwww.ecria-smiles.eu%3A443%2Fdata-files%2F-%2Fdocument_library%2FW32cCAfL2jMX%2Fview%2F664423%3F_com_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX_redirect%3Dhttps%253A%252F%252Fwww.ecria-smiles.eu%253A443%252Fdata-files%253Fp_p_id%253Dcom_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX%2526p_p_lifecycle%253D0%2526p_p_state%253Dnormal%2526p_p_mode%253Dview here] (link has 585 characters)
| https://sharewind.eu/records/f9d31abbc2824284b8c1d28894d9619a (data found at https://zenodo.org/record/1162738)
|-
! Sample Size
|| 4 KB
| 37 MB
|-
| colspan="6" style="background: #ABE" |
|-
! rowspan="4" | Findable
| F1. (meta)data are assigned a globally unique and persistent identifier
! Does the dataset have any identifiers assigned?
|| Web adress (URL)
| Globally unique, citable and persistant (e.g. DOI, PURL, ARK or Handle)
|-

| F2. data are described with rich metadata (defined by R1 below)
! Is the dataset identifier included in all metadata records/files describing the data?
|| No
| No
|-

| F3. metadata clearly and explicitly include the identifier of the data it describes
! How is the data described with metadata?
|| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
|-

| F4. (meta)data are registered or indexed in a searchable resource
! What type of repository or registry is the metadata record in?
|| Domain-specific repository
| Data is in one place but discoverable through several registries
|-

! rowspan="3" | Accessible

| A1. (meta)data are retrievable by their identifier using a standardized communications protocol
! How accessible is the data?
|| Publicly accessible
| Publicly accessible
|-
| A1.1 the protocol is open, free, and universally implementable; A1.2 the protocol allows for an authentication and authorization procedure, where necessary
! Is the data available online without requiring specialised protocols or tools once access has been approved?
|| File download from online location
| Standard web service API (e.g. OGC)
|-
| A2. metadata are accessible, even when the data are no longer available
! Will the metadata record be available even if the data is no longer available?
|| Unsure
| Yes
|-

! rowspan="3" | Interoperable
| I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
! What (file) format(s) is the data available in?
|| In a structured, open standard, machine-readable format
|| In a structured, open standard, non-machine-readable format
|-
| I2. (meta)data use vocabularies that follow FAIR principles
! What best describes the types of vocabularies/ontologies/tagging schemas used to define the data elements?
|| Standardised vocabularies/ontologies/schema without global identifiers
|| Standardised vocabularies/ontologies/schema without global identifiers
|-
| I3. (meta)data include qualified references to other (meta)data
! How is the metadata linked to other data and metadata (to enhance context and clearly indicate relationships)?
|| There are no links to other metadata
|| Metadata is represented in a machine readable format, e.g. in a linked data format such as Resource Description Framework (RDF)
|-

! rowspan="3" | Reusable
| R1.1. (meta)data are released with a clear and accessible data usage license
! Which of the following best describes the license/usage rights attached to the data?
|| No license
| Standard machine-readable license (e.g. Creative Commons)
|-
| R1.2. (meta)data are associated with detailed provenance
! How much provenance information has been captured to facilitate data reuse?
|| Partially recorded
|| Partially recorded
|-
| R1.3. (meta)data meet domain-relevant community standards
|
|
|
|-
| colspan="6" style="background: #ABE" |
|-
! Total across F.A.I.R
|
! Low: < 50%; Medium: 50-60%; High: > 60%
|| ~ 45 %
|| ~ 85 %
|-
| colspan="6" style="background: #ABE" |
|-
! Remarks about database
||
||
||
|| The data is provided by the open-source platform Zenodo, not the project website itself. A very positive aspect is that information on different versions of the data as well as the number of views and downloads is available.
|-
! Identified gaps
||
||
||
* No DOI, only URL
* Extremely long URL link spanning several lines with 585 characters, that can easily become corrupted
* No links to other metadata
* No license information given
* Provenance not fully recorded

||
* Dataset identifiers should be included in the files describing the data
* Provenance details are only partially recorded with a note with contact name for further information
|}

= Metadata assessment =

== Dublin Core Elements ==
{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ The DCMI Metadata Terms lists the current set of the Dublin Core vocabulary. Source: [https://dublincore.org/specifications/dublin-core/dcmi-terms/ Dublin Core Metadata Initiative]
| 1 || abstract || 11 || contributor || 21 || extent || 31 || isReplacedBy || 41 || publisher || 51 || tableOfContents
|-
| 2 || accessRights || 12 || coverage || 22 || format || 32 || isRequiredBy || 42 || references || 52 || temporal
|-
| 3 || accrualMethod || 13 || created || 23 || hasFormat || 33 || issued || 43 || relation || 53 || title
|-
| 4 || accrualPeriodicity || 14 || creator || 24 || hasPart || 34 || isVersionOf || 44 || replaces || 54 || type
|-
| 5 || accrualPolicy || 15 || date || 25 || hasVersion || 35 || language || 45 || requires || 55 || valid
|-
| 6 || alternative || 16 || dateAccepted || 26 || identifier || 36 || license || 46 || rights || ||
|-
| 7 || audience || 17 || dateCopyrighted || 27 || instructionalMethod || 37 || mediator || 47 || rightsHolder || ||
|-
| 8 || available || 18 || dateSubmitted || 28 || isFormatOf || 38 || medium || 48 || source || ||
|-
| 9 || bibliographicCitation || 19 || description || 29 || isPartOf || 39 || modified || 49 || spatial || ||
|-
| 10 || conformsTo || 20 || educationLevel || 30 || isReferencedBy || 40 || provenance || 50 || subject || ||
|}

'''Metadata Category Types according to [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO]'''

There are three main types of metadata:
* Descriptive metadata describes a resource for purposes such as discovery and identification. It can include elements such as title, abstract, author, and keywords.
* Structural metadata indicates how compound objects are put together, for example, how pages are ordered to form chapters.
* Administrative metadata provides information to help manage a resource, such as when and how it was created, file type and other technical information, and who can access it. There are several subsets of administrative data; two that sometimes are listed as separate metadata types are:
** Rights management meta-data, which deals with intellectual property rights,and
** Preservation metadata, which contains information needed to archive and preserve a resource.
''

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Simple Dublin Core Elements. 15 DC elements with their (shortened) official definitions and examples.
! DC element name !! DC definition ([https://www.dublincore.org/specifications/dublin-core/dces/ DCMI]) !! Example (Energy Domain) !! Category (determined with the aid of [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO])
|-
! Title
| A name given to the resource || Outdoor monitoring of a hybrid micro-CPV solar panel with integrated micro-tracking and diffuse capture || Descriptive
|-
! Subject
| The topic of the resource || concentrator photovoltaics; CPV; rooftop photovoltaics; integrated tracking || Descriptive
|-
! Description
| An account of the resource || Dataset from the outdoor characterization of a B Series module from Insolight at the rooftop of the Instituto de Energía Solar - Universidad Politécnica de Madrid. …. || Descriptive
|-
! Creator
| An entity primarily responsible for making the resource || Stephen Askins; Gaël Nardin || Descriptive
|-
! Publisher
| An entity responsible for making the resource available || Stephen Askins || Descriptive
|-
! Contributor
| An entity responsible for making contributions to the resource || Stephen Askins || Descriptive
|-
! Date
| A point or period of time associated with an event in the lifecycle of the resource || 2019-07-23; Date will be associated with the creation or availability of the resource. || Administrative
|-
! Type
| The nature or genre of the resource || info:eu-repo/semantics/other; dataset || Descriptive
|-
! Format
| The file format, physical medium, or dimensions of the resource || image
|| Structural
|-
! Identifier
| An unambiguous reference to the resource within a given context || https://zenodo.org/record/3346823; 10.5281/zenodo.3346823; oai:zenodo.org:3346823 || Descriptive
|-
! Source
| A related resource from which the described resource is derived || RC607.A26W574 1996 [where "RC607.A26W574 1996" is the call number of the print version of the resource, from which the present version was scanned] || Descriptive
|-
! Language
| A language of the resource || eng || Descriptive
|-
! Relation
| A related resource || info:eu-repo/grantAgreement/EC/H2020/787289/ || Descriptive
|-
! Coverage
| The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant || 1995-1996; Madrid, Spain || Descriptive
|-
! Rights
| Information about rights held in and over the resource || info:eu-repo/semantics/openAccess; https://creativecommons.org/licenses/by/4.0/legalcode || Administrative
|}

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Non-DC elements

! Non-DC element name !! Definition !! Example !! Category
|-
! Date of availability
| Since when is the data available
||
|| Impact/Exploitation

|-
! Number of users
| Is there any information on the number of accesses or users?
||
|| Impact/Exploitation

|-
! Number of scientific publications where data is cited/used
| Is there any information on the number of citations/uses?
||
|| Impact/Exploitation

|-
! Curation activities (is the versioning ongoing)
| Update, maintainance
||
|| Impact/Exploitation
|}

Attempting to put a value on Impact/Exploitation:
* '''ENTSO-E''': Found 172 results in Advanced Search (ALL) in Web of Science ([https://apps.webofknowledge.com/ WoSc]) (16/06/2020). Citations of all these search result add up to 714. This however does not mean, that all search results really explicitly used data from ENTSO-E. Some (for sure, for some were found) only mentioned it saying they adoped workflow etc. from the site.
* '''SMARD''': Found 82 results in Advanced Search (ALL=(SMARD)) in Web of Science ([https://apps.webofknowledge.com/ WoSc]) (16/06/2020). However: SMARD is also an acronym for ''Spinal muscular atrophy with respiratory distress (SMARD)''. This somewhat complicates results and needs some further discussion. All 82 search results accumumlated 2359 citations themselves.

Gap analysis

2020-08-28T08:43:32Z

Christopherbs: /* Dublin Core Evaluation */

<accesscontrol>Administrators,consortium</accesscontrol>

This is the page for the gap analysis.

= Tacit knowledge "FAIRification and opening of low carbon energy research data" =
 Consortium members can add any time, they feel that something is important to note even though it is not mentioned in a standard deliverable. In other words, this page collects uncodified, tacit knowledge. It will help us later to compile suggestions and lessons learned. This kind of knowledge is collected two-fold:
#Anytime if someone feels that this should be noted. Please write down: Issue, Date, Author (could also be "anonymous"), the issue described in a few words or maximal lines>
#During the final day of workshops

=== Learning process: ===

*People are hesitant to adopt new IT technologies, this is even the case among researchers heavily relying on data, algorithms and collaborative online software (e.g., R, platforms, online conferencing, HPC, ...). The effort to encourage change is not to underestimate. Reasons are several, notably, lack of time and uncertainty about potential benefit as well as overall risk aversion preferences. EERAdata is using the online software "Only Office" to facilitate collaboration (in particular also during the Covid-19 period).
 '''FAIR and open criteria:'''
*Consortium members have a fair understanding of what FAIR/O is, but there is little knowledge and/or technical experience on how to approach the FAIRification and opening. All, however, share the view that we are at a critical point in time, where we need to implement these criteria. 
*To deepen knowledge about FAIR/O criteria, it is useful to test the criteria on a database one is familiar with. For this purpose (and to start brainstorming about the platform), AIT has developed a questionnaire for application in the use cases.
'''Metadata:'''
*A good starting point is to think about metadata and to look into existing metadata concepts in one's field. The first step is to understand that also metadata need to adhere to the FAIR/O principles.
*The next step is to increase knowledge on IT specific terms, i.e. to understand what the difference is between different '''[https://energydata.onlyoffice.eu/Products/Files/doceditor.aspx?fileid=5404340 metadata frameworks] (taxonomy, thesaurus, ontology) as well as classification of metadata (high-level, medium-level, low-level OR administrative, structural and descriptive metadata).  '''WP 2 being in charge of aligning approaches between use cases, participates in all use case kickoffs to bring everybody on the same page. The presentation is linked with "metadata frameworks".
*It is useful to supply consortium members with read aheads and watch aheads on metadata to prepare the '''[https://energydata.onlyoffice.eu/Products/Files/doceditor.aspx?fileid=5296590 first EERAdata workshop]'''. The workshop starts applications and discussions in the use cases break out sessions (and bringing insights back to the plenary), using selected databases.

= FAIR Assessment=

== FAIR Guiding Principles ==
{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ This table shows the FAIR guiding principles described by [https://www.doi.org/10.1038/sdata.2016.18 Wilkinson et al., 2016].

! To be Findable
|
* F1. (meta)data are assigned a globally unique and persistent identifier
* F2. data are described with rich metadata (defined by R1 below)
* F3. metadata clearly and explicitly include the identifier of the data it describes
* F4. (meta)data are registered or indexed in a searchable resource

|-
! To be Accessible
|
* A1. (meta)data are retrievable by their identifier using a standardized communications protocol
** A1.1 the protocol is open, free, and universally implementable
** A1.2 the protocol allows for an authentication and authorization procedure, where necessary
* A2. metadata are accessible, even when the data are no longer available

|-
! To be Interoperable
|
* I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
* I2. (meta)data use vocabularies that follow FAIR principles
* I3. (meta)data include qualified references to other (meta)data

|-
! To be Reusable
|
* R1. meta(data) are richly described with a plurality of accurate and relevant attributes
* R1.1. (meta)data are released with a clear and accessible data usage license
* R1.2. (meta)data are associated with detailed provenance
* R1.3. (meta)data meet domain-relevant community standards

|}

== Identified gaps after Workshop 1 (02/06/2020 – 04/06/2020) ==

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ This table shows a summary of the specific issues that were identified in Workshop 1. The aim was to categorise different issues to get a better understanding of how to tackle these challenges.
! FAIR/O !! Specific FAIR/O !! Gaps !! Gaps for energy domain !! Use case specific gaps !! Consequences for researchers
|-
! F
| F2
|| Metadata scope
|| Different fields require different metadata (potentially very specific)
|| UC3: lack of additional metadata for applications of materials
|| Data are less useful for the specific field
|-
! F
|| F1/F3
|| Identifiers
||
* Missing identifiers on websites
* Identifier not in downloaded data files
||
|| Makes research more difficult.
|-
! I
| I2
|| Taxonomy/ontology/common vocabulary and language issues
||
* Lack of standardisation
* Heterogeneous data makes standardisation hard
* No vocabulary documentation on websites
* Words used for same term in other languages may differ
* Databases in other languages than English
||
|| Research costs more time
|-
! I
| I3
|| References to other (meta)data
||
* No linking to source documents and related publications (for contextual knowledge)
* Possible need for links to other fields
|| UC3: link between microscopic and macroscopic materials (e.g., turbine blades)
||
* Data cannot be connected to the source
* Makes research more difficult.

|-
! R
| R1.1
|| Licensing
||
* No licenses available may mean the data is not reusable for the researcher
* Licenses not clear and accessible
* Obtaining licenses may result in more effort and costs
||
|| Data potentially not reusable

|-
! R
| R1.2
|| Reliability and provenance of data
|| When the data is collected from different and high number of sources, its reliability decreases
||
|| Data potentially not reusable

|-
! O
|
|| Privacy concerns and expected disadvantages
|| Not publishing data due to privacy concerns (sensitive data)
|| UC2: In case of distribution network data this is relevant
|| Data not accessible

|-
! Other
|
|| Conducting FAIR assessments
||
* No qualitative assessment of the data itself (which may be limited)
* Discrepancies between results conducted by humans versus machines
* Sometimes not even DC standards are met
* Metadata is not updated
* Lacking encryption of websites (https)
* Problems assessing whether metadata will be available after data is unavailable
* Interface design/layout may be unclear, incomplete or not intuitive for humans
* Authentication details (user registration login / good or bad, clarify)

||
* UC1 lack of data availability for time-series
||
* Assessment is uncertain (human assessment may need more clarification and understanding)
* Makes research more time-intensive

|}

== FAIR Self-Assessment tool ==

To conduct a human FAIR evaluation for different databases and their respective datasets, different free online tools were considered. Some seemed to be quite useful (e.g. [https://satifyd.dans.knaw.nl/ SATISFYD] ) and some were simple self-evaluation PDFs to be printed and filled out.

We decided to chose the [https://ardc.edu.au/resources/working-with-data/fair-data/fair-self-assessment-tool/ self-assessment tool] provided by the Australian Research Data Commons (ARDC). The decision was based manly upon the fact that the tools questions closely matched the FAIR categories specified by [https://www.doi.org/10.1038/sdata.2016.18 Wilkinson et al., 2016] which would make it easily comparable to the existing preferable FAIR conditions and aid with the repeatability of the results, and the half-automated scoring features that show the extent of the "FAIRness" of the analysed sample as a simple bar chart.

The result of the FAIR assessment is provided as a bar chart where an empty bar chart displays 0 % FAIR compatibility, and a full bar chart represents 100 % FAIR compatibility. The score is therefore not returned as a specific value. During the assessment below, the "Total Across FAIR" in Table ... was estimated according to the level of the bar chart from 0 % to 100 %.
Resulting scores where categorised as follows: Low: < 50%; Medium: 50-60%; High: > 60% .

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ All options for each question of the ARDCs self-assessment tool
!colspan="4"|Findable
!colspan="3"|Accessible
!colspan="3"|Interoperable
!colspan="3"|Reusable
|-
| F1. (meta)data are assigned a globally unique and persistent identifier
|| F2. data are described with rich metadata (defined by R1 below)
|| F3. metadata clearly and explicitly include the identifier of the data it describes
|| F4. (meta)data are registered or indexed in a searchable resource
|| A1. (meta)data are retrievable by their identifier using a standardized communications protocol
|| A1.1 the protocol is open, free, and universally implementable and A1.2 the protocol allows for an authentication and authorization procedure, where necessary
|| A2. metadata are accessible, even when the data are no longer available
|| I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
|| I2. (meta)data use vocabularies that follow FAIR principles
|| I3. (meta)data include qualified references to other (meta)data
|| R1.1. (meta)data are released with a clear and accessible data usage license
|| R1.2. (meta)data are associated with detailed provenance
|| R1.3. (meta)data meet domain-relevant community standards
|-
! Does the dataset have any identifiers assigned?
! Is the dataset identifier included in all metadata records/files describing the data?
! How is the data described with metadata?
! What type of repository or registry is the metadata record in?
! How accessible is the data?
! Is the data available online without requiring specialised protocols or tools once access has been approved?
! Will the metadata record be available even if the data is no longer available?
! What (file) format(s) is the data available in?
! What best describes the types of vocabularies/ontologies/tagging schemas used to define the data elements?
! How is the metadata linked to other data and metadata (to enhance context and clearly indicate relationships)?
! Which of the following best describes the license/usage rights attached to the data?
! How much provenance information has been captured to facilitate data reuse?
!
|-
| Globally unique, citable and persistant (e.g. DOI, PURL, ARK or Handle)
|| Yes
|| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
|| Data is in one place but discoverable through several registries
|| Publicly accessible
|| Standard web service API (e.g. OGC)
|| Yes
|| In a structured, open standard, machine-readable format
|| Standardised open and universal using resolvable global identifiers linking to explainations
|| Meadata is represented in a machine readable format, e.g. in a linked data format such as Resource Description Framework (RDF)
|| Standard machine-readable license (e.g. Creative Commons)
|| Fully recorded in a machine-readable format
||
|-
| Web adress (URL)
|| No
|| Comprehensively, but in a text-based, non-standard format
|| Generalist public repository
|| Fully accessible to person who meet explicitly stated conditions, e.g. ethics approval for sensitive data
|| Non-standard web service (e.g. OpenAPI/Swagger/informal API)
|| No
|| In a structured, open standard, non-machine-readable format
|| Standardised vocabularies/ontologies/schema without global identifiers
|| The metadata record includes URI links to related metadata, data and definitions
|| Standard text based license
|| Fully recorded in a text format
||
|-
| Local identifier
||
|| Brief title and description
|| Domain-specific repository
|| A de-identified / modified subset of the data is publicly accessible
|| File download from online location
|| Unsure
|| Mostly in a proprietary format
|| No standards have been applied in the description of data elements
|| There are no links to other metadata
|| Non-standard machine-readably license (clearly indicating under what conditions the data may be reused)
|| Partially recorded
||
|-
| No identifier
||
|| The data is not described
|| Local institutional repository
|| Embaged access after a specific date
|| By individual arrangement
||
||
|| Data elements not described
||
|| Non-standard text-based license
|| No provenance information is recorded
||
|-
|
||
||
|| The data is not described in any repository
|| Unspecified conditional access e.g. contact the data custodian for access
|| No access to data
||
||
||
||
|| No license
||
||
|-
|
||
||
||
|| Access to metadata only
||
||
||
||
||
||
||
||
|-
|
||
||
||
|| No access to data or metadata
||
||
||
||
||
||
||
||
|}

At this point it is important to note that the assessment of databases and/or datasets using this tool is solely considered a human assessment. This may lead to different evaluations by different users. To counteract this phenomenon, it was suggested that the evaluations should be completed using the 'four-eyes principle' to create more verifiable results. Also, it is planned to additionally evaluate the respective databases using an external tool to perform a machine-assessment.

In addition, not all questions are weighted equally and thus a worse result for one question may not have a large impact whereas only choosing the second-best option in another question will bring down the overall FAIR value considerably.

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ The ARDC tool provides detailed information about the requirements for each of the options of its tool. These descriptions are documented here.
|-
! rowspan="5" | F
|| F
|| About Findable
|| Making data Findable includes assigning a persistent identifier (like a DOI or Handle ), having rich metadata to describe the data and making sure it is findable through disciplinary and generalist discovery portals (local and international).

|-
| F1
|| About identifiers
|| Identifiers are essential for identifying, finding, retrieving, linking and citing datasets. A Web address (URL) can be used to specify the online location of a resource but over time URLs tend to change which leads to broken links to the data. To be useful, identifiers need to be persistent and unique. Digital Object Identifiers (DOIs) and other persistent identifiers (PIDs) provide a permanent citable reference to a particular dataset. The DOI is a permanent fixed reference to the dataset no matter where it is located online and enables citation and citation metrics. Services to create a persistent identifier are often offered by your affiliated institution or the repository you are using to describe your data. Talk to your library service for more information.
|-
| F2
|| About identifiers
|| The identifier (preferably a persistent identifier) needs to be clearly stated in the metadata record describing the data collection, and also in any associated data files or metadata.

|-
|F3
|| About metadata descriptions
|| Comprehensive metadata records will include descriptive content that facilitates discovery, access and reuse of the data being described. While there is no 'one size fits all' list, comprehensive metadata should include:
* a globally unique persistent identifier e.g. a DOI
* a title
* related people, i.e. the data creator or custodian
* how to access the data and file formats
* a description of how the data were created and how to interpret the data subject or keywords
* citation information that clearly indicates how the data should be cited
* a machine-readable data licence
* provenance and contextual information such as:
* links to related publications, projects, services and software
* methodology and processes involved in data production
* spatial and temporal coverage (if relevant)
* object-level data description

Providing metadata in a standard schema allows it to be read and used by machines as well as humans.

|-
| F4
|| About metadata repositories
|| A rich metadata description alone does not ensure a dataset’s ‘findability’ on the internet; the dataset needs to be registered or indexed in a searchable resource, such as a generalist (e.g. Figshare, Dryad, Zenodo domain-specific (e.g. PANGAEA, ADA, ICPSR), or institutional (e.g. ANU Research Repository , Sydney eScholarship Repository, data.gov.au) data repository or registry. Ideally these repositories/registries are indexed by search engines such as Google and/or Google Scholar.

|-
! rowspan = "4" | A
| A
|| About Accessible
|| To make data accessible may include making the data open using a standardised protocol. However the data does not necessarily have to be open. There are sometimes good reasons why data cannot be made open, for example privacy concerns, national security or commercial interests. If it is not open there should be clarity and transparency around the conditions governing access and reuse
|-
| A1
|| About data accessibility
|| Not all data that is discoverable can be freely accessed. Often there are embargoes, access controls, and access permissions associated with data due to a variety of issues such as privacy and commercial interests. Even with all these issues much sensitive data can be shared. Many other issues that could be perceived as blockers to sharing data may be overcome.
|-
| A1.1/A1.2
|| About protocols
|| Ideally users would like to retrieve appropriate internet content directly and unhindered once they have located it. Internet protocols (e.g. http and ftp) define rules and conventions for communication between devices, and tools and services are available to facilitate this process, e.g. APIs.

Protocols:
* HTTP (Hypertext Transfer Protocol) is the set of rules for transferring files (text, graphic images, sound, video, and other multimedia files) on the World Wide Web. As soon as a Web user opens their Web browser, the user is indirectly making use of HTTP.
* FTP (File Transfer Protocol) is a standard Internet protocol for transmitting files between computers on the Internet over TCP/IP connections. Read more: Web protocols
* API (Application Programming Interface): When information is made available in any machine readable format, it becomes possible to make that information directly available to programs that request that information over the web. An API is the way this information is made directly available to other machines.

|-
| A2
|| About metadata availability
|| Effort is often required to maintain data resources online which can often lead to it being neglected especially over long periods of time. This often leads to broken links between the metadata and the data. Having at least a description of the data in the form of a metadata record means there is a record of the data's existence allowing the possibility of someone tracking it down.

|-
! rowspan="4" | I
|| I
|| About Interoperability
|| To be interoperable (i.e. data that is interpretable by a computer, so that they can be automatically combined with other data) the data will need to use community agreed formats, language and vocabularies. The metadata will also need to use a community agreed standards and vocabularies, and contain links to related information using identifiers.
|-
|| I1
|| About file formats
|| When selecting file formats for archiving, the formats should ideally be:
* Non-proprietary
* Unencrypted
* Uncompressed
* In common usage by the research community
* Adherent to an open, documented standard, such as described by the State of California (see AB 1668, 2007)
* Interoperable among diverse platforms and applications
* Fully published and available royalty-free
* Fully and independently implementable by multiple software providers on multiple platforms without any intellectual property restrictions for necessary technology
* Developed and maintained by an open standards organization with a well-defined inclusive process for evolution of the standard.

From: Stanford University Library - Best practices for file formats
File format examples:
* Structured, open standard, machine-readable format e.g. (text) PDF/A, HTML, Plain text, (images) TIFF, JPEG 2000, GIF, (audio) MP3, AIFF, WAVE, (video) MOV, MPEG, AVI, (Tabular data) CSV
* structured, open standard, non-machine-readable format, e.g. PDF, HTML, JPG
* Proprietary format, e.g. doc (Word), .xls (Excel), .ppt (PowerPoint), .sav

Read more:
* ANDS
* European Data Portal

|-
|| I2
|| About schema standards
|| Schema standards are schemas that have gone through a formal validation process by a standards organisation, such as the International Standards Organisation (ISO) or an equivalent body such as the Dublin Core Metadata Initiative (DCMI); or, commonly used and consistently applied metadata schemas that are well documented, endorsed, and maintained can also become ‘de-facto standards’, e.g. RIF-CS for describing data collections and services used in Research Data Australia.

Read more: [https://www.ands.org.au/guides/metadata-working Metadata schema]

Standardised open and universal schemas, e.g, [https://www.dcc.ac.uk/resources/metadata-standards/datacite-metadata-schema DataCite Metadata Schema], [https://www.dcc.ac.uk/resources/metadata-standards/prov PROV], [https://www.dcc.ac.uk/resources/metadata-standards/dublin-core Dublin Core]

Domain-specific standards, e.g. [http://human-phenotype-ontology.github.io/ HPO - Human Phenotype Ontology], [https://www.nlm.nih.gov/mesh/ MeSH - Medical Subject Headings] , [https://mcp-profile-docs.readthedocs.io/en/stable/ Marine Community Profile], [https://ddialliance.org/ DDI - Data Documentation Initiative].

For more examples of standards: [http://rd-alliance.github.io/metadata-directory/ Here].

Resolvable global identifiers - An identifier is any label used to name something uniquely (whether online or offline). URLs are an example of an identifier. So are serial numbers, and personal names. A persistent identifier is guaranteed to be managed and kept up to date over a defined time period.

Read more: [https://www.ands.org.au/guides/persistent-identifiers-awareness Persistent Identifiers]

|-
||I3
|| About metadata links
|| The goal is to create as many meaningful linkages as possible between (meta)data resources to enrich the contextual knowledge about the data, balanced against the time/energy involved in making a good data model.

'''Machine-readable (meta)data'''

(Meta)data in a format that can be automatically read and processed by a computer, such as [http://opendatahandbook.org/glossary/en/terms/csv/ CSV], [http://opendatahandbook.org/glossary/en/terms/json/ JSON], [http://opendatahandbook.org/glossary/en/terms/xml/ XML] etc. For more information: [http://opendatahandbook.org/glossary/en/terms/machine-readable/ Open Data Handbook]

'''Linked Data'''

Linked Data (also known as Linking Data) can be applied to improve the exploitation of the “Web of data.” The expression refers to the publishing of structured data in a way that typed links are created between data from different sources to provide a higher level of usability. By using Linked Data, it is possible to find other, related data. Structured data should meet four requirements to be called
Linked Data:

* URIs should be assigned to all entities of the dataset.
* HTTP URIs are required to ensure that all entities can be referenced and cited by users and user agents.
* Entities should be described using standard formats such as RDF/XML.
* Links should be created to other, related entity URIs.

All data that fulfil these requirements and are released for the public are called Linked Open Data (LOD). http://www.lesliesikos.com/semantic-web-machine-readable-structured-data-with-meaningful-annotations/

Read more under the heading (meta)data include qualified references to other (meta)data in the following resource: https://www.dtls.nl/fair-data/fair-principles-explained/

For more information on Linked Data standards - RDF - https://www.w3.org/RDF/

Other related linked data standards:
* OWL - https://www.w3.org/OWL/
* SKOS - https://www.w3.org/2004/02/skos/
* SPARQL - https://www.w3.org/TR/rdf-sparql-query/

|-
! rowspan="3" | R
|| R
|| About Reusable
|| Reusable data should maintain its initial richness. For example, it should not be abridged for the purpose of explaining the findings in one particular publication. It needs a clear machine-readable [https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse licence] and [https://www.ands.org.au/partners-and-communities/ands-communities/data-provenance-interest-group provenance] information on how the data was formed. It should also use discipline-specific data and [https://www.ands.org.au/working-with-data/metadata metadata] standards to give it rich contextual information that will allow for accurate interpretation and reuse.

|-
|| R1.1
|| About licenses
|| If data is not licensed no-one else can use it. In Australia, no licence is regarded as the same as 'all rights reserved', confining any reuse to very limited circumstances. Applying a Creative Commons licence to your data is a simple way to ensure that your data can be reused. The less restrictive the licence, the more that can be done with the data.
To make it easy for the Web to know when a work is available under a particular license, a “machine readable” version of the license provides a summary of the key freedoms and obligations written into a format that software systems, search engines, and other kinds of technology can understand.
Example from the [https://creativecommons.org/choose/ Creative Commons License Choser]:

What appears in the metadata:

This work is licensed under a [https://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International License].

Code snippet:

<code>
<a rel="license" href="http://creativecommons.org/licenses/by/4.0/">

<img alt="Creative Commons License" style="border-width:0"

src="https://i.creativecommons.org/l/by/4.0/88x31.png"/>

</a> This work is licensed under a

<a rel="license" href="http://creativecommons.org/licenses/by/4.0/">

Creative Commons Attribution 4.0 International License</a>.
<\code>

Read more: https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse

|-
||R1.2
|| About provenance
|| Data provenance is used to document where a piece of data comes from and the process and methodology by which it is produced. It is important to confirm the authenticity of data enabling trust, credibility and reproducibility. This is becoming increasingly important, especially in the eScience community where research is data intensive and often involves complex data transformations and procedures ([https://www.ands.org.au/partners-and-communities/ands-communities/data-provenance-interest-group Read more]). Provenance vocabularies such as [https://www.w3.org/2005/Incubator/prov/XGR-prov-20101214/#Recommendations Provenir Ontology (PROV-O) and others].

|}

== Results for SMILES and ShareWind ==

{| class="mw-collapsible wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Datasets from the SMILES and ShareWind platforms was analysed using the self-assessment tool provided by ARDC.
! rowspan="5" |
| rowspan="5" |
! Database Code
| rowspan="24" style="background: #ABE" |
| ED4
| ED9
|-
! Database Title
|| SMILES
| ShareWind
|-
! Database Link
|| https://www.ecria-smiles.eu/
| https://sharewind.eu/
|-
! Sample Link
|| [https://www.ecria-smiles.eu/data-files/-/document_library/W32cCAfL2jMX/view_file664466?_com_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX_redirect=https%3A%2F%2Fwww.ecria-smiles.eu%3A443%2Fdata-files%2F-%2Fdocument_library%2FW32cCAfL2jMX%2Fview%2F664423%3F_com_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX_redirect%3Dhttps%253A%252F%252Fwww.ecria-smiles.eu%253A443%252Fdata-files%253Fp_p_id%253Dcom_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX%2526p_p_lifecycle%253D0%2526p_p_state%253Dnormal%2526p_p_mode%253Dview here] (link has 585 characters)
| https://sharewind.eu/records/f9d31abbc2824284b8c1d28894d9619a (data found at https://zenodo.org/record/1162738)
|-
! Sample Size
|| 4 KB
| 37 MB
|-
| colspan="6" style="background: #ABE" |
|-
! rowspan="4" | Findable
| F1. (meta)data are assigned a globally unique and persistent identifier
! Does the dataset have any identifiers assigned?
|| Web adress (URL)
| Globally unique, citable and persistant (e.g. DOI, PURL, ARK or Handle)
|-

| F2. data are described with rich metadata (defined by R1 below)
! Is the dataset identifier included in all metadata records/files describing the data?
|| No
| No
|-

| F3. metadata clearly and explicitly include the identifier of the data it describes
! How is the data described with metadata?
|| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
|-

| F4. (meta)data are registered or indexed in a searchable resource
! What type of repository or registry is the metadata record in?
|| Domain-specific repository
| Data is in one place but discoverable through several registries
|-

! rowspan="3" | Accessible

| A1. (meta)data are retrievable by their identifier using a standardized communications protocol
! How accessible is the data?
|| Publicly accessible
| Publicly accessible
|-
| A1.1 the protocol is open, free, and universally implementable; A1.2 the protocol allows for an authentication and authorization procedure, where necessary
! Is the data available online without requiring specialised protocols or tools once access has been approved?
|| File download from online location
| Standard web service API (e.g. OGC)
|-
| A2. metadata are accessible, even when the data are no longer available
! Will the metadata record be available even if the data is no longer available?
|| Unsure
| Yes
|-

! rowspan="3" | Interoperable
| I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
! What (file) format(s) is the data available in?
|| In a structured, open standard, machine-readable format
|| In a structured, open standard, non-machine-readable format
|-
| I2. (meta)data use vocabularies that follow FAIR principles
! What best describes the types of vocabularies/ontologies/tagging schemas used to define the data elements?
|| Standardised vocabularies/ontologies/schema without global identifiers
|| Standardised vocabularies/ontologies/schema without global identifiers
|-
| I3. (meta)data include qualified references to other (meta)data
! How is the metadata linked to other data and metadata (to enhance context and clearly indicate relationships)?
|| There are no links to other metadata
|| Metadata is represented in a machine readable format, e.g. in a linked data format such as Resource Description Framework (RDF)
|-

! rowspan="3" | Reusable
| R1.1. (meta)data are released with a clear and accessible data usage license
! Which of the following best describes the license/usage rights attached to the data?
|| No license
| Standard machine-readable license (e.g. Creative Commons)
|-
| R1.2. (meta)data are associated with detailed provenance
! How much provenance information has been captured to facilitate data reuse?
|| Partially recorded
|| Partially recorded
|-
| R1.3. (meta)data meet domain-relevant community standards
|
|
|
|-
| colspan="6" style="background: #ABE" |
|-
! Total across F.A.I.R
|
! Low: < 50%; Medium: 50-60%; High: > 60%
|| 45 %
| 85 %
|-
| colspan="6" style="background: #ABE" |
|-
! Remarks about database
||
||
||
|| The data is provided by the open-source platform Zenodo, not the project website itself. A very positive aspect is that information on different versions of the data as well as the number of views and downloads is available.
|-
! Identified gaps
||
||
||
* No DOI, only URL
* Extremely long URL link spanning several lines with 585 characters, that can easily become corrupted
* No links to other metadata
* No license information given
* Provenance not fully recorded

||
* Dataset identifiers should be included in the files describing the data
* Provenance details are only partially recorded with a note with contact name for further information
|}

= Metadata assessment =

== Dublin Core Elements ==
{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ The DCMI Metadata Terms lists the current set of the Dublin Core vocabulary. Source: [https://dublincore.org/specifications/dublin-core/dcmi-terms/ Dublin Core Metadata Initiative]
| 1 || abstract || 11 || contributor || 21 || extent || 31 || isReplacedBy || 41 || publisher || 51 || tableOfContents
|-
| 2 || accessRights || 12 || coverage || 22 || format || 32 || isRequiredBy || 42 || references || 52 || temporal
|-
| 3 || accrualMethod || 13 || created || 23 || hasFormat || 33 || issued || 43 || relation || 53 || title
|-
| 4 || accrualPeriodicity || 14 || creator || 24 || hasPart || 34 || isVersionOf || 44 || replaces || 54 || type
|-
| 5 || accrualPolicy || 15 || date || 25 || hasVersion || 35 || language || 45 || requires || 55 || valid
|-
| 6 || alternative || 16 || dateAccepted || 26 || identifier || 36 || license || 46 || rights || ||
|-
| 7 || audience || 17 || dateCopyrighted || 27 || instructionalMethod || 37 || mediator || 47 || rightsHolder || ||
|-
| 8 || available || 18 || dateSubmitted || 28 || isFormatOf || 38 || medium || 48 || source || ||
|-
| 9 || bibliographicCitation || 19 || description || 29 || isPartOf || 39 || modified || 49 || spatial || ||
|-
| 10 || conformsTo || 20 || educationLevel || 30 || isReferencedBy || 40 || provenance || 50 || subject || ||
|}

'''Metadata Category Types according to [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO]'''

There are three main types of metadata:
* Descriptive metadata describes a resource for purposes such as discovery and identification. It can include elements such as title, abstract, author, and keywords.
* Structural metadata indicates how compound objects are put together, for example, how pages are ordered to form chapters.
* Administrative metadata provides information to help manage a resource, such as when and how it was created, file type and other technical information, and who can access it. There are several subsets of administrative data; two that sometimes are listed as separate metadata types are:
** Rights management meta-data, which deals with intellectual property rights,and
** Preservation metadata, which contains information needed to archive and preserve a resource.
''

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Simple Dublin Core Elements. 15 DC elements with their (shortened) official definitions and examples.
! DC element name !! DC definition ([https://www.dublincore.org/specifications/dublin-core/dces/ DCMI]) !! Example (Energy Domain) !! Category (determined with the aid of [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO])
|-
! Title
| A name given to the resource || Outdoor monitoring of a hybrid micro-CPV solar panel with integrated micro-tracking and diffuse capture || Descriptive
|-
! Subject
| The topic of the resource || concentrator photovoltaics; CPV; rooftop photovoltaics; integrated tracking || Descriptive
|-
! Description
| An account of the resource || Dataset from the outdoor characterization of a B Series module from Insolight at the rooftop of the Instituto de Energía Solar - Universidad Politécnica de Madrid. …. || Descriptive
|-
! Creator
| An entity primarily responsible for making the resource || Stephen Askins; Gaël Nardin || Descriptive
|-
! Publisher
| An entity responsible for making the resource available || Stephen Askins || Descriptive
|-
! Contributor
| An entity responsible for making contributions to the resource || Stephen Askins || Descriptive
|-
! Date
| A point or period of time associated with an event in the lifecycle of the resource || 2019-07-23; Date will be associated with the creation or availability of the resource. || Administrative
|-
! Type
| The nature or genre of the resource || info:eu-repo/semantics/other; dataset || Descriptive
|-
! Format
| The file format, physical medium, or dimensions of the resource || image
|| Structural
|-
! Identifier
| An unambiguous reference to the resource within a given context || https://zenodo.org/record/3346823; 10.5281/zenodo.3346823; oai:zenodo.org:3346823 || Descriptive
|-
! Source
| A related resource from which the described resource is derived || RC607.A26W574 1996 [where "RC607.A26W574 1996" is the call number of the print version of the resource, from which the present version was scanned] || Descriptive
|-
! Language
| A language of the resource || eng || Descriptive
|-
! Relation
| A related resource || info:eu-repo/grantAgreement/EC/H2020/787289/ || Descriptive
|-
! Coverage
| The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant || 1995-1996; Madrid, Spain || Descriptive
|-
! Rights
| Information about rights held in and over the resource || info:eu-repo/semantics/openAccess; https://creativecommons.org/licenses/by/4.0/legalcode || Administrative
|}

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Non-DC elements

! Non-DC element name !! Definition !! Example !! Category
|-
! Date of availability
| Since when is the data available
||
|| Impact/Exploitation

|-
! Number of users
| Is there any information on the number of accesses or users?
||
|| Impact/Exploitation

|-
! Number of scientific publications where data is cited/used
| Is there any information on the number of citations/uses?
||
|| Impact/Exploitation

|-
! Curation activities (is the versioning ongoing)
| Update, maintainance
||
|| Impact/Exploitation
|}

Attempting to put a value on Impact/Exploitation:
* '''ENTSO-E''': Found 172 results in Advanced Search (ALL) in Web of Science ([https://apps.webofknowledge.com/ WoSc]) (16/06/2020). Citations of all these search result add up to 714. This however does not mean, that all search results really explicitly used data from ENTSO-E. Some (for sure, for some were found) only mentioned it saying they adoped workflow etc. from the site.
* '''SMARD''': Found 82 results in Advanced Search (ALL=(SMARD)) in Web of Science ([https://apps.webofknowledge.com/ WoSc]) (16/06/2020). However: SMARD is also an acronym for ''Spinal muscular atrophy with respiratory distress (SMARD)''. This somewhat complicates results and needs some further discussion. All 82 search results accumumlated 2359 citations themselves.

Gap analysis

2020-08-28T08:41:33Z

Christopherbs: /* Results for SMILES and ShareWind */

<accesscontrol>Administrators,consortium</accesscontrol>

This is the page for the gap analysis.

= Tacit knowledge "FAIRification and opening of low carbon energy research data" =
 Consortium members can add any time, they feel that something is important to note even though it is not mentioned in a standard deliverable. In other words, this page collects uncodified, tacit knowledge. It will help us later to compile suggestions and lessons learned. This kind of knowledge is collected two-fold:
#Anytime if someone feels that this should be noted. Please write down: Issue, Date, Author (could also be "anonymous"), the issue described in a few words or maximal lines>
#During the final day of workshops

=== Learning process: ===

*People are hesitant to adopt new IT technologies, this is even the case among researchers heavily relying on data, algorithms and collaborative online software (e.g., R, platforms, online conferencing, HPC, ...). The effort to encourage change is not to underestimate. Reasons are several, notably, lack of time and uncertainty about potential benefit as well as overall risk aversion preferences. EERAdata is using the online software "Only Office" to facilitate collaboration (in particular also during the Covid-19 period).
 '''FAIR and open criteria:'''
*Consortium members have a fair understanding of what FAIR/O is, but there is little knowledge and/or technical experience on how to approach the FAIRification and opening. All, however, share the view that we are at a critical point in time, where we need to implement these criteria. 
*To deepen knowledge about FAIR/O criteria, it is useful to test the criteria on a database one is familiar with. For this purpose (and to start brainstorming about the platform), AIT has developed a questionnaire for application in the use cases.
'''Metadata:'''
*A good starting point is to think about metadata and to look into existing metadata concepts in one's field. The first step is to understand that also metadata need to adhere to the FAIR/O principles.
*The next step is to increase knowledge on IT specific terms, i.e. to understand what the difference is between different '''[https://energydata.onlyoffice.eu/Products/Files/doceditor.aspx?fileid=5404340 metadata frameworks] (taxonomy, thesaurus, ontology) as well as classification of metadata (high-level, medium-level, low-level OR administrative, structural and descriptive metadata).  '''WP 2 being in charge of aligning approaches between use cases, participates in all use case kickoffs to bring everybody on the same page. The presentation is linked with "metadata frameworks".
*It is useful to supply consortium members with read aheads and watch aheads on metadata to prepare the '''[https://energydata.onlyoffice.eu/Products/Files/doceditor.aspx?fileid=5296590 first EERAdata workshop]'''. The workshop starts applications and discussions in the use cases break out sessions (and bringing insights back to the plenary), using selected databases.

= FAIR Assessment=

== FAIR Guiding Principles ==
{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ This table shows the FAIR guiding principles described by [https://www.doi.org/10.1038/sdata.2016.18 Wilkinson et al., 2016].

! To be Findable
|
* F1. (meta)data are assigned a globally unique and persistent identifier
* F2. data are described with rich metadata (defined by R1 below)
* F3. metadata clearly and explicitly include the identifier of the data it describes
* F4. (meta)data are registered or indexed in a searchable resource

|-
! To be Accessible
|
* A1. (meta)data are retrievable by their identifier using a standardized communications protocol
** A1.1 the protocol is open, free, and universally implementable
** A1.2 the protocol allows for an authentication and authorization procedure, where necessary
* A2. metadata are accessible, even when the data are no longer available

|-
! To be Interoperable
|
* I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
* I2. (meta)data use vocabularies that follow FAIR principles
* I3. (meta)data include qualified references to other (meta)data

|-
! To be Reusable
|
* R1. meta(data) are richly described with a plurality of accurate and relevant attributes
* R1.1. (meta)data are released with a clear and accessible data usage license
* R1.2. (meta)data are associated with detailed provenance
* R1.3. (meta)data meet domain-relevant community standards

|}

== Identified gaps after Workshop 1 (02/06/2020 – 04/06/2020) ==

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ This table shows a summary of the specific issues that were identified in Workshop 1. The aim was to categorise different issues to get a better understanding of how to tackle these challenges.
! FAIR/O !! Specific FAIR/O !! Gaps !! Gaps for energy domain !! Use case specific gaps !! Consequences for researchers
|-
! F
| F2
|| Metadata scope
|| Different fields require different metadata (potentially very specific)
|| UC3: lack of additional metadata for applications of materials
|| Data are less useful for the specific field
|-
! F
|| F1/F3
|| Identifiers
||
* Missing identifiers on websites
* Identifier not in downloaded data files
||
|| Makes research more difficult.
|-
! I
| I2
|| Taxonomy/ontology/common vocabulary and language issues
||
* Lack of standardisation
* Heterogeneous data makes standardisation hard
* No vocabulary documentation on websites
* Words used for same term in other languages may differ
* Databases in other languages than English
||
|| Research costs more time
|-
! I
| I3
|| References to other (meta)data
||
* No linking to source documents and related publications (for contextual knowledge)
* Possible need for links to other fields
|| UC3: link between microscopic and macroscopic materials (e.g., turbine blades)
||
* Data cannot be connected to the source
* Makes research more difficult.

|-
! R
| R1.1
|| Licensing
||
* No licenses available may mean the data is not reusable for the researcher
* Licenses not clear and accessible
* Obtaining licenses may result in more effort and costs
||
|| Data potentially not reusable

|-
! R
| R1.2
|| Reliability and provenance of data
|| When the data is collected from different and high number of sources, its reliability decreases
||
|| Data potentially not reusable

|-
! O
|
|| Privacy concerns and expected disadvantages
|| Not publishing data due to privacy concerns (sensitive data)
|| UC2: In case of distribution network data this is relevant
|| Data not accessible

|-
! Other
|
|| Conducting FAIR assessments
||
* No qualitative assessment of the data itself (which may be limited)
* Discrepancies between results conducted by humans versus machines
* Sometimes not even DC standards are met
* Metadata is not updated
* Lacking encryption of websites (https)
* Problems assessing whether metadata will be available after data is unavailable
* Interface design/layout may be unclear, incomplete or not intuitive for humans
* Authentication details (user registration login / good or bad, clarify)

||
* UC1 lack of data availability for time-series
||
* Assessment is uncertain (human assessment may need more clarification and understanding)
* Makes research more time-intensive

|}

== FAIR Self-Assessment tool ==

To conduct a human FAIR evaluation for different databases and their respective datasets, different free online tools were considered. Some seemed to be quite useful (e.g. [https://satifyd.dans.knaw.nl/ SATISFYD] ) and some were simple self-evaluation PDFs to be printed and filled out.

We decided to chose the [https://ardc.edu.au/resources/working-with-data/fair-data/fair-self-assessment-tool/ self-assessment tool] provided by the Australian Research Data Commons (ARDC). The decision was based manly upon the fact that the tools questions closely matched the FAIR categories specified by [https://www.doi.org/10.1038/sdata.2016.18 Wilkinson et al., 2016] which would make it easily comparable to the existing preferable FAIR conditions and aid with the repeatability of the results, and the half-automated scoring features that show the extent of the "FAIRness" of the analysed sample as a simple bar chart.

The result of the FAIR assessment is provided as a bar chart where an empty bar chart displays 0 % FAIR compatibility, and a full bar chart represents 100 % FAIR compatibility. The score is therefore not returned as a specific value. During the assessment below, the "Total Across FAIR" in Table ... was estimated according to the level of the bar chart from 0 % to 100 %.
Resulting scores where categorised as follows: Low: < 50%; Medium: 50-60%; High: > 60% .

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ All options for each question of the ARDCs self-assessment tool
!colspan="4"|Findable
!colspan="3"|Accessible
!colspan="3"|Interoperable
!colspan="3"|Reusable
|-
| F1. (meta)data are assigned a globally unique and persistent identifier
|| F2. data are described with rich metadata (defined by R1 below)
|| F3. metadata clearly and explicitly include the identifier of the data it describes
|| F4. (meta)data are registered or indexed in a searchable resource
|| A1. (meta)data are retrievable by their identifier using a standardized communications protocol
|| A1.1 the protocol is open, free, and universally implementable and A1.2 the protocol allows for an authentication and authorization procedure, where necessary
|| A2. metadata are accessible, even when the data are no longer available
|| I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
|| I2. (meta)data use vocabularies that follow FAIR principles
|| I3. (meta)data include qualified references to other (meta)data
|| R1.1. (meta)data are released with a clear and accessible data usage license
|| R1.2. (meta)data are associated with detailed provenance
|| R1.3. (meta)data meet domain-relevant community standards
|-
! Does the dataset have any identifiers assigned?
! Is the dataset identifier included in all metadata records/files describing the data?
! How is the data described with metadata?
! What type of repository or registry is the metadata record in?
! How accessible is the data?
! Is the data available online without requiring specialised protocols or tools once access has been approved?
! Will the metadata record be available even if the data is no longer available?
! What (file) format(s) is the data available in?
! What best describes the types of vocabularies/ontologies/tagging schemas used to define the data elements?
! How is the metadata linked to other data and metadata (to enhance context and clearly indicate relationships)?
! Which of the following best describes the license/usage rights attached to the data?
! How much provenance information has been captured to facilitate data reuse?
!
|-
| Globally unique, citable and persistant (e.g. DOI, PURL, ARK or Handle)
|| Yes
|| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
|| Data is in one place but discoverable through several registries
|| Publicly accessible
|| Standard web service API (e.g. OGC)
|| Yes
|| In a structured, open standard, machine-readable format
|| Standardised open and universal using resolvable global identifiers linking to explainations
|| Meadata is represented in a machine readable format, e.g. in a linked data format such as Resource Description Framework (RDF)
|| Standard machine-readable license (e.g. Creative Commons)
|| Fully recorded in a machine-readable format
||
|-
| Web adress (URL)
|| No
|| Comprehensively, but in a text-based, non-standard format
|| Generalist public repository
|| Fully accessible to person who meet explicitly stated conditions, e.g. ethics approval for sensitive data
|| Non-standard web service (e.g. OpenAPI/Swagger/informal API)
|| No
|| In a structured, open standard, non-machine-readable format
|| Standardised vocabularies/ontologies/schema without global identifiers
|| The metadata record includes URI links to related metadata, data and definitions
|| Standard text based license
|| Fully recorded in a text format
||
|-
| Local identifier
||
|| Brief title and description
|| Domain-specific repository
|| A de-identified / modified subset of the data is publicly accessible
|| File download from online location
|| Unsure
|| Mostly in a proprietary format
|| No standards have been applied in the description of data elements
|| There are no links to other metadata
|| Non-standard machine-readably license (clearly indicating under what conditions the data may be reused)
|| Partially recorded
||
|-
| No identifier
||
|| The data is not described
|| Local institutional repository
|| Embaged access after a specific date
|| By individual arrangement
||
||
|| Data elements not described
||
|| Non-standard text-based license
|| No provenance information is recorded
||
|-
|
||
||
|| The data is not described in any repository
|| Unspecified conditional access e.g. contact the data custodian for access
|| No access to data
||
||
||
||
|| No license
||
||
|-
|
||
||
||
|| Access to metadata only
||
||
||
||
||
||
||
||
|-
|
||
||
||
|| No access to data or metadata
||
||
||
||
||
||
||
||
|}

At this point it is important to note that the assessment of databases and/or datasets using this tool is solely considered a human assessment. This may lead to different evaluations by different users. To counteract this phenomenon, it was suggested that the evaluations should be completed using the 'four-eyes principle' to create more verifiable results. Also, it is planned to additionally evaluate the respective databases using an external tool to perform a machine-assessment.

In addition, not all questions are weighted equally and thus a worse result for one question may not have a large impact whereas only choosing the second-best option in another question will bring down the overall FAIR value considerably.

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ The ARDC tool provides detailed information about the requirements for each of the options of its tool. These descriptions are documented here.
|-
! rowspan="5" | F
|| F
|| About Findable
|| Making data Findable includes assigning a persistent identifier (like a DOI or Handle ), having rich metadata to describe the data and making sure it is findable through disciplinary and generalist discovery portals (local and international).

|-
| F1
|| About identifiers
|| Identifiers are essential for identifying, finding, retrieving, linking and citing datasets. A Web address (URL) can be used to specify the online location of a resource but over time URLs tend to change which leads to broken links to the data. To be useful, identifiers need to be persistent and unique. Digital Object Identifiers (DOIs) and other persistent identifiers (PIDs) provide a permanent citable reference to a particular dataset. The DOI is a permanent fixed reference to the dataset no matter where it is located online and enables citation and citation metrics. Services to create a persistent identifier are often offered by your affiliated institution or the repository you are using to describe your data. Talk to your library service for more information.
|-
| F2
|| About identifiers
|| The identifier (preferably a persistent identifier) needs to be clearly stated in the metadata record describing the data collection, and also in any associated data files or metadata.

|-
|F3
|| About metadata descriptions
|| Comprehensive metadata records will include descriptive content that facilitates discovery, access and reuse of the data being described. While there is no 'one size fits all' list, comprehensive metadata should include:
* a globally unique persistent identifier e.g. a DOI
* a title
* related people, i.e. the data creator or custodian
* how to access the data and file formats
* a description of how the data were created and how to interpret the data subject or keywords
* citation information that clearly indicates how the data should be cited
* a machine-readable data licence
* provenance and contextual information such as:
* links to related publications, projects, services and software
* methodology and processes involved in data production
* spatial and temporal coverage (if relevant)
* object-level data description

Providing metadata in a standard schema allows it to be read and used by machines as well as humans.

|-
| F4
|| About metadata repositories
|| A rich metadata description alone does not ensure a dataset’s ‘findability’ on the internet; the dataset needs to be registered or indexed in a searchable resource, such as a generalist (e.g. Figshare, Dryad, Zenodo domain-specific (e.g. PANGAEA, ADA, ICPSR), or institutional (e.g. ANU Research Repository , Sydney eScholarship Repository, data.gov.au) data repository or registry. Ideally these repositories/registries are indexed by search engines such as Google and/or Google Scholar.

|-
! rowspan = "4" | A
| A
|| About Accessible
|| To make data accessible may include making the data open using a standardised protocol. However the data does not necessarily have to be open. There are sometimes good reasons why data cannot be made open, for example privacy concerns, national security or commercial interests. If it is not open there should be clarity and transparency around the conditions governing access and reuse
|-
| A1
|| About data accessibility
|| Not all data that is discoverable can be freely accessed. Often there are embargoes, access controls, and access permissions associated with data due to a variety of issues such as privacy and commercial interests. Even with all these issues much sensitive data can be shared. Many other issues that could be perceived as blockers to sharing data may be overcome.
|-
| A1.1/A1.2
|| About protocols
|| Ideally users would like to retrieve appropriate internet content directly and unhindered once they have located it. Internet protocols (e.g. http and ftp) define rules and conventions for communication between devices, and tools and services are available to facilitate this process, e.g. APIs.

Protocols:
* HTTP (Hypertext Transfer Protocol) is the set of rules for transferring files (text, graphic images, sound, video, and other multimedia files) on the World Wide Web. As soon as a Web user opens their Web browser, the user is indirectly making use of HTTP.
* FTP (File Transfer Protocol) is a standard Internet protocol for transmitting files between computers on the Internet over TCP/IP connections. Read more: Web protocols
* API (Application Programming Interface): When information is made available in any machine readable format, it becomes possible to make that information directly available to programs that request that information over the web. An API is the way this information is made directly available to other machines.

|-
| A2
|| About metadata availability
|| Effort is often required to maintain data resources online which can often lead to it being neglected especially over long periods of time. This often leads to broken links between the metadata and the data. Having at least a description of the data in the form of a metadata record means there is a record of the data's existence allowing the possibility of someone tracking it down.

|-
! rowspan="4" | I
|| I
|| About Interoperability
|| To be interoperable (i.e. data that is interpretable by a computer, so that they can be automatically combined with other data) the data will need to use community agreed formats, language and vocabularies. The metadata will also need to use a community agreed standards and vocabularies, and contain links to related information using identifiers.
|-
|| I1
|| About file formats
|| When selecting file formats for archiving, the formats should ideally be:
* Non-proprietary
* Unencrypted
* Uncompressed
* In common usage by the research community
* Adherent to an open, documented standard, such as described by the State of California (see AB 1668, 2007)
* Interoperable among diverse platforms and applications
* Fully published and available royalty-free
* Fully and independently implementable by multiple software providers on multiple platforms without any intellectual property restrictions for necessary technology
* Developed and maintained by an open standards organization with a well-defined inclusive process for evolution of the standard.

From: Stanford University Library - Best practices for file formats
File format examples:
* Structured, open standard, machine-readable format e.g. (text) PDF/A, HTML, Plain text, (images) TIFF, JPEG 2000, GIF, (audio) MP3, AIFF, WAVE, (video) MOV, MPEG, AVI, (Tabular data) CSV
* structured, open standard, non-machine-readable format, e.g. PDF, HTML, JPG
* Proprietary format, e.g. doc (Word), .xls (Excel), .ppt (PowerPoint), .sav

Read more:
* ANDS
* European Data Portal

|-
|| I2
|| About schema standards
|| Schema standards are schemas that have gone through a formal validation process by a standards organisation, such as the International Standards Organisation (ISO) or an equivalent body such as the Dublin Core Metadata Initiative (DCMI); or, commonly used and consistently applied metadata schemas that are well documented, endorsed, and maintained can also become ‘de-facto standards’, e.g. RIF-CS for describing data collections and services used in Research Data Australia.

Read more: [https://www.ands.org.au/guides/metadata-working Metadata schema]

Standardised open and universal schemas, e.g, [https://www.dcc.ac.uk/resources/metadata-standards/datacite-metadata-schema DataCite Metadata Schema], [https://www.dcc.ac.uk/resources/metadata-standards/prov PROV], [https://www.dcc.ac.uk/resources/metadata-standards/dublin-core Dublin Core]

Domain-specific standards, e.g. [http://human-phenotype-ontology.github.io/ HPO - Human Phenotype Ontology], [https://www.nlm.nih.gov/mesh/ MeSH - Medical Subject Headings] , [https://mcp-profile-docs.readthedocs.io/en/stable/ Marine Community Profile], [https://ddialliance.org/ DDI - Data Documentation Initiative].

For more examples of standards: [http://rd-alliance.github.io/metadata-directory/ Here].

Resolvable global identifiers - An identifier is any label used to name something uniquely (whether online or offline). URLs are an example of an identifier. So are serial numbers, and personal names. A persistent identifier is guaranteed to be managed and kept up to date over a defined time period.

Read more: [https://www.ands.org.au/guides/persistent-identifiers-awareness Persistent Identifiers]

|-
||I3
|| About metadata links
|| The goal is to create as many meaningful linkages as possible between (meta)data resources to enrich the contextual knowledge about the data, balanced against the time/energy involved in making a good data model.

'''Machine-readable (meta)data'''

(Meta)data in a format that can be automatically read and processed by a computer, such as [http://opendatahandbook.org/glossary/en/terms/csv/ CSV], [http://opendatahandbook.org/glossary/en/terms/json/ JSON], [http://opendatahandbook.org/glossary/en/terms/xml/ XML] etc. For more information: [http://opendatahandbook.org/glossary/en/terms/machine-readable/ Open Data Handbook]

'''Linked Data'''

Linked Data (also known as Linking Data) can be applied to improve the exploitation of the “Web of data.” The expression refers to the publishing of structured data in a way that typed links are created between data from different sources to provide a higher level of usability. By using Linked Data, it is possible to find other, related data. Structured data should meet four requirements to be called
Linked Data:

* URIs should be assigned to all entities of the dataset.
* HTTP URIs are required to ensure that all entities can be referenced and cited by users and user agents.
* Entities should be described using standard formats such as RDF/XML.
* Links should be created to other, related entity URIs.

All data that fulfil these requirements and are released for the public are called Linked Open Data (LOD). http://www.lesliesikos.com/semantic-web-machine-readable-structured-data-with-meaningful-annotations/

Read more under the heading (meta)data include qualified references to other (meta)data in the following resource: https://www.dtls.nl/fair-data/fair-principles-explained/

For more information on Linked Data standards - RDF - https://www.w3.org/RDF/

Other related linked data standards:
* OWL - https://www.w3.org/OWL/
* SKOS - https://www.w3.org/2004/02/skos/
* SPARQL - https://www.w3.org/TR/rdf-sparql-query/

|-
! rowspan="3" | R
|| R
|| About Reusable
|| Reusable data should maintain its initial richness. For example, it should not be abridged for the purpose of explaining the findings in one particular publication. It needs a clear machine-readable [https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse licence] and [https://www.ands.org.au/partners-and-communities/ands-communities/data-provenance-interest-group provenance] information on how the data was formed. It should also use discipline-specific data and [https://www.ands.org.au/working-with-data/metadata metadata] standards to give it rich contextual information that will allow for accurate interpretation and reuse.

|-
|| R1.1
|| About licenses
|| If data is not licensed no-one else can use it. In Australia, no licence is regarded as the same as 'all rights reserved', confining any reuse to very limited circumstances. Applying a Creative Commons licence to your data is a simple way to ensure that your data can be reused. The less restrictive the licence, the more that can be done with the data.
To make it easy for the Web to know when a work is available under a particular license, a “machine readable” version of the license provides a summary of the key freedoms and obligations written into a format that software systems, search engines, and other kinds of technology can understand.
Example from the [https://creativecommons.org/choose/ Creative Commons License Choser]:

What appears in the metadata:

This work is licensed under a [https://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International License].

Code snippet:

<code>
<a rel="license" href="http://creativecommons.org/licenses/by/4.0/">

<img alt="Creative Commons License" style="border-width:0"

src="https://i.creativecommons.org/l/by/4.0/88x31.png"/>

</a> This work is licensed under a

<a rel="license" href="http://creativecommons.org/licenses/by/4.0/">

Creative Commons Attribution 4.0 International License</a>.
<\code>

Read more: https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse

|-
||R1.2
|| About provenance
|| Data provenance is used to document where a piece of data comes from and the process and methodology by which it is produced. It is important to confirm the authenticity of data enabling trust, credibility and reproducibility. This is becoming increasingly important, especially in the eScience community where research is data intensive and often involves complex data transformations and procedures ([https://www.ands.org.au/partners-and-communities/ands-communities/data-provenance-interest-group Read more]). Provenance vocabularies such as [https://www.w3.org/2005/Incubator/prov/XGR-prov-20101214/#Recommendations Provenir Ontology (PROV-O) and others].

|}

== Results for SMILES and ShareWind ==

{| class="mw-collapsible wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Datasets from the SMILES and ShareWind platforms was analysed using the self-assessment tool provided by ARDC.
! rowspan="5" |
| rowspan="5" |
! Database Code
| rowspan="24" style="background: #ABE" |
| ED4
| ED9
|-
! Database Title
|| SMILES
| ShareWind
|-
! Database Link
|| https://www.ecria-smiles.eu/
| https://sharewind.eu/
|-
! Sample Link
|| [https://www.ecria-smiles.eu/data-files/-/document_library/W32cCAfL2jMX/view_file664466?_com_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX_redirect=https%3A%2F%2Fwww.ecria-smiles.eu%3A443%2Fdata-files%2F-%2Fdocument_library%2FW32cCAfL2jMX%2Fview%2F664423%3F_com_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX_redirect%3Dhttps%253A%252F%252Fwww.ecria-smiles.eu%253A443%252Fdata-files%253Fp_p_id%253Dcom_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX%2526p_p_lifecycle%253D0%2526p_p_state%253Dnormal%2526p_p_mode%253Dview here] (link has 585 characters)
| https://sharewind.eu/records/f9d31abbc2824284b8c1d28894d9619a (data found at https://zenodo.org/record/1162738)
|-
! Sample Size
|| 4 KB
| 37 MB
|-
| colspan="6" style="background: #ABE" |
|-
! rowspan="4" | Findable
| F1. (meta)data are assigned a globally unique and persistent identifier
! Does the dataset have any identifiers assigned?
|| Web adress (URL)
| Globally unique, citable and persistant (e.g. DOI, PURL, ARK or Handle)
|-

| F2. data are described with rich metadata (defined by R1 below)
! Is the dataset identifier included in all metadata records/files describing the data?
|| No
| No
|-

| F3. metadata clearly and explicitly include the identifier of the data it describes
! How is the data described with metadata?
|| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
|-

| F4. (meta)data are registered or indexed in a searchable resource
! What type of repository or registry is the metadata record in?
|| Domain-specific repository
| Data is in one place but discoverable through several registries
|-

! rowspan="3" | Accessible

| A1. (meta)data are retrievable by their identifier using a standardized communications protocol
! How accessible is the data?
|| Publicly accessible
| Publicly accessible
|-
| A1.1 the protocol is open, free, and universally implementable; A1.2 the protocol allows for an authentication and authorization procedure, where necessary
! Is the data available online without requiring specialised protocols or tools once access has been approved?
|| File download from online location
| Standard web service API (e.g. OGC)
|-
| A2. metadata are accessible, even when the data are no longer available
! Will the metadata record be available even if the data is no longer available?
|| Unsure
| Yes
|-

! rowspan="3" | Interoperable
| I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
! What (file) format(s) is the data available in?
|| In a structured, open standard, machine-readable format
|| In a structured, open standard, non-machine-readable format
|-
| I2. (meta)data use vocabularies that follow FAIR principles
! What best describes the types of vocabularies/ontologies/tagging schemas used to define the data elements?
|| Standardised vocabularies/ontologies/schema without global identifiers
|| Standardised vocabularies/ontologies/schema without global identifiers
|-
| I3. (meta)data include qualified references to other (meta)data
! How is the metadata linked to other data and metadata (to enhance context and clearly indicate relationships)?
|| There are no links to other metadata
|| Metadata is represented in a machine readable format, e.g. in a linked data format such as Resource Description Framework (RDF)
|-

! rowspan="3" | Reusable
| R1.1. (meta)data are released with a clear and accessible data usage license
! Which of the following best describes the license/usage rights attached to the data?
|| No license
| Standard machine-readable license (e.g. Creative Commons)
|-
| R1.2. (meta)data are associated with detailed provenance
! How much provenance information has been captured to facilitate data reuse?
|| Partially recorded
|| Partially recorded
|-
| R1.3. (meta)data meet domain-relevant community standards
|
|
|
|-
| colspan="6" style="background: #ABE" |
|-
! Total across F.A.I.R
|
! Low: < 50%; Medium: 50-60%; High: > 60%
|| 45 %
| 85 %
|-
| colspan="6" style="background: #ABE" |
|-
! Remarks about database
||
||
||
|| The data is provided by the open-source platform Zenodo, not the project website itself. A very positive aspect is that information on different versions of the data as well as the number of views and downloads is available.
|-
! Identified gaps
||
||
||
* No DOI, only URL
* Extremely long URL link spanning several lines with 585 characters, that can easily become corrupted
* No links to other metadata
* No license information given
* Provenance not fully recorded

||
* Dataset identifiers should be included in the files describing the data
* Provenance details are only partially recorded with a note with contact name for further information
|}

= Dublin Core Evaluation =

== Dublin Core Elements ==
{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ The DCMI Metadata Terms lists the current set of the Dublin Core vocabulary. Source: [https://dublincore.org/specifications/dublin-core/dcmi-terms/ Dublin Core Metadata Initiative]
| 1 || abstract || 11 || contributor || 21 || extent || 31 || isReplacedBy || 41 || publisher || 51 || tableOfContents
|-
| 2 || accessRights || 12 || coverage || 22 || format || 32 || isRequiredBy || 42 || references || 52 || temporal
|-
| 3 || accrualMethod || 13 || created || 23 || hasFormat || 33 || issued || 43 || relation || 53 || title
|-
| 4 || accrualPeriodicity || 14 || creator || 24 || hasPart || 34 || isVersionOf || 44 || replaces || 54 || type
|-
| 5 || accrualPolicy || 15 || date || 25 || hasVersion || 35 || language || 45 || requires || 55 || valid
|-
| 6 || alternative || 16 || dateAccepted || 26 || identifier || 36 || license || 46 || rights || ||
|-
| 7 || audience || 17 || dateCopyrighted || 27 || instructionalMethod || 37 || mediator || 47 || rightsHolder || ||
|-
| 8 || available || 18 || dateSubmitted || 28 || isFormatOf || 38 || medium || 48 || source || ||
|-
| 9 || bibliographicCitation || 19 || description || 29 || isPartOf || 39 || modified || 49 || spatial || ||
|-
| 10 || conformsTo || 20 || educationLevel || 30 || isReferencedBy || 40 || provenance || 50 || subject || ||
|}

'''Metadata Category Types according to [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO]'''

There are three main types of metadata:
* Descriptive metadata describes a resource for purposes such as discovery and identification. It can include elements such as title, abstract, author, and keywords.
* Structural metadata indicates how compound objects are put together, for example, how pages are ordered to form chapters.
* Administrative metadata provides information to help manage a resource, such as when and how it was created, file type and other technical information, and who can access it. There are several subsets of administrative data; two that sometimes are listed as separate metadata types are:
** Rights management meta-data, which deals with intellectual property rights,and
** Preservation metadata, which contains information needed to archive and preserve a resource.
''

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Simple Dublin Core Elements. 15 DC elements with their (shortened) official definitions and examples.
! DC element name !! DC definition ([https://www.dublincore.org/specifications/dublin-core/dces/ DCMI]) !! Example (Energy Domain) !! Category (determined with the aid of [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO])
|-
! Title
| A name given to the resource || Outdoor monitoring of a hybrid micro-CPV solar panel with integrated micro-tracking and diffuse capture || Descriptive
|-
! Subject
| The topic of the resource || concentrator photovoltaics; CPV; rooftop photovoltaics; integrated tracking || Descriptive
|-
! Description
| An account of the resource || Dataset from the outdoor characterization of a B Series module from Insolight at the rooftop of the Instituto de Energía Solar - Universidad Politécnica de Madrid. …. || Descriptive
|-
! Creator
| An entity primarily responsible for making the resource || Stephen Askins; Gaël Nardin || Descriptive
|-
! Publisher
| An entity responsible for making the resource available || Stephen Askins || Descriptive
|-
! Contributor
| An entity responsible for making contributions to the resource || Stephen Askins || Descriptive
|-
! Date
| A point or period of time associated with an event in the lifecycle of the resource || 2019-07-23; Date will be associated with the creation or availability of the resource. || Administrative
|-
! Type
| The nature or genre of the resource || info:eu-repo/semantics/other; dataset || Descriptive
|-
! Format
| The file format, physical medium, or dimensions of the resource || image
|| Structural
|-
! Identifier
| An unambiguous reference to the resource within a given context || https://zenodo.org/record/3346823; 10.5281/zenodo.3346823; oai:zenodo.org:3346823 || Descriptive
|-
! Source
| A related resource from which the described resource is derived || RC607.A26W574 1996 [where "RC607.A26W574 1996" is the call number of the print version of the resource, from which the present version was scanned] || Descriptive
|-
! Language
| A language of the resource || eng || Descriptive
|-
! Relation
| A related resource || info:eu-repo/grantAgreement/EC/H2020/787289/ || Descriptive
|-
! Coverage
| The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant || 1995-1996; Madrid, Spain || Descriptive
|-
! Rights
| Information about rights held in and over the resource || info:eu-repo/semantics/openAccess; https://creativecommons.org/licenses/by/4.0/legalcode || Administrative
|}

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Non-DC elements

! Non-DC element name !! Definition !! Example !! Category
|-
! Date of availability
| Since when is the data available
||
|| Impact/Exploitation

|-
! Number of users
| Is there any information on the number of accesses or users?
||
|| Impact/Exploitation

|-
! Number of scientific publications where data is cited/used
| Is there any information on the number of citations/uses?
||
|| Impact/Exploitation

|-
! Curation activities (is the versioning ongoing)
| Update, maintainance
||
|| Impact/Exploitation
|}

Attempting to put a value on Impact/Exploitation:
* '''ENTSO-E''': Found 172 results in Advanced Search (ALL) in Web of Science ([https://apps.webofknowledge.com/ WoSc]) (16/06/2020). Citations of all these search result add up to 714. This however does not mean, that all search results really explicitly used data from ENTSO-E. Some (for sure, for some were found) only mentioned it saying they adoped workflow etc. from the site.
* '''SMARD''': Found 82 results in Advanced Search (ALL=(SMARD)) in Web of Science ([https://apps.webofknowledge.com/ WoSc]) (16/06/2020). However: SMARD is also an acronym for ''Spinal muscular atrophy with respiratory distress (SMARD)''. This somewhat complicates results and needs some further discussion. All 82 search results accumumlated 2359 citations themselves.

Gap analysis

2020-08-28T08:41:11Z

Christopherbs: /* FAIR Self-Assessment tool */

<accesscontrol>Administrators,consortium</accesscontrol>

This is the page for the gap analysis.

= Tacit knowledge "FAIRification and opening of low carbon energy research data" =
 Consortium members can add any time, they feel that something is important to note even though it is not mentioned in a standard deliverable. In other words, this page collects uncodified, tacit knowledge. It will help us later to compile suggestions and lessons learned. This kind of knowledge is collected two-fold:
#Anytime if someone feels that this should be noted. Please write down: Issue, Date, Author (could also be "anonymous"), the issue described in a few words or maximal lines>
#During the final day of workshops

=== Learning process: ===

*People are hesitant to adopt new IT technologies, this is even the case among researchers heavily relying on data, algorithms and collaborative online software (e.g., R, platforms, online conferencing, HPC, ...). The effort to encourage change is not to underestimate. Reasons are several, notably, lack of time and uncertainty about potential benefit as well as overall risk aversion preferences. EERAdata is using the online software "Only Office" to facilitate collaboration (in particular also during the Covid-19 period).
 '''FAIR and open criteria:'''
*Consortium members have a fair understanding of what FAIR/O is, but there is little knowledge and/or technical experience on how to approach the FAIRification and opening. All, however, share the view that we are at a critical point in time, where we need to implement these criteria. 
*To deepen knowledge about FAIR/O criteria, it is useful to test the criteria on a database one is familiar with. For this purpose (and to start brainstorming about the platform), AIT has developed a questionnaire for application in the use cases.
'''Metadata:'''
*A good starting point is to think about metadata and to look into existing metadata concepts in one's field. The first step is to understand that also metadata need to adhere to the FAIR/O principles.
*The next step is to increase knowledge on IT specific terms, i.e. to understand what the difference is between different '''[https://energydata.onlyoffice.eu/Products/Files/doceditor.aspx?fileid=5404340 metadata frameworks] (taxonomy, thesaurus, ontology) as well as classification of metadata (high-level, medium-level, low-level OR administrative, structural and descriptive metadata).  '''WP 2 being in charge of aligning approaches between use cases, participates in all use case kickoffs to bring everybody on the same page. The presentation is linked with "metadata frameworks".
*It is useful to supply consortium members with read aheads and watch aheads on metadata to prepare the '''[https://energydata.onlyoffice.eu/Products/Files/doceditor.aspx?fileid=5296590 first EERAdata workshop]'''. The workshop starts applications and discussions in the use cases break out sessions (and bringing insights back to the plenary), using selected databases.

= FAIR Assessment=

== FAIR Guiding Principles ==
{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ This table shows the FAIR guiding principles described by [https://www.doi.org/10.1038/sdata.2016.18 Wilkinson et al., 2016].

! To be Findable
|
* F1. (meta)data are assigned a globally unique and persistent identifier
* F2. data are described with rich metadata (defined by R1 below)
* F3. metadata clearly and explicitly include the identifier of the data it describes
* F4. (meta)data are registered or indexed in a searchable resource

|-
! To be Accessible
|
* A1. (meta)data are retrievable by their identifier using a standardized communications protocol
** A1.1 the protocol is open, free, and universally implementable
** A1.2 the protocol allows for an authentication and authorization procedure, where necessary
* A2. metadata are accessible, even when the data are no longer available

|-
! To be Interoperable
|
* I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
* I2. (meta)data use vocabularies that follow FAIR principles
* I3. (meta)data include qualified references to other (meta)data

|-
! To be Reusable
|
* R1. meta(data) are richly described with a plurality of accurate and relevant attributes
* R1.1. (meta)data are released with a clear and accessible data usage license
* R1.2. (meta)data are associated with detailed provenance
* R1.3. (meta)data meet domain-relevant community standards

|}

== Identified gaps after Workshop 1 (02/06/2020 – 04/06/2020) ==

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ This table shows a summary of the specific issues that were identified in Workshop 1. The aim was to categorise different issues to get a better understanding of how to tackle these challenges.
! FAIR/O !! Specific FAIR/O !! Gaps !! Gaps for energy domain !! Use case specific gaps !! Consequences for researchers
|-
! F
| F2
|| Metadata scope
|| Different fields require different metadata (potentially very specific)
|| UC3: lack of additional metadata for applications of materials
|| Data are less useful for the specific field
|-
! F
|| F1/F3
|| Identifiers
||
* Missing identifiers on websites
* Identifier not in downloaded data files
||
|| Makes research more difficult.
|-
! I
| I2
|| Taxonomy/ontology/common vocabulary and language issues
||
* Lack of standardisation
* Heterogeneous data makes standardisation hard
* No vocabulary documentation on websites
* Words used for same term in other languages may differ
* Databases in other languages than English
||
|| Research costs more time
|-
! I
| I3
|| References to other (meta)data
||
* No linking to source documents and related publications (for contextual knowledge)
* Possible need for links to other fields
|| UC3: link between microscopic and macroscopic materials (e.g., turbine blades)
||
* Data cannot be connected to the source
* Makes research more difficult.

|-
! R
| R1.1
|| Licensing
||
* No licenses available may mean the data is not reusable for the researcher
* Licenses not clear and accessible
* Obtaining licenses may result in more effort and costs
||
|| Data potentially not reusable

|-
! R
| R1.2
|| Reliability and provenance of data
|| When the data is collected from different and high number of sources, its reliability decreases
||
|| Data potentially not reusable

|-
! O
|
|| Privacy concerns and expected disadvantages
|| Not publishing data due to privacy concerns (sensitive data)
|| UC2: In case of distribution network data this is relevant
|| Data not accessible

|-
! Other
|
|| Conducting FAIR assessments
||
* No qualitative assessment of the data itself (which may be limited)
* Discrepancies between results conducted by humans versus machines
* Sometimes not even DC standards are met
* Metadata is not updated
* Lacking encryption of websites (https)
* Problems assessing whether metadata will be available after data is unavailable
* Interface design/layout may be unclear, incomplete or not intuitive for humans
* Authentication details (user registration login / good or bad, clarify)

||
* UC1 lack of data availability for time-series
||
* Assessment is uncertain (human assessment may need more clarification and understanding)
* Makes research more time-intensive

|}

== FAIR Self-Assessment tool ==

To conduct a human FAIR evaluation for different databases and their respective datasets, different free online tools were considered. Some seemed to be quite useful (e.g. [https://satifyd.dans.knaw.nl/ SATISFYD] ) and some were simple self-evaluation PDFs to be printed and filled out.

We decided to chose the [https://ardc.edu.au/resources/working-with-data/fair-data/fair-self-assessment-tool/ self-assessment tool] provided by the Australian Research Data Commons (ARDC). The decision was based manly upon the fact that the tools questions closely matched the FAIR categories specified by [https://www.doi.org/10.1038/sdata.2016.18 Wilkinson et al., 2016] which would make it easily comparable to the existing preferable FAIR conditions and aid with the repeatability of the results, and the half-automated scoring features that show the extent of the "FAIRness" of the analysed sample as a simple bar chart.

The result of the FAIR assessment is provided as a bar chart where an empty bar chart displays 0 % FAIR compatibility, and a full bar chart represents 100 % FAIR compatibility. The score is therefore not returned as a specific value. During the assessment below, the "Total Across FAIR" in Table ... was estimated according to the level of the bar chart from 0 % to 100 %.
Resulting scores where categorised as follows: Low: < 50%; Medium: 50-60%; High: > 60% .

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ All options for each question of the ARDCs self-assessment tool
!colspan="4"|Findable
!colspan="3"|Accessible
!colspan="3"|Interoperable
!colspan="3"|Reusable
|-
| F1. (meta)data are assigned a globally unique and persistent identifier
|| F2. data are described with rich metadata (defined by R1 below)
|| F3. metadata clearly and explicitly include the identifier of the data it describes
|| F4. (meta)data are registered or indexed in a searchable resource
|| A1. (meta)data are retrievable by their identifier using a standardized communications protocol
|| A1.1 the protocol is open, free, and universally implementable and A1.2 the protocol allows for an authentication and authorization procedure, where necessary
|| A2. metadata are accessible, even when the data are no longer available
|| I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
|| I2. (meta)data use vocabularies that follow FAIR principles
|| I3. (meta)data include qualified references to other (meta)data
|| R1.1. (meta)data are released with a clear and accessible data usage license
|| R1.2. (meta)data are associated with detailed provenance
|| R1.3. (meta)data meet domain-relevant community standards
|-
! Does the dataset have any identifiers assigned?
! Is the dataset identifier included in all metadata records/files describing the data?
! How is the data described with metadata?
! What type of repository or registry is the metadata record in?
! How accessible is the data?
! Is the data available online without requiring specialised protocols or tools once access has been approved?
! Will the metadata record be available even if the data is no longer available?
! What (file) format(s) is the data available in?
! What best describes the types of vocabularies/ontologies/tagging schemas used to define the data elements?
! How is the metadata linked to other data and metadata (to enhance context and clearly indicate relationships)?
! Which of the following best describes the license/usage rights attached to the data?
! How much provenance information has been captured to facilitate data reuse?
!
|-
| Globally unique, citable and persistant (e.g. DOI, PURL, ARK or Handle)
|| Yes
|| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
|| Data is in one place but discoverable through several registries
|| Publicly accessible
|| Standard web service API (e.g. OGC)
|| Yes
|| In a structured, open standard, machine-readable format
|| Standardised open and universal using resolvable global identifiers linking to explainations
|| Meadata is represented in a machine readable format, e.g. in a linked data format such as Resource Description Framework (RDF)
|| Standard machine-readable license (e.g. Creative Commons)
|| Fully recorded in a machine-readable format
||
|-
| Web adress (URL)
|| No
|| Comprehensively, but in a text-based, non-standard format
|| Generalist public repository
|| Fully accessible to person who meet explicitly stated conditions, e.g. ethics approval for sensitive data
|| Non-standard web service (e.g. OpenAPI/Swagger/informal API)
|| No
|| In a structured, open standard, non-machine-readable format
|| Standardised vocabularies/ontologies/schema without global identifiers
|| The metadata record includes URI links to related metadata, data and definitions
|| Standard text based license
|| Fully recorded in a text format
||
|-
| Local identifier
||
|| Brief title and description
|| Domain-specific repository
|| A de-identified / modified subset of the data is publicly accessible
|| File download from online location
|| Unsure
|| Mostly in a proprietary format
|| No standards have been applied in the description of data elements
|| There are no links to other metadata
|| Non-standard machine-readably license (clearly indicating under what conditions the data may be reused)
|| Partially recorded
||
|-
| No identifier
||
|| The data is not described
|| Local institutional repository
|| Embaged access after a specific date
|| By individual arrangement
||
||
|| Data elements not described
||
|| Non-standard text-based license
|| No provenance information is recorded
||
|-
|
||
||
|| The data is not described in any repository
|| Unspecified conditional access e.g. contact the data custodian for access
|| No access to data
||
||
||
||
|| No license
||
||
|-
|
||
||
||
|| Access to metadata only
||
||
||
||
||
||
||
||
|-
|
||
||
||
|| No access to data or metadata
||
||
||
||
||
||
||
||
|}

At this point it is important to note that the assessment of databases and/or datasets using this tool is solely considered a human assessment. This may lead to different evaluations by different users. To counteract this phenomenon, it was suggested that the evaluations should be completed using the 'four-eyes principle' to create more verifiable results. Also, it is planned to additionally evaluate the respective databases using an external tool to perform a machine-assessment.

In addition, not all questions are weighted equally and thus a worse result for one question may not have a large impact whereas only choosing the second-best option in another question will bring down the overall FAIR value considerably.

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ The ARDC tool provides detailed information about the requirements for each of the options of its tool. These descriptions are documented here.
|-
! rowspan="5" | F
|| F
|| About Findable
|| Making data Findable includes assigning a persistent identifier (like a DOI or Handle ), having rich metadata to describe the data and making sure it is findable through disciplinary and generalist discovery portals (local and international).

|-
| F1
|| About identifiers
|| Identifiers are essential for identifying, finding, retrieving, linking and citing datasets. A Web address (URL) can be used to specify the online location of a resource but over time URLs tend to change which leads to broken links to the data. To be useful, identifiers need to be persistent and unique. Digital Object Identifiers (DOIs) and other persistent identifiers (PIDs) provide a permanent citable reference to a particular dataset. The DOI is a permanent fixed reference to the dataset no matter where it is located online and enables citation and citation metrics. Services to create a persistent identifier are often offered by your affiliated institution or the repository you are using to describe your data. Talk to your library service for more information.
|-
| F2
|| About identifiers
|| The identifier (preferably a persistent identifier) needs to be clearly stated in the metadata record describing the data collection, and also in any associated data files or metadata.

|-
|F3
|| About metadata descriptions
|| Comprehensive metadata records will include descriptive content that facilitates discovery, access and reuse of the data being described. While there is no 'one size fits all' list, comprehensive metadata should include:
* a globally unique persistent identifier e.g. a DOI
* a title
* related people, i.e. the data creator or custodian
* how to access the data and file formats
* a description of how the data were created and how to interpret the data subject or keywords
* citation information that clearly indicates how the data should be cited
* a machine-readable data licence
* provenance and contextual information such as:
* links to related publications, projects, services and software
* methodology and processes involved in data production
* spatial and temporal coverage (if relevant)
* object-level data description

Providing metadata in a standard schema allows it to be read and used by machines as well as humans.

|-
| F4
|| About metadata repositories
|| A rich metadata description alone does not ensure a dataset’s ‘findability’ on the internet; the dataset needs to be registered or indexed in a searchable resource, such as a generalist (e.g. Figshare, Dryad, Zenodo domain-specific (e.g. PANGAEA, ADA, ICPSR), or institutional (e.g. ANU Research Repository , Sydney eScholarship Repository, data.gov.au) data repository or registry. Ideally these repositories/registries are indexed by search engines such as Google and/or Google Scholar.

|-
! rowspan = "4" | A
| A
|| About Accessible
|| To make data accessible may include making the data open using a standardised protocol. However the data does not necessarily have to be open. There are sometimes good reasons why data cannot be made open, for example privacy concerns, national security or commercial interests. If it is not open there should be clarity and transparency around the conditions governing access and reuse
|-
| A1
|| About data accessibility
|| Not all data that is discoverable can be freely accessed. Often there are embargoes, access controls, and access permissions associated with data due to a variety of issues such as privacy and commercial interests. Even with all these issues much sensitive data can be shared. Many other issues that could be perceived as blockers to sharing data may be overcome.
|-
| A1.1/A1.2
|| About protocols
|| Ideally users would like to retrieve appropriate internet content directly and unhindered once they have located it. Internet protocols (e.g. http and ftp) define rules and conventions for communication between devices, and tools and services are available to facilitate this process, e.g. APIs.

Protocols:
* HTTP (Hypertext Transfer Protocol) is the set of rules for transferring files (text, graphic images, sound, video, and other multimedia files) on the World Wide Web. As soon as a Web user opens their Web browser, the user is indirectly making use of HTTP.
* FTP (File Transfer Protocol) is a standard Internet protocol for transmitting files between computers on the Internet over TCP/IP connections. Read more: Web protocols
* API (Application Programming Interface): When information is made available in any machine readable format, it becomes possible to make that information directly available to programs that request that information over the web. An API is the way this information is made directly available to other machines.

|-
| A2
|| About metadata availability
|| Effort is often required to maintain data resources online which can often lead to it being neglected especially over long periods of time. This often leads to broken links between the metadata and the data. Having at least a description of the data in the form of a metadata record means there is a record of the data's existence allowing the possibility of someone tracking it down.

|-
! rowspan="4" | I
|| I
|| About Interoperability
|| To be interoperable (i.e. data that is interpretable by a computer, so that they can be automatically combined with other data) the data will need to use community agreed formats, language and vocabularies. The metadata will also need to use a community agreed standards and vocabularies, and contain links to related information using identifiers.
|-
|| I1
|| About file formats
|| When selecting file formats for archiving, the formats should ideally be:
* Non-proprietary
* Unencrypted
* Uncompressed
* In common usage by the research community
* Adherent to an open, documented standard, such as described by the State of California (see AB 1668, 2007)
* Interoperable among diverse platforms and applications
* Fully published and available royalty-free
* Fully and independently implementable by multiple software providers on multiple platforms without any intellectual property restrictions for necessary technology
* Developed and maintained by an open standards organization with a well-defined inclusive process for evolution of the standard.

From: Stanford University Library - Best practices for file formats
File format examples:
* Structured, open standard, machine-readable format e.g. (text) PDF/A, HTML, Plain text, (images) TIFF, JPEG 2000, GIF, (audio) MP3, AIFF, WAVE, (video) MOV, MPEG, AVI, (Tabular data) CSV
* structured, open standard, non-machine-readable format, e.g. PDF, HTML, JPG
* Proprietary format, e.g. doc (Word), .xls (Excel), .ppt (PowerPoint), .sav

Read more:
* ANDS
* European Data Portal

|-
|| I2
|| About schema standards
|| Schema standards are schemas that have gone through a formal validation process by a standards organisation, such as the International Standards Organisation (ISO) or an equivalent body such as the Dublin Core Metadata Initiative (DCMI); or, commonly used and consistently applied metadata schemas that are well documented, endorsed, and maintained can also become ‘de-facto standards’, e.g. RIF-CS for describing data collections and services used in Research Data Australia.

Read more: [https://www.ands.org.au/guides/metadata-working Metadata schema]

Standardised open and universal schemas, e.g, [https://www.dcc.ac.uk/resources/metadata-standards/datacite-metadata-schema DataCite Metadata Schema], [https://www.dcc.ac.uk/resources/metadata-standards/prov PROV], [https://www.dcc.ac.uk/resources/metadata-standards/dublin-core Dublin Core]

Domain-specific standards, e.g. [http://human-phenotype-ontology.github.io/ HPO - Human Phenotype Ontology], [https://www.nlm.nih.gov/mesh/ MeSH - Medical Subject Headings] , [https://mcp-profile-docs.readthedocs.io/en/stable/ Marine Community Profile], [https://ddialliance.org/ DDI - Data Documentation Initiative].

For more examples of standards: [http://rd-alliance.github.io/metadata-directory/ Here].

Resolvable global identifiers - An identifier is any label used to name something uniquely (whether online or offline). URLs are an example of an identifier. So are serial numbers, and personal names. A persistent identifier is guaranteed to be managed and kept up to date over a defined time period.

Read more: [https://www.ands.org.au/guides/persistent-identifiers-awareness Persistent Identifiers]

|-
||I3
|| About metadata links
|| The goal is to create as many meaningful linkages as possible between (meta)data resources to enrich the contextual knowledge about the data, balanced against the time/energy involved in making a good data model.

'''Machine-readable (meta)data'''

(Meta)data in a format that can be automatically read and processed by a computer, such as [http://opendatahandbook.org/glossary/en/terms/csv/ CSV], [http://opendatahandbook.org/glossary/en/terms/json/ JSON], [http://opendatahandbook.org/glossary/en/terms/xml/ XML] etc. For more information: [http://opendatahandbook.org/glossary/en/terms/machine-readable/ Open Data Handbook]

'''Linked Data'''

Linked Data (also known as Linking Data) can be applied to improve the exploitation of the “Web of data.” The expression refers to the publishing of structured data in a way that typed links are created between data from different sources to provide a higher level of usability. By using Linked Data, it is possible to find other, related data. Structured data should meet four requirements to be called
Linked Data:

* URIs should be assigned to all entities of the dataset.
* HTTP URIs are required to ensure that all entities can be referenced and cited by users and user agents.
* Entities should be described using standard formats such as RDF/XML.
* Links should be created to other, related entity URIs.

All data that fulfil these requirements and are released for the public are called Linked Open Data (LOD). http://www.lesliesikos.com/semantic-web-machine-readable-structured-data-with-meaningful-annotations/

Read more under the heading (meta)data include qualified references to other (meta)data in the following resource: https://www.dtls.nl/fair-data/fair-principles-explained/

For more information on Linked Data standards - RDF - https://www.w3.org/RDF/

Other related linked data standards:
* OWL - https://www.w3.org/OWL/
* SKOS - https://www.w3.org/2004/02/skos/
* SPARQL - https://www.w3.org/TR/rdf-sparql-query/

|-
! rowspan="3" | R
|| R
|| About Reusable
|| Reusable data should maintain its initial richness. For example, it should not be abridged for the purpose of explaining the findings in one particular publication. It needs a clear machine-readable [https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse licence] and [https://www.ands.org.au/partners-and-communities/ands-communities/data-provenance-interest-group provenance] information on how the data was formed. It should also use discipline-specific data and [https://www.ands.org.au/working-with-data/metadata metadata] standards to give it rich contextual information that will allow for accurate interpretation and reuse.

|-
|| R1.1
|| About licenses
|| If data is not licensed no-one else can use it. In Australia, no licence is regarded as the same as 'all rights reserved', confining any reuse to very limited circumstances. Applying a Creative Commons licence to your data is a simple way to ensure that your data can be reused. The less restrictive the licence, the more that can be done with the data.
To make it easy for the Web to know when a work is available under a particular license, a “machine readable” version of the license provides a summary of the key freedoms and obligations written into a format that software systems, search engines, and other kinds of technology can understand.
Example from the [https://creativecommons.org/choose/ Creative Commons License Choser]:

What appears in the metadata:

This work is licensed under a [https://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International License].

Code snippet:

<code>
<a rel="license" href="http://creativecommons.org/licenses/by/4.0/">

<img alt="Creative Commons License" style="border-width:0"

src="https://i.creativecommons.org/l/by/4.0/88x31.png"/>

</a> This work is licensed under a

<a rel="license" href="http://creativecommons.org/licenses/by/4.0/">

Creative Commons Attribution 4.0 International License</a>.
<\code>

Read more: https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse

|-
||R1.2
|| About provenance
|| Data provenance is used to document where a piece of data comes from and the process and methodology by which it is produced. It is important to confirm the authenticity of data enabling trust, credibility and reproducibility. This is becoming increasingly important, especially in the eScience community where research is data intensive and often involves complex data transformations and procedures ([https://www.ands.org.au/partners-and-communities/ands-communities/data-provenance-interest-group Read more]). Provenance vocabularies such as [https://www.w3.org/2005/Incubator/prov/XGR-prov-20101214/#Recommendations Provenir Ontology (PROV-O) and others].

|}

== Results for SMILES and ShareWind ==

{| class="mw-collapsible wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Datasets from the SMILES and ShareWind platforms was analysed using the self-assessment tool provided by ARDC.
! rowspan="5" |
| rowspan="5" |
! Database Code
| rowspan="24" style="background: #ABE" |
| ED4
| ED9
|-
! Database Title
|| SMILES
| ShareWind
|-
! Database Link
|| https://www.ecria-smiles.eu/
| https://sharewind.eu/
|-
! Sample Link
|| [https://www.ecria-smiles.eu/data-files/-/document_library/W32cCAfL2jMX/view_file664466?_com_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX_redirect=https%3A%2F%2Fwww.ecria-smiles.eu%3A443%2Fdata-files%2F-%2Fdocument_library%2FW32cCAfL2jMX%2Fview%2F664423%3F_com_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX_redirect%3Dhttps%253A%252F%252Fwww.ecria-smiles.eu%253A443%252Fdata-files%253Fp_p_id%253Dcom_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX%2526p_p_lifecycle%253D0%2526p_p_state%253Dnormal%2526p_p_mode%253Dview here] (link has 585 characters)
| https://sharewind.eu/records/f9d31abbc2824284b8c1d28894d9619a (data found at https://zenodo.org/record/1162738)
|-
! Sample Size
|| 4 KB
| 37 MB
|-
| colspan="6" style="background: #ABE" |
|-
! rowspan="4" | Findable
| F1. (meta)data are assigned a globally unique and persistent identifier
! Does the dataset have any identifiers assigned?
|| Web adress (URL)
| Globally unique, citable and persistant (e.g. DOI, PURL, ARK or Handle)
|-

| F2. data are described with rich metadata (defined by R1 below)
! Is the dataset identifier included in all metadata records/files describing the data?
|| No
| No
|-

| F3. metadata clearly and explicitly include the identifier of the data it describes
! How is the data described with metadata?
|| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
|-

| F4. (meta)data are registered or indexed in a searchable resource
! What type of repository or registry is the metadata record in?
|| Domain-specific repository
| Data is in one place but discoverable through several registries
|-

! rowspan="3" | Accessible

| A1. (meta)data are retrievable by their identifier using a standardized communications protocol
! How accessible is the data?
|| Publicly accessible
| Publicly accessible
|-
| A1.1 the protocol is open, free, and universally implementable; A1.2 the protocol allows for an authentication and authorization procedure, where necessary
! Is the data available online without requiring specialised protocols or tools once access has been approved?
|| File download from online location
| Standard web service API (e.g. OGC)
|-
| A2. metadata are accessible, even when the data are no longer available
! Will the metadata record be available even if the data is no longer available?
|| Unsure
| Yes
|-

! rowspan="3" | Interoperable
| I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
! What (file) format(s) is the data available in?
|| In a structured, open standard, machine-readable format
|| In a structured, open standard, non-machine-readable format
|-
| I2. (meta)data use vocabularies that follow FAIR principles
! What best describes the types of vocabularies/ontologies/tagging schemas used to define the data elements?
|| Standardised vocabularies/ontologies/schema without global identifiers
|| Standardised vocabularies/ontologies/schema without global identifiers
|-
| I3. (meta)data include qualified references to other (meta)data
! How is the metadata linked to other data and metadata (to enhance context and clearly indicate relationships)?
|| There are no links to other metadata
|| Metadata is represented in a machine readable format, e.g. in a linked data format such as Resource Description Framework (RDF)
|-

! rowspan="3" | Reusable
| R1.1. (meta)data are released with a clear and accessible data usage license
! Which of the following best describes the license/usage rights attached to the data?
|| No license
| Standard machine-readable license (e.g. Creative Commons)
|-
| R1.2. (meta)data are associated with detailed provenance
! How much provenance information has been captured to facilitate data reuse?
|| Partially recorded
|| Partially recorded
|-
| R1.3. (meta)data meet domain-relevant community standards
|
|
|
|-
| colspan="6" style="background: #ABE" |
|-
! Total across F.A.I.R
|
! Low: < 40%; Medium: 50-60%; High: > 60%
|| 45 %
| 85 %
|-
| colspan="6" style="background: #ABE" |
|-
! Remarks about database
||
||
||
|| The data is provided by the open-source platform Zenodo, not the project website itself. A very positive aspect is that information on different versions of the data as well as the number of views and downloads is available.
|-
! Identified gaps
||
||
||
* No DOI, only URL
* Extremely long URL link spanning several lines with 585 characters, that can easily become corrupted
* No links to other metadata
* No license information given
* Provenance not fully recorded

||
* Dataset identifiers should be included in the files describing the data
* Provenance details are only partially recorded with a note with contact name for further information
|}

= Dublin Core Evaluation =

== Dublin Core Elements ==
{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ The DCMI Metadata Terms lists the current set of the Dublin Core vocabulary. Source: [https://dublincore.org/specifications/dublin-core/dcmi-terms/ Dublin Core Metadata Initiative]
| 1 || abstract || 11 || contributor || 21 || extent || 31 || isReplacedBy || 41 || publisher || 51 || tableOfContents
|-
| 2 || accessRights || 12 || coverage || 22 || format || 32 || isRequiredBy || 42 || references || 52 || temporal
|-
| 3 || accrualMethod || 13 || created || 23 || hasFormat || 33 || issued || 43 || relation || 53 || title
|-
| 4 || accrualPeriodicity || 14 || creator || 24 || hasPart || 34 || isVersionOf || 44 || replaces || 54 || type
|-
| 5 || accrualPolicy || 15 || date || 25 || hasVersion || 35 || language || 45 || requires || 55 || valid
|-
| 6 || alternative || 16 || dateAccepted || 26 || identifier || 36 || license || 46 || rights || ||
|-
| 7 || audience || 17 || dateCopyrighted || 27 || instructionalMethod || 37 || mediator || 47 || rightsHolder || ||
|-
| 8 || available || 18 || dateSubmitted || 28 || isFormatOf || 38 || medium || 48 || source || ||
|-
| 9 || bibliographicCitation || 19 || description || 29 || isPartOf || 39 || modified || 49 || spatial || ||
|-
| 10 || conformsTo || 20 || educationLevel || 30 || isReferencedBy || 40 || provenance || 50 || subject || ||
|}

'''Metadata Category Types according to [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO]'''

There are three main types of metadata:
* Descriptive metadata describes a resource for purposes such as discovery and identification. It can include elements such as title, abstract, author, and keywords.
* Structural metadata indicates how compound objects are put together, for example, how pages are ordered to form chapters.
* Administrative metadata provides information to help manage a resource, such as when and how it was created, file type and other technical information, and who can access it. There are several subsets of administrative data; two that sometimes are listed as separate metadata types are:
** Rights management meta-data, which deals with intellectual property rights,and
** Preservation metadata, which contains information needed to archive and preserve a resource.
''

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Simple Dublin Core Elements. 15 DC elements with their (shortened) official definitions and examples.
! DC element name !! DC definition ([https://www.dublincore.org/specifications/dublin-core/dces/ DCMI]) !! Example (Energy Domain) !! Category (determined with the aid of [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO])
|-
! Title
| A name given to the resource || Outdoor monitoring of a hybrid micro-CPV solar panel with integrated micro-tracking and diffuse capture || Descriptive
|-
! Subject
| The topic of the resource || concentrator photovoltaics; CPV; rooftop photovoltaics; integrated tracking || Descriptive
|-
! Description
| An account of the resource || Dataset from the outdoor characterization of a B Series module from Insolight at the rooftop of the Instituto de Energía Solar - Universidad Politécnica de Madrid. …. || Descriptive
|-
! Creator
| An entity primarily responsible for making the resource || Stephen Askins; Gaël Nardin || Descriptive
|-
! Publisher
| An entity responsible for making the resource available || Stephen Askins || Descriptive
|-
! Contributor
| An entity responsible for making contributions to the resource || Stephen Askins || Descriptive
|-
! Date
| A point or period of time associated with an event in the lifecycle of the resource || 2019-07-23; Date will be associated with the creation or availability of the resource. || Administrative
|-
! Type
| The nature or genre of the resource || info:eu-repo/semantics/other; dataset || Descriptive
|-
! Format
| The file format, physical medium, or dimensions of the resource || image
|| Structural
|-
! Identifier
| An unambiguous reference to the resource within a given context || https://zenodo.org/record/3346823; 10.5281/zenodo.3346823; oai:zenodo.org:3346823 || Descriptive
|-
! Source
| A related resource from which the described resource is derived || RC607.A26W574 1996 [where "RC607.A26W574 1996" is the call number of the print version of the resource, from which the present version was scanned] || Descriptive
|-
! Language
| A language of the resource || eng || Descriptive
|-
! Relation
| A related resource || info:eu-repo/grantAgreement/EC/H2020/787289/ || Descriptive
|-
! Coverage
| The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant || 1995-1996; Madrid, Spain || Descriptive
|-
! Rights
| Information about rights held in and over the resource || info:eu-repo/semantics/openAccess; https://creativecommons.org/licenses/by/4.0/legalcode || Administrative
|}

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Non-DC elements

! Non-DC element name !! Definition !! Example !! Category
|-
! Date of availability
| Since when is the data available
||
|| Impact/Exploitation

|-
! Number of users
| Is there any information on the number of accesses or users?
||
|| Impact/Exploitation

|-
! Number of scientific publications where data is cited/used
| Is there any information on the number of citations/uses?
||
|| Impact/Exploitation

|-
! Curation activities (is the versioning ongoing)
| Update, maintainance
||
|| Impact/Exploitation
|}

Attempting to put a value on Impact/Exploitation:
* '''ENTSO-E''': Found 172 results in Advanced Search (ALL) in Web of Science ([https://apps.webofknowledge.com/ WoSc]) (16/06/2020). Citations of all these search result add up to 714. This however does not mean, that all search results really explicitly used data from ENTSO-E. Some (for sure, for some were found) only mentioned it saying they adoped workflow etc. from the site.
* '''SMARD''': Found 82 results in Advanced Search (ALL=(SMARD)) in Web of Science ([https://apps.webofknowledge.com/ WoSc]) (16/06/2020). However: SMARD is also an acronym for ''Spinal muscular atrophy with respiratory distress (SMARD)''. This somewhat complicates results and needs some further discussion. All 82 search results accumumlated 2359 citations themselves.

Gap analysis

2020-08-28T08:38:42Z

Christopherbs: /* FAIR Self-Assessment tool */

<accesscontrol>Administrators,consortium</accesscontrol>

This is the page for the gap analysis.

= Tacit knowledge "FAIRification and opening of low carbon energy research data" =
 Consortium members can add any time, they feel that something is important to note even though it is not mentioned in a standard deliverable. In other words, this page collects uncodified, tacit knowledge. It will help us later to compile suggestions and lessons learned. This kind of knowledge is collected two-fold:
#Anytime if someone feels that this should be noted. Please write down: Issue, Date, Author (could also be "anonymous"), the issue described in a few words or maximal lines>
#During the final day of workshops

=== Learning process: ===

*People are hesitant to adopt new IT technologies, this is even the case among researchers heavily relying on data, algorithms and collaborative online software (e.g., R, platforms, online conferencing, HPC, ...). The effort to encourage change is not to underestimate. Reasons are several, notably, lack of time and uncertainty about potential benefit as well as overall risk aversion preferences. EERAdata is using the online software "Only Office" to facilitate collaboration (in particular also during the Covid-19 period).
 '''FAIR and open criteria:'''
*Consortium members have a fair understanding of what FAIR/O is, but there is little knowledge and/or technical experience on how to approach the FAIRification and opening. All, however, share the view that we are at a critical point in time, where we need to implement these criteria. 
*To deepen knowledge about FAIR/O criteria, it is useful to test the criteria on a database one is familiar with. For this purpose (and to start brainstorming about the platform), AIT has developed a questionnaire for application in the use cases.
'''Metadata:'''
*A good starting point is to think about metadata and to look into existing metadata concepts in one's field. The first step is to understand that also metadata need to adhere to the FAIR/O principles.
*The next step is to increase knowledge on IT specific terms, i.e. to understand what the difference is between different '''[https://energydata.onlyoffice.eu/Products/Files/doceditor.aspx?fileid=5404340 metadata frameworks] (taxonomy, thesaurus, ontology) as well as classification of metadata (high-level, medium-level, low-level OR administrative, structural and descriptive metadata).  '''WP 2 being in charge of aligning approaches between use cases, participates in all use case kickoffs to bring everybody on the same page. The presentation is linked with "metadata frameworks".
*It is useful to supply consortium members with read aheads and watch aheads on metadata to prepare the '''[https://energydata.onlyoffice.eu/Products/Files/doceditor.aspx?fileid=5296590 first EERAdata workshop]'''. The workshop starts applications and discussions in the use cases break out sessions (and bringing insights back to the plenary), using selected databases.

= FAIR Assessment=

== FAIR Guiding Principles ==
{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ This table shows the FAIR guiding principles described by [https://www.doi.org/10.1038/sdata.2016.18 Wilkinson et al., 2016].

! To be Findable
|
* F1. (meta)data are assigned a globally unique and persistent identifier
* F2. data are described with rich metadata (defined by R1 below)
* F3. metadata clearly and explicitly include the identifier of the data it describes
* F4. (meta)data are registered or indexed in a searchable resource

|-
! To be Accessible
|
* A1. (meta)data are retrievable by their identifier using a standardized communications protocol
** A1.1 the protocol is open, free, and universally implementable
** A1.2 the protocol allows for an authentication and authorization procedure, where necessary
* A2. metadata are accessible, even when the data are no longer available

|-
! To be Interoperable
|
* I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
* I2. (meta)data use vocabularies that follow FAIR principles
* I3. (meta)data include qualified references to other (meta)data

|-
! To be Reusable
|
* R1. meta(data) are richly described with a plurality of accurate and relevant attributes
* R1.1. (meta)data are released with a clear and accessible data usage license
* R1.2. (meta)data are associated with detailed provenance
* R1.3. (meta)data meet domain-relevant community standards

|}

== Identified gaps after Workshop 1 (02/06/2020 – 04/06/2020) ==

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ This table shows a summary of the specific issues that were identified in Workshop 1. The aim was to categorise different issues to get a better understanding of how to tackle these challenges.
! FAIR/O !! Specific FAIR/O !! Gaps !! Gaps for energy domain !! Use case specific gaps !! Consequences for researchers
|-
! F
| F2
|| Metadata scope
|| Different fields require different metadata (potentially very specific)
|| UC3: lack of additional metadata for applications of materials
|| Data are less useful for the specific field
|-
! F
|| F1/F3
|| Identifiers
||
* Missing identifiers on websites
* Identifier not in downloaded data files
||
|| Makes research more difficult.
|-
! I
| I2
|| Taxonomy/ontology/common vocabulary and language issues
||
* Lack of standardisation
* Heterogeneous data makes standardisation hard
* No vocabulary documentation on websites
* Words used for same term in other languages may differ
* Databases in other languages than English
||
|| Research costs more time
|-
! I
| I3
|| References to other (meta)data
||
* No linking to source documents and related publications (for contextual knowledge)
* Possible need for links to other fields
|| UC3: link between microscopic and macroscopic materials (e.g., turbine blades)
||
* Data cannot be connected to the source
* Makes research more difficult.

|-
! R
| R1.1
|| Licensing
||
* No licenses available may mean the data is not reusable for the researcher
* Licenses not clear and accessible
* Obtaining licenses may result in more effort and costs
||
|| Data potentially not reusable

|-
! R
| R1.2
|| Reliability and provenance of data
|| When the data is collected from different and high number of sources, its reliability decreases
||
|| Data potentially not reusable

|-
! O
|
|| Privacy concerns and expected disadvantages
|| Not publishing data due to privacy concerns (sensitive data)
|| UC2: In case of distribution network data this is relevant
|| Data not accessible

|-
! Other
|
|| Conducting FAIR assessments
||
* No qualitative assessment of the data itself (which may be limited)
* Discrepancies between results conducted by humans versus machines
* Sometimes not even DC standards are met
* Metadata is not updated
* Lacking encryption of websites (https)
* Problems assessing whether metadata will be available after data is unavailable
* Interface design/layout may be unclear, incomplete or not intuitive for humans
* Authentication details (user registration login / good or bad, clarify)

||
* UC1 lack of data availability for time-series
||
* Assessment is uncertain (human assessment may need more clarification and understanding)
* Makes research more time-intensive

|}

== FAIR Self-Assessment tool ==

To conduct a human FAIR evaluation for different databases and their respective datasets, different free online tools were considered. Some seemed to be quite useful (e.g. [https://satifyd.dans.knaw.nl/ SATISFYD] ) and some were simple self-evaluation PDFs to be printed and filled out.

We decided to chose the [https://ardc.edu.au/resources/working-with-data/fair-data/fair-self-assessment-tool/ self-assessment tool] provided by the Australian Research Data Commons (ARDC). The decision was based manly upon the fact that the tools questions closely matched the FAIR categories specified by [https://www.doi.org/10.1038/sdata.2016.18 Wilkinson et al., 2016] which would make it easily comparable to the existing preferable FAIR conditions and aid with the repeatability of the results, and the half-automated scoring features that show the extent of the "FAIRness" of the analysed sample as a simple bar chart.

The result of the FAIR assessment is provided as a bar chart where an empty bar chart displays 0 % FAIR compatibility, and a full bar chart represents 100 % FAIR compatibility. The score is therefore not returned as a specific value. During the assessment below, the "Total Across FAIR" in Table ... was estimated according to the level of the bar chart from 0 % to 100 %.

The tool gives a FAIR asssessment by providing

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ All options for each question of the ARDCs self-assessment tool
!colspan="4"|Findable
!colspan="3"|Accessible
!colspan="3"|Interoperable
!colspan="3"|Reusable
|-
| F1. (meta)data are assigned a globally unique and persistent identifier
|| F2. data are described with rich metadata (defined by R1 below)
|| F3. metadata clearly and explicitly include the identifier of the data it describes
|| F4. (meta)data are registered or indexed in a searchable resource
|| A1. (meta)data are retrievable by their identifier using a standardized communications protocol
|| A1.1 the protocol is open, free, and universally implementable and A1.2 the protocol allows for an authentication and authorization procedure, where necessary
|| A2. metadata are accessible, even when the data are no longer available
|| I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
|| I2. (meta)data use vocabularies that follow FAIR principles
|| I3. (meta)data include qualified references to other (meta)data
|| R1.1. (meta)data are released with a clear and accessible data usage license
|| R1.2. (meta)data are associated with detailed provenance
|| R1.3. (meta)data meet domain-relevant community standards
|-
! Does the dataset have any identifiers assigned?
! Is the dataset identifier included in all metadata records/files describing the data?
! How is the data described with metadata?
! What type of repository or registry is the metadata record in?
! How accessible is the data?
! Is the data available online without requiring specialised protocols or tools once access has been approved?
! Will the metadata record be available even if the data is no longer available?
! What (file) format(s) is the data available in?
! What best describes the types of vocabularies/ontologies/tagging schemas used to define the data elements?
! How is the metadata linked to other data and metadata (to enhance context and clearly indicate relationships)?
! Which of the following best describes the license/usage rights attached to the data?
! How much provenance information has been captured to facilitate data reuse?
!
|-
| Globally unique, citable and persistant (e.g. DOI, PURL, ARK or Handle)
|| Yes
|| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
|| Data is in one place but discoverable through several registries
|| Publicly accessible
|| Standard web service API (e.g. OGC)
|| Yes
|| In a structured, open standard, machine-readable format
|| Standardised open and universal using resolvable global identifiers linking to explainations
|| Meadata is represented in a machine readable format, e.g. in a linked data format such as Resource Description Framework (RDF)
|| Standard machine-readable license (e.g. Creative Commons)
|| Fully recorded in a machine-readable format
||
|-
| Web adress (URL)
|| No
|| Comprehensively, but in a text-based, non-standard format
|| Generalist public repository
|| Fully accessible to person who meet explicitly stated conditions, e.g. ethics approval for sensitive data
|| Non-standard web service (e.g. OpenAPI/Swagger/informal API)
|| No
|| In a structured, open standard, non-machine-readable format
|| Standardised vocabularies/ontologies/schema without global identifiers
|| The metadata record includes URI links to related metadata, data and definitions
|| Standard text based license
|| Fully recorded in a text format
||
|-
| Local identifier
||
|| Brief title and description
|| Domain-specific repository
|| A de-identified / modified subset of the data is publicly accessible
|| File download from online location
|| Unsure
|| Mostly in a proprietary format
|| No standards have been applied in the description of data elements
|| There are no links to other metadata
|| Non-standard machine-readably license (clearly indicating under what conditions the data may be reused)
|| Partially recorded
||
|-
| No identifier
||
|| The data is not described
|| Local institutional repository
|| Embaged access after a specific date
|| By individual arrangement
||
||
|| Data elements not described
||
|| Non-standard text-based license
|| No provenance information is recorded
||
|-
|
||
||
|| The data is not described in any repository
|| Unspecified conditional access e.g. contact the data custodian for access
|| No access to data
||
||
||
||
|| No license
||
||
|-
|
||
||
||
|| Access to metadata only
||
||
||
||
||
||
||
||
|-
|
||
||
||
|| No access to data or metadata
||
||
||
||
||
||
||
||
|}

At this point it is important to note that the assessment of databases and/or datasets using this tool is solely considered a human assessment. This may lead to different evaluations by different users. To counteract this phenomenon, it was suggested that the evaluations should be completed using the 'four-eyes principle' to create more verifiable results. Also, it is planned to additionally evaluate the respective databases using an external tool to perform a machine-assessment.

In addition, not all questions are weighted equally and thus a worse result for one question may not have a large impact whereas only choosing the second-best option in another question will bring down the overall FAIR value considerably.

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ The ARDC tool provides detailed information about the requirements for each of the options of its tool. These descriptions are documented here.
|-
! rowspan="5" | F
|| F
|| About Findable
|| Making data Findable includes assigning a persistent identifier (like a DOI or Handle ), having rich metadata to describe the data and making sure it is findable through disciplinary and generalist discovery portals (local and international).

|-
| F1
|| About identifiers
|| Identifiers are essential for identifying, finding, retrieving, linking and citing datasets. A Web address (URL) can be used to specify the online location of a resource but over time URLs tend to change which leads to broken links to the data. To be useful, identifiers need to be persistent and unique. Digital Object Identifiers (DOIs) and other persistent identifiers (PIDs) provide a permanent citable reference to a particular dataset. The DOI is a permanent fixed reference to the dataset no matter where it is located online and enables citation and citation metrics. Services to create a persistent identifier are often offered by your affiliated institution or the repository you are using to describe your data. Talk to your library service for more information.
|-
| F2
|| About identifiers
|| The identifier (preferably a persistent identifier) needs to be clearly stated in the metadata record describing the data collection, and also in any associated data files or metadata.

|-
|F3
|| About metadata descriptions
|| Comprehensive metadata records will include descriptive content that facilitates discovery, access and reuse of the data being described. While there is no 'one size fits all' list, comprehensive metadata should include:
* a globally unique persistent identifier e.g. a DOI
* a title
* related people, i.e. the data creator or custodian
* how to access the data and file formats
* a description of how the data were created and how to interpret the data subject or keywords
* citation information that clearly indicates how the data should be cited
* a machine-readable data licence
* provenance and contextual information such as:
* links to related publications, projects, services and software
* methodology and processes involved in data production
* spatial and temporal coverage (if relevant)
* object-level data description

Providing metadata in a standard schema allows it to be read and used by machines as well as humans.

|-
| F4
|| About metadata repositories
|| A rich metadata description alone does not ensure a dataset’s ‘findability’ on the internet; the dataset needs to be registered or indexed in a searchable resource, such as a generalist (e.g. Figshare, Dryad, Zenodo domain-specific (e.g. PANGAEA, ADA, ICPSR), or institutional (e.g. ANU Research Repository , Sydney eScholarship Repository, data.gov.au) data repository or registry. Ideally these repositories/registries are indexed by search engines such as Google and/or Google Scholar.

|-
! rowspan = "4" | A
| A
|| About Accessible
|| To make data accessible may include making the data open using a standardised protocol. However the data does not necessarily have to be open. There are sometimes good reasons why data cannot be made open, for example privacy concerns, national security or commercial interests. If it is not open there should be clarity and transparency around the conditions governing access and reuse
|-
| A1
|| About data accessibility
|| Not all data that is discoverable can be freely accessed. Often there are embargoes, access controls, and access permissions associated with data due to a variety of issues such as privacy and commercial interests. Even with all these issues much sensitive data can be shared. Many other issues that could be perceived as blockers to sharing data may be overcome.
|-
| A1.1/A1.2
|| About protocols
|| Ideally users would like to retrieve appropriate internet content directly and unhindered once they have located it. Internet protocols (e.g. http and ftp) define rules and conventions for communication between devices, and tools and services are available to facilitate this process, e.g. APIs.

Protocols:
* HTTP (Hypertext Transfer Protocol) is the set of rules for transferring files (text, graphic images, sound, video, and other multimedia files) on the World Wide Web. As soon as a Web user opens their Web browser, the user is indirectly making use of HTTP.
* FTP (File Transfer Protocol) is a standard Internet protocol for transmitting files between computers on the Internet over TCP/IP connections. Read more: Web protocols
* API (Application Programming Interface): When information is made available in any machine readable format, it becomes possible to make that information directly available to programs that request that information over the web. An API is the way this information is made directly available to other machines.

|-
| A2
|| About metadata availability
|| Effort is often required to maintain data resources online which can often lead to it being neglected especially over long periods of time. This often leads to broken links between the metadata and the data. Having at least a description of the data in the form of a metadata record means there is a record of the data's existence allowing the possibility of someone tracking it down.

|-
! rowspan="4" | I
|| I
|| About Interoperability
|| To be interoperable (i.e. data that is interpretable by a computer, so that they can be automatically combined with other data) the data will need to use community agreed formats, language and vocabularies. The metadata will also need to use a community agreed standards and vocabularies, and contain links to related information using identifiers.
|-
|| I1
|| About file formats
|| When selecting file formats for archiving, the formats should ideally be:
* Non-proprietary
* Unencrypted
* Uncompressed
* In common usage by the research community
* Adherent to an open, documented standard, such as described by the State of California (see AB 1668, 2007)
* Interoperable among diverse platforms and applications
* Fully published and available royalty-free
* Fully and independently implementable by multiple software providers on multiple platforms without any intellectual property restrictions for necessary technology
* Developed and maintained by an open standards organization with a well-defined inclusive process for evolution of the standard.

From: Stanford University Library - Best practices for file formats
File format examples:
* Structured, open standard, machine-readable format e.g. (text) PDF/A, HTML, Plain text, (images) TIFF, JPEG 2000, GIF, (audio) MP3, AIFF, WAVE, (video) MOV, MPEG, AVI, (Tabular data) CSV
* structured, open standard, non-machine-readable format, e.g. PDF, HTML, JPG
* Proprietary format, e.g. doc (Word), .xls (Excel), .ppt (PowerPoint), .sav

Read more:
* ANDS
* European Data Portal

|-
|| I2
|| About schema standards
|| Schema standards are schemas that have gone through a formal validation process by a standards organisation, such as the International Standards Organisation (ISO) or an equivalent body such as the Dublin Core Metadata Initiative (DCMI); or, commonly used and consistently applied metadata schemas that are well documented, endorsed, and maintained can also become ‘de-facto standards’, e.g. RIF-CS for describing data collections and services used in Research Data Australia.

Read more: [https://www.ands.org.au/guides/metadata-working Metadata schema]

Standardised open and universal schemas, e.g, [https://www.dcc.ac.uk/resources/metadata-standards/datacite-metadata-schema DataCite Metadata Schema], [https://www.dcc.ac.uk/resources/metadata-standards/prov PROV], [https://www.dcc.ac.uk/resources/metadata-standards/dublin-core Dublin Core]

Domain-specific standards, e.g. [http://human-phenotype-ontology.github.io/ HPO - Human Phenotype Ontology], [https://www.nlm.nih.gov/mesh/ MeSH - Medical Subject Headings] , [https://mcp-profile-docs.readthedocs.io/en/stable/ Marine Community Profile], [https://ddialliance.org/ DDI - Data Documentation Initiative].

For more examples of standards: [http://rd-alliance.github.io/metadata-directory/ Here].

Resolvable global identifiers - An identifier is any label used to name something uniquely (whether online or offline). URLs are an example of an identifier. So are serial numbers, and personal names. A persistent identifier is guaranteed to be managed and kept up to date over a defined time period.

Read more: [https://www.ands.org.au/guides/persistent-identifiers-awareness Persistent Identifiers]

|-
||I3
|| About metadata links
|| The goal is to create as many meaningful linkages as possible between (meta)data resources to enrich the contextual knowledge about the data, balanced against the time/energy involved in making a good data model.

'''Machine-readable (meta)data'''

(Meta)data in a format that can be automatically read and processed by a computer, such as [http://opendatahandbook.org/glossary/en/terms/csv/ CSV], [http://opendatahandbook.org/glossary/en/terms/json/ JSON], [http://opendatahandbook.org/glossary/en/terms/xml/ XML] etc. For more information: [http://opendatahandbook.org/glossary/en/terms/machine-readable/ Open Data Handbook]

'''Linked Data'''

Linked Data (also known as Linking Data) can be applied to improve the exploitation of the “Web of data.” The expression refers to the publishing of structured data in a way that typed links are created between data from different sources to provide a higher level of usability. By using Linked Data, it is possible to find other, related data. Structured data should meet four requirements to be called
Linked Data:

* URIs should be assigned to all entities of the dataset.
* HTTP URIs are required to ensure that all entities can be referenced and cited by users and user agents.
* Entities should be described using standard formats such as RDF/XML.
* Links should be created to other, related entity URIs.

All data that fulfil these requirements and are released for the public are called Linked Open Data (LOD). http://www.lesliesikos.com/semantic-web-machine-readable-structured-data-with-meaningful-annotations/

Read more under the heading (meta)data include qualified references to other (meta)data in the following resource: https://www.dtls.nl/fair-data/fair-principles-explained/

For more information on Linked Data standards - RDF - https://www.w3.org/RDF/

Other related linked data standards:
* OWL - https://www.w3.org/OWL/
* SKOS - https://www.w3.org/2004/02/skos/
* SPARQL - https://www.w3.org/TR/rdf-sparql-query/

|-
! rowspan="3" | R
|| R
|| About Reusable
|| Reusable data should maintain its initial richness. For example, it should not be abridged for the purpose of explaining the findings in one particular publication. It needs a clear machine-readable [https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse licence] and [https://www.ands.org.au/partners-and-communities/ands-communities/data-provenance-interest-group provenance] information on how the data was formed. It should also use discipline-specific data and [https://www.ands.org.au/working-with-data/metadata metadata] standards to give it rich contextual information that will allow for accurate interpretation and reuse.

|-
|| R1.1
|| About licenses
|| If data is not licensed no-one else can use it. In Australia, no licence is regarded as the same as 'all rights reserved', confining any reuse to very limited circumstances. Applying a Creative Commons licence to your data is a simple way to ensure that your data can be reused. The less restrictive the licence, the more that can be done with the data.
To make it easy for the Web to know when a work is available under a particular license, a “machine readable” version of the license provides a summary of the key freedoms and obligations written into a format that software systems, search engines, and other kinds of technology can understand.
Example from the [https://creativecommons.org/choose/ Creative Commons License Choser]:

What appears in the metadata:

This work is licensed under a [https://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International License].

Code snippet:

<code>
<a rel="license" href="http://creativecommons.org/licenses/by/4.0/">

<img alt="Creative Commons License" style="border-width:0"

src="https://i.creativecommons.org/l/by/4.0/88x31.png"/>

</a> This work is licensed under a

<a rel="license" href="http://creativecommons.org/licenses/by/4.0/">

Creative Commons Attribution 4.0 International License</a>.
<\code>

Read more: https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse

|-
||R1.2
|| About provenance
|| Data provenance is used to document where a piece of data comes from and the process and methodology by which it is produced. It is important to confirm the authenticity of data enabling trust, credibility and reproducibility. This is becoming increasingly important, especially in the eScience community where research is data intensive and often involves complex data transformations and procedures ([https://www.ands.org.au/partners-and-communities/ands-communities/data-provenance-interest-group Read more]). Provenance vocabularies such as [https://www.w3.org/2005/Incubator/prov/XGR-prov-20101214/#Recommendations Provenir Ontology (PROV-O) and others].

|}

== Results for SMILES and ShareWind ==

{| class="mw-collapsible wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Datasets from the SMILES and ShareWind platforms was analysed using the self-assessment tool provided by ARDC.
! rowspan="5" |
| rowspan="5" |
! Database Code
| rowspan="24" style="background: #ABE" |
| ED4
| ED9
|-
! Database Title
|| SMILES
| ShareWind
|-
! Database Link
|| https://www.ecria-smiles.eu/
| https://sharewind.eu/
|-
! Sample Link
|| [https://www.ecria-smiles.eu/data-files/-/document_library/W32cCAfL2jMX/view_file664466?_com_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX_redirect=https%3A%2F%2Fwww.ecria-smiles.eu%3A443%2Fdata-files%2F-%2Fdocument_library%2FW32cCAfL2jMX%2Fview%2F664423%3F_com_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX_redirect%3Dhttps%253A%252F%252Fwww.ecria-smiles.eu%253A443%252Fdata-files%253Fp_p_id%253Dcom_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX%2526p_p_lifecycle%253D0%2526p_p_state%253Dnormal%2526p_p_mode%253Dview here] (link has 585 characters)
| https://sharewind.eu/records/f9d31abbc2824284b8c1d28894d9619a (data found at https://zenodo.org/record/1162738)
|-
! Sample Size
|| 4 KB
| 37 MB
|-
| colspan="6" style="background: #ABE" |
|-
! rowspan="4" | Findable
| F1. (meta)data are assigned a globally unique and persistent identifier
! Does the dataset have any identifiers assigned?
|| Web adress (URL)
| Globally unique, citable and persistant (e.g. DOI, PURL, ARK or Handle)
|-

| F2. data are described with rich metadata (defined by R1 below)
! Is the dataset identifier included in all metadata records/files describing the data?
|| No
| No
|-

| F3. metadata clearly and explicitly include the identifier of the data it describes
! How is the data described with metadata?
|| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
|-

| F4. (meta)data are registered or indexed in a searchable resource
! What type of repository or registry is the metadata record in?
|| Domain-specific repository
| Data is in one place but discoverable through several registries
|-

! rowspan="3" | Accessible

| A1. (meta)data are retrievable by their identifier using a standardized communications protocol
! How accessible is the data?
|| Publicly accessible
| Publicly accessible
|-
| A1.1 the protocol is open, free, and universally implementable; A1.2 the protocol allows for an authentication and authorization procedure, where necessary
! Is the data available online without requiring specialised protocols or tools once access has been approved?
|| File download from online location
| Standard web service API (e.g. OGC)
|-
| A2. metadata are accessible, even when the data are no longer available
! Will the metadata record be available even if the data is no longer available?
|| Unsure
| Yes
|-

! rowspan="3" | Interoperable
| I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
! What (file) format(s) is the data available in?
|| In a structured, open standard, machine-readable format
|| In a structured, open standard, non-machine-readable format
|-
| I2. (meta)data use vocabularies that follow FAIR principles
! What best describes the types of vocabularies/ontologies/tagging schemas used to define the data elements?
|| Standardised vocabularies/ontologies/schema without global identifiers
|| Standardised vocabularies/ontologies/schema without global identifiers
|-
| I3. (meta)data include qualified references to other (meta)data
! How is the metadata linked to other data and metadata (to enhance context and clearly indicate relationships)?
|| There are no links to other metadata
|| Metadata is represented in a machine readable format, e.g. in a linked data format such as Resource Description Framework (RDF)
|-

! rowspan="3" | Reusable
| R1.1. (meta)data are released with a clear and accessible data usage license
! Which of the following best describes the license/usage rights attached to the data?
|| No license
| Standard machine-readable license (e.g. Creative Commons)
|-
| R1.2. (meta)data are associated with detailed provenance
! How much provenance information has been captured to facilitate data reuse?
|| Partially recorded
|| Partially recorded
|-
| R1.3. (meta)data meet domain-relevant community standards
|
|
|
|-
| colspan="6" style="background: #ABE" |
|-
! Total across F.A.I.R
|
! Low: < 40%; Medium: 50-60%; High: > 60%
|| 45 %
| 85 %
|-
| colspan="6" style="background: #ABE" |
|-
! Remarks about database
||
||
||
|| The data is provided by the open-source platform Zenodo, not the project website itself. A very positive aspect is that information on different versions of the data as well as the number of views and downloads is available.
|-
! Identified gaps
||
||
||
* No DOI, only URL
* Extremely long URL link spanning several lines with 585 characters, that can easily become corrupted
* No links to other metadata
* No license information given
* Provenance not fully recorded

||
* Dataset identifiers should be included in the files describing the data
* Provenance details are only partially recorded with a note with contact name for further information
|}

= Dublin Core Evaluation =

== Dublin Core Elements ==
{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ The DCMI Metadata Terms lists the current set of the Dublin Core vocabulary. Source: [https://dublincore.org/specifications/dublin-core/dcmi-terms/ Dublin Core Metadata Initiative]
| 1 || abstract || 11 || contributor || 21 || extent || 31 || isReplacedBy || 41 || publisher || 51 || tableOfContents
|-
| 2 || accessRights || 12 || coverage || 22 || format || 32 || isRequiredBy || 42 || references || 52 || temporal
|-
| 3 || accrualMethod || 13 || created || 23 || hasFormat || 33 || issued || 43 || relation || 53 || title
|-
| 4 || accrualPeriodicity || 14 || creator || 24 || hasPart || 34 || isVersionOf || 44 || replaces || 54 || type
|-
| 5 || accrualPolicy || 15 || date || 25 || hasVersion || 35 || language || 45 || requires || 55 || valid
|-
| 6 || alternative || 16 || dateAccepted || 26 || identifier || 36 || license || 46 || rights || ||
|-
| 7 || audience || 17 || dateCopyrighted || 27 || instructionalMethod || 37 || mediator || 47 || rightsHolder || ||
|-
| 8 || available || 18 || dateSubmitted || 28 || isFormatOf || 38 || medium || 48 || source || ||
|-
| 9 || bibliographicCitation || 19 || description || 29 || isPartOf || 39 || modified || 49 || spatial || ||
|-
| 10 || conformsTo || 20 || educationLevel || 30 || isReferencedBy || 40 || provenance || 50 || subject || ||
|}

'''Metadata Category Types according to [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO]'''

There are three main types of metadata:
* Descriptive metadata describes a resource for purposes such as discovery and identification. It can include elements such as title, abstract, author, and keywords.
* Structural metadata indicates how compound objects are put together, for example, how pages are ordered to form chapters.
* Administrative metadata provides information to help manage a resource, such as when and how it was created, file type and other technical information, and who can access it. There are several subsets of administrative data; two that sometimes are listed as separate metadata types are:
** Rights management meta-data, which deals with intellectual property rights,and
** Preservation metadata, which contains information needed to archive and preserve a resource.
''

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Simple Dublin Core Elements. 15 DC elements with their (shortened) official definitions and examples.
! DC element name !! DC definition ([https://www.dublincore.org/specifications/dublin-core/dces/ DCMI]) !! Example (Energy Domain) !! Category (determined with the aid of [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO])
|-
! Title
| A name given to the resource || Outdoor monitoring of a hybrid micro-CPV solar panel with integrated micro-tracking and diffuse capture || Descriptive
|-
! Subject
| The topic of the resource || concentrator photovoltaics; CPV; rooftop photovoltaics; integrated tracking || Descriptive
|-
! Description
| An account of the resource || Dataset from the outdoor characterization of a B Series module from Insolight at the rooftop of the Instituto de Energía Solar - Universidad Politécnica de Madrid. …. || Descriptive
|-
! Creator
| An entity primarily responsible for making the resource || Stephen Askins; Gaël Nardin || Descriptive
|-
! Publisher
| An entity responsible for making the resource available || Stephen Askins || Descriptive
|-
! Contributor
| An entity responsible for making contributions to the resource || Stephen Askins || Descriptive
|-
! Date
| A point or period of time associated with an event in the lifecycle of the resource || 2019-07-23; Date will be associated with the creation or availability of the resource. || Administrative
|-
! Type
| The nature or genre of the resource || info:eu-repo/semantics/other; dataset || Descriptive
|-
! Format
| The file format, physical medium, or dimensions of the resource || image
|| Structural
|-
! Identifier
| An unambiguous reference to the resource within a given context || https://zenodo.org/record/3346823; 10.5281/zenodo.3346823; oai:zenodo.org:3346823 || Descriptive
|-
! Source
| A related resource from which the described resource is derived || RC607.A26W574 1996 [where "RC607.A26W574 1996" is the call number of the print version of the resource, from which the present version was scanned] || Descriptive
|-
! Language
| A language of the resource || eng || Descriptive
|-
! Relation
| A related resource || info:eu-repo/grantAgreement/EC/H2020/787289/ || Descriptive
|-
! Coverage
| The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant || 1995-1996; Madrid, Spain || Descriptive
|-
! Rights
| Information about rights held in and over the resource || info:eu-repo/semantics/openAccess; https://creativecommons.org/licenses/by/4.0/legalcode || Administrative
|}

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Non-DC elements

! Non-DC element name !! Definition !! Example !! Category
|-
! Date of availability
| Since when is the data available
||
|| Impact/Exploitation

|-
! Number of users
| Is there any information on the number of accesses or users?
||
|| Impact/Exploitation

|-
! Number of scientific publications where data is cited/used
| Is there any information on the number of citations/uses?
||
|| Impact/Exploitation

|-
! Curation activities (is the versioning ongoing)
| Update, maintainance
||
|| Impact/Exploitation
|}

Attempting to put a value on Impact/Exploitation:
* '''ENTSO-E''': Found 172 results in Advanced Search (ALL) in Web of Science ([https://apps.webofknowledge.com/ WoSc]) (16/06/2020). Citations of all these search result add up to 714. This however does not mean, that all search results really explicitly used data from ENTSO-E. Some (for sure, for some were found) only mentioned it saying they adoped workflow etc. from the site.
* '''SMARD''': Found 82 results in Advanced Search (ALL=(SMARD)) in Web of Science ([https://apps.webofknowledge.com/ WoSc]) (16/06/2020). However: SMARD is also an acronym for ''Spinal muscular atrophy with respiratory distress (SMARD)''. This somewhat complicates results and needs some further discussion. All 82 search results accumumlated 2359 citations themselves.

Gap analysis

2020-08-28T08:29:47Z

Christopherbs: /* Results for SMILES and ShareWind */

<accesscontrol>Administrators,consortium</accesscontrol>

This is the page for the gap analysis.

= Tacit knowledge "FAIRification and opening of low carbon energy research data" =
 Consortium members can add any time, they feel that something is important to note even though it is not mentioned in a standard deliverable. In other words, this page collects uncodified, tacit knowledge. It will help us later to compile suggestions and lessons learned. This kind of knowledge is collected two-fold:
#Anytime if someone feels that this should be noted. Please write down: Issue, Date, Author (could also be "anonymous"), the issue described in a few words or maximal lines>
#During the final day of workshops

=== Learning process: ===

*People are hesitant to adopt new IT technologies, this is even the case among researchers heavily relying on data, algorithms and collaborative online software (e.g., R, platforms, online conferencing, HPC, ...). The effort to encourage change is not to underestimate. Reasons are several, notably, lack of time and uncertainty about potential benefit as well as overall risk aversion preferences. EERAdata is using the online software "Only Office" to facilitate collaboration (in particular also during the Covid-19 period).
 '''FAIR and open criteria:'''
*Consortium members have a fair understanding of what FAIR/O is, but there is little knowledge and/or technical experience on how to approach the FAIRification and opening. All, however, share the view that we are at a critical point in time, where we need to implement these criteria. 
*To deepen knowledge about FAIR/O criteria, it is useful to test the criteria on a database one is familiar with. For this purpose (and to start brainstorming about the platform), AIT has developed a questionnaire for application in the use cases.
'''Metadata:'''
*A good starting point is to think about metadata and to look into existing metadata concepts in one's field. The first step is to understand that also metadata need to adhere to the FAIR/O principles.
*The next step is to increase knowledge on IT specific terms, i.e. to understand what the difference is between different '''[https://energydata.onlyoffice.eu/Products/Files/doceditor.aspx?fileid=5404340 metadata frameworks] (taxonomy, thesaurus, ontology) as well as classification of metadata (high-level, medium-level, low-level OR administrative, structural and descriptive metadata).  '''WP 2 being in charge of aligning approaches between use cases, participates in all use case kickoffs to bring everybody on the same page. The presentation is linked with "metadata frameworks".
*It is useful to supply consortium members with read aheads and watch aheads on metadata to prepare the '''[https://energydata.onlyoffice.eu/Products/Files/doceditor.aspx?fileid=5296590 first EERAdata workshop]'''. The workshop starts applications and discussions in the use cases break out sessions (and bringing insights back to the plenary), using selected databases.

= FAIR Assessment=

== FAIR Guiding Principles ==
{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ This table shows the FAIR guiding principles described by [https://www.doi.org/10.1038/sdata.2016.18 Wilkinson et al., 2016].

! To be Findable
|
* F1. (meta)data are assigned a globally unique and persistent identifier
* F2. data are described with rich metadata (defined by R1 below)
* F3. metadata clearly and explicitly include the identifier of the data it describes
* F4. (meta)data are registered or indexed in a searchable resource

|-
! To be Accessible
|
* A1. (meta)data are retrievable by their identifier using a standardized communications protocol
** A1.1 the protocol is open, free, and universally implementable
** A1.2 the protocol allows for an authentication and authorization procedure, where necessary
* A2. metadata are accessible, even when the data are no longer available

|-
! To be Interoperable
|
* I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
* I2. (meta)data use vocabularies that follow FAIR principles
* I3. (meta)data include qualified references to other (meta)data

|-
! To be Reusable
|
* R1. meta(data) are richly described with a plurality of accurate and relevant attributes
* R1.1. (meta)data are released with a clear and accessible data usage license
* R1.2. (meta)data are associated with detailed provenance
* R1.3. (meta)data meet domain-relevant community standards

|}

== Identified gaps after Workshop 1 (02/06/2020 – 04/06/2020) ==

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ This table shows a summary of the specific issues that were identified in Workshop 1. The aim was to categorise different issues to get a better understanding of how to tackle these challenges.
! FAIR/O !! Specific FAIR/O !! Gaps !! Gaps for energy domain !! Use case specific gaps !! Consequences for researchers
|-
! F
| F2
|| Metadata scope
|| Different fields require different metadata (potentially very specific)
|| UC3: lack of additional metadata for applications of materials
|| Data are less useful for the specific field
|-
! F
|| F1/F3
|| Identifiers
||
* Missing identifiers on websites
* Identifier not in downloaded data files
||
|| Makes research more difficult.
|-
! I
| I2
|| Taxonomy/ontology/common vocabulary and language issues
||
* Lack of standardisation
* Heterogeneous data makes standardisation hard
* No vocabulary documentation on websites
* Words used for same term in other languages may differ
* Databases in other languages than English
||
|| Research costs more time
|-
! I
| I3
|| References to other (meta)data
||
* No linking to source documents and related publications (for contextual knowledge)
* Possible need for links to other fields
|| UC3: link between microscopic and macroscopic materials (e.g., turbine blades)
||
* Data cannot be connected to the source
* Makes research more difficult.

|-
! R
| R1.1
|| Licensing
||
* No licenses available may mean the data is not reusable for the researcher
* Licenses not clear and accessible
* Obtaining licenses may result in more effort and costs
||
|| Data potentially not reusable

|-
! R
| R1.2
|| Reliability and provenance of data
|| When the data is collected from different and high number of sources, its reliability decreases
||
|| Data potentially not reusable

|-
! O
|
|| Privacy concerns and expected disadvantages
|| Not publishing data due to privacy concerns (sensitive data)
|| UC2: In case of distribution network data this is relevant
|| Data not accessible

|-
! Other
|
|| Conducting FAIR assessments
||
* No qualitative assessment of the data itself (which may be limited)
* Discrepancies between results conducted by humans versus machines
* Sometimes not even DC standards are met
* Metadata is not updated
* Lacking encryption of websites (https)
* Problems assessing whether metadata will be available after data is unavailable
* Interface design/layout may be unclear, incomplete or not intuitive for humans
* Authentication details (user registration login / good or bad, clarify)

||
* UC1 lack of data availability for time-series
||
* Assessment is uncertain (human assessment may need more clarification and understanding)
* Makes research more time-intensive

|}

== FAIR Self-Assessment tool ==

To conduct a human FAIR evaluation for different databases and their respective datasets, different free online tools were considered. Some seemed to be quite useful (e.g. [https://satifyd.dans.knaw.nl/ SATISFYD] ) and some were simple self-evaluation PDFs to be printed and filled out.

We decided to chose the [https://ardc.edu.au/resources/working-with-data/fair-data/fair-self-assessment-tool/ self-assessment tool] provided by the Australian Research Data Commons (ARDC). The decision was based manly upon the fact that the tools questions closely matched the FAIR categories specified by [https://www.doi.org/10.1038/sdata.2016.18 Wilkinson et al., 2016] which would make it easily comparable to the existing preferable FAIR conditions and aid with the repeatability of the results, and the half-automated scoring features that show the extent of the "FAIRness" of the analysed sample as a simple bar chart.

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ All options for each question of the ARDCs self-assessment tool
!colspan="4"|Findable
!colspan="3"|Accessible
!colspan="3"|Interoperable
!colspan="3"|Reusable
|-
| F1. (meta)data are assigned a globally unique and persistent identifier
|| F2. data are described with rich metadata (defined by R1 below)
|| F3. metadata clearly and explicitly include the identifier of the data it describes
|| F4. (meta)data are registered or indexed in a searchable resource
|| A1. (meta)data are retrievable by their identifier using a standardized communications protocol
|| A1.1 the protocol is open, free, and universally implementable and A1.2 the protocol allows for an authentication and authorization procedure, where necessary
|| A2. metadata are accessible, even when the data are no longer available
|| I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
|| I2. (meta)data use vocabularies that follow FAIR principles
|| I3. (meta)data include qualified references to other (meta)data
|| R1.1. (meta)data are released with a clear and accessible data usage license
|| R1.2. (meta)data are associated with detailed provenance
|| R1.3. (meta)data meet domain-relevant community standards
|-
! Does the dataset have any identifiers assigned?
! Is the dataset identifier included in all metadata records/files describing the data?
! How is the data described with metadata?
! What type of repository or registry is the metadata record in?
! How accessible is the data?
! Is the data available online without requiring specialised protocols or tools once access has been approved?
! Will the metadata record be available even if the data is no longer available?
! What (file) format(s) is the data available in?
! What best describes the types of vocabularies/ontologies/tagging schemas used to define the data elements?
! How is the metadata linked to other data and metadata (to enhance context and clearly indicate relationships)?
! Which of the following best describes the license/usage rights attached to the data?
! How much provenance information has been captured to facilitate data reuse?
!
|-
| Globally unique, citable and persistant (e.g. DOI, PURL, ARK or Handle)
|| Yes
|| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
|| Data is in one place but discoverable through several registries
|| Publicly accessible
|| Standard web service API (e.g. OGC)
|| Yes
|| In a structured, open standard, machine-readable format
|| Standardised open and universal using resolvable global identifiers linking to explainations
|| Meadata is represented in a machine readable format, e.g. in a linked data format such as Resource Description Framework (RDF)
|| Standard machine-readable license (e.g. Creative Commons)
|| Fully recorded in a machine-readable format
||
|-
| Web adress (URL)
|| No
|| Comprehensively, but in a text-based, non-standard format
|| Generalist public repository
|| Fully accessible to person who meet explicitly stated conditions, e.g. ethics approval for sensitive data
|| Non-standard web service (e.g. OpenAPI/Swagger/informal API)
|| No
|| In a structured, open standard, non-machine-readable format
|| Standardised vocabularies/ontologies/schema without global identifiers
|| The metadata record includes URI links to related metadata, data and definitions
|| Standard text based license
|| Fully recorded in a text format
||
|-
| Local identifier
||
|| Brief title and description
|| Domain-specific repository
|| A de-identified / modified subset of the data is publicly accessible
|| File download from online location
|| Unsure
|| Mostly in a proprietary format
|| No standards have been applied in the description of data elements
|| There are no links to other metadata
|| Non-standard machine-readably license (clearly indicating under what conditions the data may be reused)
|| Partially recorded
||
|-
| No identifier
||
|| The data is not described
|| Local institutional repository
|| Embaged access after a specific date
|| By individual arrangement
||
||
|| Data elements not described
||
|| Non-standard text-based license
|| No provenance information is recorded
||
|-
|
||
||
|| The data is not described in any repository
|| Unspecified conditional access e.g. contact the data custodian for access
|| No access to data
||
||
||
||
|| No license
||
||
|-
|
||
||
||
|| Access to metadata only
||
||
||
||
||
||
||
||
|-
|
||
||
||
|| No access to data or metadata
||
||
||
||
||
||
||
||
|}

At this point it is important to note that the assessment of databases and/or datasets using this tool is solely considered a human assessment. This may lead to different evaluations by different users. To counteract this phenomenon, it was suggested that the evaluations should be completed using the 'four-eyes principle' to create more verifiable results. Also, it is planned to additionally evaluate the respective databases using an external tool to perform a machine-assessment.

In addition, not all questions are weighted equally and thus a worse result for one question may not have a large impact whereas only choosing the second-best option in another question will bring down the overall FAIR value considerably.

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ The ARDC tool provides detailed information about the requirements for each of the options of its tool. These descriptions are documented here.
|-
! rowspan="5" | F
|| F
|| About Findable
|| Making data Findable includes assigning a persistent identifier (like a DOI or Handle ), having rich metadata to describe the data and making sure it is findable through disciplinary and generalist discovery portals (local and international).

|-
| F1
|| About identifiers
|| Identifiers are essential for identifying, finding, retrieving, linking and citing datasets. A Web address (URL) can be used to specify the online location of a resource but over time URLs tend to change which leads to broken links to the data. To be useful, identifiers need to be persistent and unique. Digital Object Identifiers (DOIs) and other persistent identifiers (PIDs) provide a permanent citable reference to a particular dataset. The DOI is a permanent fixed reference to the dataset no matter where it is located online and enables citation and citation metrics. Services to create a persistent identifier are often offered by your affiliated institution or the repository you are using to describe your data. Talk to your library service for more information.
|-
| F2
|| About identifiers
|| The identifier (preferably a persistent identifier) needs to be clearly stated in the metadata record describing the data collection, and also in any associated data files or metadata.

|-
|F3
|| About metadata descriptions
|| Comprehensive metadata records will include descriptive content that facilitates discovery, access and reuse of the data being described. While there is no 'one size fits all' list, comprehensive metadata should include:
* a globally unique persistent identifier e.g. a DOI
* a title
* related people, i.e. the data creator or custodian
* how to access the data and file formats
* a description of how the data were created and how to interpret the data subject or keywords
* citation information that clearly indicates how the data should be cited
* a machine-readable data licence
* provenance and contextual information such as:
* links to related publications, projects, services and software
* methodology and processes involved in data production
* spatial and temporal coverage (if relevant)
* object-level data description

Providing metadata in a standard schema allows it to be read and used by machines as well as humans.

|-
| F4
|| About metadata repositories
|| A rich metadata description alone does not ensure a dataset’s ‘findability’ on the internet; the dataset needs to be registered or indexed in a searchable resource, such as a generalist (e.g. Figshare, Dryad, Zenodo domain-specific (e.g. PANGAEA, ADA, ICPSR), or institutional (e.g. ANU Research Repository , Sydney eScholarship Repository, data.gov.au) data repository or registry. Ideally these repositories/registries are indexed by search engines such as Google and/or Google Scholar.

|-
! rowspan = "4" | A
| A
|| About Accessible
|| To make data accessible may include making the data open using a standardised protocol. However the data does not necessarily have to be open. There are sometimes good reasons why data cannot be made open, for example privacy concerns, national security or commercial interests. If it is not open there should be clarity and transparency around the conditions governing access and reuse
|-
| A1
|| About data accessibility
|| Not all data that is discoverable can be freely accessed. Often there are embargoes, access controls, and access permissions associated with data due to a variety of issues such as privacy and commercial interests. Even with all these issues much sensitive data can be shared. Many other issues that could be perceived as blockers to sharing data may be overcome.
|-
| A1.1/A1.2
|| About protocols
|| Ideally users would like to retrieve appropriate internet content directly and unhindered once they have located it. Internet protocols (e.g. http and ftp) define rules and conventions for communication between devices, and tools and services are available to facilitate this process, e.g. APIs.

Protocols:
* HTTP (Hypertext Transfer Protocol) is the set of rules for transferring files (text, graphic images, sound, video, and other multimedia files) on the World Wide Web. As soon as a Web user opens their Web browser, the user is indirectly making use of HTTP.
* FTP (File Transfer Protocol) is a standard Internet protocol for transmitting files between computers on the Internet over TCP/IP connections. Read more: Web protocols
* API (Application Programming Interface): When information is made available in any machine readable format, it becomes possible to make that information directly available to programs that request that information over the web. An API is the way this information is made directly available to other machines.

|-
| A2
|| About metadata availability
|| Effort is often required to maintain data resources online which can often lead to it being neglected especially over long periods of time. This often leads to broken links between the metadata and the data. Having at least a description of the data in the form of a metadata record means there is a record of the data's existence allowing the possibility of someone tracking it down.

|-
! rowspan="4" | I
|| I
|| About Interoperability
|| To be interoperable (i.e. data that is interpretable by a computer, so that they can be automatically combined with other data) the data will need to use community agreed formats, language and vocabularies. The metadata will also need to use a community agreed standards and vocabularies, and contain links to related information using identifiers.
|-
|| I1
|| About file formats
|| When selecting file formats for archiving, the formats should ideally be:
* Non-proprietary
* Unencrypted
* Uncompressed
* In common usage by the research community
* Adherent to an open, documented standard, such as described by the State of California (see AB 1668, 2007)
* Interoperable among diverse platforms and applications
* Fully published and available royalty-free
* Fully and independently implementable by multiple software providers on multiple platforms without any intellectual property restrictions for necessary technology
* Developed and maintained by an open standards organization with a well-defined inclusive process for evolution of the standard.

From: Stanford University Library - Best practices for file formats
File format examples:
* Structured, open standard, machine-readable format e.g. (text) PDF/A, HTML, Plain text, (images) TIFF, JPEG 2000, GIF, (audio) MP3, AIFF, WAVE, (video) MOV, MPEG, AVI, (Tabular data) CSV
* structured, open standard, non-machine-readable format, e.g. PDF, HTML, JPG
* Proprietary format, e.g. doc (Word), .xls (Excel), .ppt (PowerPoint), .sav

Read more:
* ANDS
* European Data Portal

|-
|| I2
|| About schema standards
|| Schema standards are schemas that have gone through a formal validation process by a standards organisation, such as the International Standards Organisation (ISO) or an equivalent body such as the Dublin Core Metadata Initiative (DCMI); or, commonly used and consistently applied metadata schemas that are well documented, endorsed, and maintained can also become ‘de-facto standards’, e.g. RIF-CS for describing data collections and services used in Research Data Australia.

Read more: [https://www.ands.org.au/guides/metadata-working Metadata schema]

Standardised open and universal schemas, e.g, [https://www.dcc.ac.uk/resources/metadata-standards/datacite-metadata-schema DataCite Metadata Schema], [https://www.dcc.ac.uk/resources/metadata-standards/prov PROV], [https://www.dcc.ac.uk/resources/metadata-standards/dublin-core Dublin Core]

Domain-specific standards, e.g. [http://human-phenotype-ontology.github.io/ HPO - Human Phenotype Ontology], [https://www.nlm.nih.gov/mesh/ MeSH - Medical Subject Headings] , [https://mcp-profile-docs.readthedocs.io/en/stable/ Marine Community Profile], [https://ddialliance.org/ DDI - Data Documentation Initiative].

For more examples of standards: [http://rd-alliance.github.io/metadata-directory/ Here].

Resolvable global identifiers - An identifier is any label used to name something uniquely (whether online or offline). URLs are an example of an identifier. So are serial numbers, and personal names. A persistent identifier is guaranteed to be managed and kept up to date over a defined time period.

Read more: [https://www.ands.org.au/guides/persistent-identifiers-awareness Persistent Identifiers]

|-
||I3
|| About metadata links
|| The goal is to create as many meaningful linkages as possible between (meta)data resources to enrich the contextual knowledge about the data, balanced against the time/energy involved in making a good data model.

'''Machine-readable (meta)data'''

(Meta)data in a format that can be automatically read and processed by a computer, such as [http://opendatahandbook.org/glossary/en/terms/csv/ CSV], [http://opendatahandbook.org/glossary/en/terms/json/ JSON], [http://opendatahandbook.org/glossary/en/terms/xml/ XML] etc. For more information: [http://opendatahandbook.org/glossary/en/terms/machine-readable/ Open Data Handbook]

'''Linked Data'''

Linked Data (also known as Linking Data) can be applied to improve the exploitation of the “Web of data.” The expression refers to the publishing of structured data in a way that typed links are created between data from different sources to provide a higher level of usability. By using Linked Data, it is possible to find other, related data. Structured data should meet four requirements to be called
Linked Data:

* URIs should be assigned to all entities of the dataset.
* HTTP URIs are required to ensure that all entities can be referenced and cited by users and user agents.
* Entities should be described using standard formats such as RDF/XML.
* Links should be created to other, related entity URIs.

All data that fulfil these requirements and are released for the public are called Linked Open Data (LOD). http://www.lesliesikos.com/semantic-web-machine-readable-structured-data-with-meaningful-annotations/

Read more under the heading (meta)data include qualified references to other (meta)data in the following resource: https://www.dtls.nl/fair-data/fair-principles-explained/

For more information on Linked Data standards - RDF - https://www.w3.org/RDF/

Other related linked data standards:
* OWL - https://www.w3.org/OWL/
* SKOS - https://www.w3.org/2004/02/skos/
* SPARQL - https://www.w3.org/TR/rdf-sparql-query/

|-
! rowspan="3" | R
|| R
|| About Reusable
|| Reusable data should maintain its initial richness. For example, it should not be abridged for the purpose of explaining the findings in one particular publication. It needs a clear machine-readable [https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse licence] and [https://www.ands.org.au/partners-and-communities/ands-communities/data-provenance-interest-group provenance] information on how the data was formed. It should also use discipline-specific data and [https://www.ands.org.au/working-with-data/metadata metadata] standards to give it rich contextual information that will allow for accurate interpretation and reuse.

|-
|| R1.1
|| About licenses
|| If data is not licensed no-one else can use it. In Australia, no licence is regarded as the same as 'all rights reserved', confining any reuse to very limited circumstances. Applying a Creative Commons licence to your data is a simple way to ensure that your data can be reused. The less restrictive the licence, the more that can be done with the data.
To make it easy for the Web to know when a work is available under a particular license, a “machine readable” version of the license provides a summary of the key freedoms and obligations written into a format that software systems, search engines, and other kinds of technology can understand.
Example from the [https://creativecommons.org/choose/ Creative Commons License Choser]:

What appears in the metadata:

This work is licensed under a [https://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International License].

Code snippet:

<code>
<a rel="license" href="http://creativecommons.org/licenses/by/4.0/">

<img alt="Creative Commons License" style="border-width:0"

src="https://i.creativecommons.org/l/by/4.0/88x31.png"/>

</a> This work is licensed under a

<a rel="license" href="http://creativecommons.org/licenses/by/4.0/">

Creative Commons Attribution 4.0 International License</a>.
<\code>

Read more: https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse

|-
||R1.2
|| About provenance
|| Data provenance is used to document where a piece of data comes from and the process and methodology by which it is produced. It is important to confirm the authenticity of data enabling trust, credibility and reproducibility. This is becoming increasingly important, especially in the eScience community where research is data intensive and often involves complex data transformations and procedures ([https://www.ands.org.au/partners-and-communities/ands-communities/data-provenance-interest-group Read more]). Provenance vocabularies such as [https://www.w3.org/2005/Incubator/prov/XGR-prov-20101214/#Recommendations Provenir Ontology (PROV-O) and others].

|}

== Results for SMILES and ShareWind ==

{| class="mw-collapsible wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Datasets from the SMILES and ShareWind platforms was analysed using the self-assessment tool provided by ARDC.
! rowspan="5" |
| rowspan="5" |
! Database Code
| rowspan="24" style="background: #ABE" |
| ED4
| ED9
|-
! Database Title
|| SMILES
| ShareWind
|-
! Database Link
|| https://www.ecria-smiles.eu/
| https://sharewind.eu/
|-
! Sample Link
|| [https://www.ecria-smiles.eu/data-files/-/document_library/W32cCAfL2jMX/view_file664466?_com_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX_redirect=https%3A%2F%2Fwww.ecria-smiles.eu%3A443%2Fdata-files%2F-%2Fdocument_library%2FW32cCAfL2jMX%2Fview%2F664423%3F_com_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX_redirect%3Dhttps%253A%252F%252Fwww.ecria-smiles.eu%253A443%252Fdata-files%253Fp_p_id%253Dcom_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX%2526p_p_lifecycle%253D0%2526p_p_state%253Dnormal%2526p_p_mode%253Dview here] (link has 585 characters)
| https://sharewind.eu/records/f9d31abbc2824284b8c1d28894d9619a (data found at https://zenodo.org/record/1162738)
|-
! Sample Size
|| 4 KB
| 37 MB
|-
| colspan="6" style="background: #ABE" |
|-
! rowspan="4" | Findable
| F1. (meta)data are assigned a globally unique and persistent identifier
! Does the dataset have any identifiers assigned?
|| Web adress (URL)
| Globally unique, citable and persistant (e.g. DOI, PURL, ARK or Handle)
|-

| F2. data are described with rich metadata (defined by R1 below)
! Is the dataset identifier included in all metadata records/files describing the data?
|| No
| No
|-

| F3. metadata clearly and explicitly include the identifier of the data it describes
! How is the data described with metadata?
|| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
|-

| F4. (meta)data are registered or indexed in a searchable resource
! What type of repository or registry is the metadata record in?
|| Domain-specific repository
| Data is in one place but discoverable through several registries
|-

! rowspan="3" | Accessible

| A1. (meta)data are retrievable by their identifier using a standardized communications protocol
! How accessible is the data?
|| Publicly accessible
| Publicly accessible
|-
| A1.1 the protocol is open, free, and universally implementable; A1.2 the protocol allows for an authentication and authorization procedure, where necessary
! Is the data available online without requiring specialised protocols or tools once access has been approved?
|| File download from online location
| Standard web service API (e.g. OGC)
|-
| A2. metadata are accessible, even when the data are no longer available
! Will the metadata record be available even if the data is no longer available?
|| Unsure
| Yes
|-

! rowspan="3" | Interoperable
| I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
! What (file) format(s) is the data available in?
|| In a structured, open standard, machine-readable format
|| In a structured, open standard, non-machine-readable format
|-
| I2. (meta)data use vocabularies that follow FAIR principles
! What best describes the types of vocabularies/ontologies/tagging schemas used to define the data elements?
|| Standardised vocabularies/ontologies/schema without global identifiers
|| Standardised vocabularies/ontologies/schema without global identifiers
|-
| I3. (meta)data include qualified references to other (meta)data
! How is the metadata linked to other data and metadata (to enhance context and clearly indicate relationships)?
|| There are no links to other metadata
|| Metadata is represented in a machine readable format, e.g. in a linked data format such as Resource Description Framework (RDF)
|-

! rowspan="3" | Reusable
| R1.1. (meta)data are released with a clear and accessible data usage license
! Which of the following best describes the license/usage rights attached to the data?
|| No license
| Standard machine-readable license (e.g. Creative Commons)
|-
| R1.2. (meta)data are associated with detailed provenance
! How much provenance information has been captured to facilitate data reuse?
|| Partially recorded
|| Partially recorded
|-
| R1.3. (meta)data meet domain-relevant community standards
|
|
|
|-
| colspan="6" style="background: #ABE" |
|-
! Total across F.A.I.R
|
! Low: < 40%; Medium: 50-60%; High: > 60%
|| 45 %
| 85 %
|-
| colspan="6" style="background: #ABE" |
|-
! Remarks about database
||
||
||
|| The data is provided by the open-source platform Zenodo, not the project website itself. A very positive aspect is that information on different versions of the data as well as the number of views and downloads is available.
|-
! Identified gaps
||
||
||
* No DOI, only URL
* Extremely long URL link spanning several lines with 585 characters, that can easily become corrupted
* No links to other metadata
* No license information given
* Provenance not fully recorded

||
* Dataset identifiers should be included in the files describing the data
* Provenance details are only partially recorded with a note with contact name for further information
|}

= Dublin Core Evaluation =

== Dublin Core Elements ==
{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ The DCMI Metadata Terms lists the current set of the Dublin Core vocabulary. Source: [https://dublincore.org/specifications/dublin-core/dcmi-terms/ Dublin Core Metadata Initiative]
| 1 || abstract || 11 || contributor || 21 || extent || 31 || isReplacedBy || 41 || publisher || 51 || tableOfContents
|-
| 2 || accessRights || 12 || coverage || 22 || format || 32 || isRequiredBy || 42 || references || 52 || temporal
|-
| 3 || accrualMethod || 13 || created || 23 || hasFormat || 33 || issued || 43 || relation || 53 || title
|-
| 4 || accrualPeriodicity || 14 || creator || 24 || hasPart || 34 || isVersionOf || 44 || replaces || 54 || type
|-
| 5 || accrualPolicy || 15 || date || 25 || hasVersion || 35 || language || 45 || requires || 55 || valid
|-
| 6 || alternative || 16 || dateAccepted || 26 || identifier || 36 || license || 46 || rights || ||
|-
| 7 || audience || 17 || dateCopyrighted || 27 || instructionalMethod || 37 || mediator || 47 || rightsHolder || ||
|-
| 8 || available || 18 || dateSubmitted || 28 || isFormatOf || 38 || medium || 48 || source || ||
|-
| 9 || bibliographicCitation || 19 || description || 29 || isPartOf || 39 || modified || 49 || spatial || ||
|-
| 10 || conformsTo || 20 || educationLevel || 30 || isReferencedBy || 40 || provenance || 50 || subject || ||
|}

'''Metadata Category Types according to [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO]'''

There are three main types of metadata:
* Descriptive metadata describes a resource for purposes such as discovery and identification. It can include elements such as title, abstract, author, and keywords.
* Structural metadata indicates how compound objects are put together, for example, how pages are ordered to form chapters.
* Administrative metadata provides information to help manage a resource, such as when and how it was created, file type and other technical information, and who can access it. There are several subsets of administrative data; two that sometimes are listed as separate metadata types are:
** Rights management meta-data, which deals with intellectual property rights,and
** Preservation metadata, which contains information needed to archive and preserve a resource.
''

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Simple Dublin Core Elements. 15 DC elements with their (shortened) official definitions and examples.
! DC element name !! DC definition ([https://www.dublincore.org/specifications/dublin-core/dces/ DCMI]) !! Example (Energy Domain) !! Category (determined with the aid of [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO])
|-
! Title
| A name given to the resource || Outdoor monitoring of a hybrid micro-CPV solar panel with integrated micro-tracking and diffuse capture || Descriptive
|-
! Subject
| The topic of the resource || concentrator photovoltaics; CPV; rooftop photovoltaics; integrated tracking || Descriptive
|-
! Description
| An account of the resource || Dataset from the outdoor characterization of a B Series module from Insolight at the rooftop of the Instituto de Energía Solar - Universidad Politécnica de Madrid. …. || Descriptive
|-
! Creator
| An entity primarily responsible for making the resource || Stephen Askins; Gaël Nardin || Descriptive
|-
! Publisher
| An entity responsible for making the resource available || Stephen Askins || Descriptive
|-
! Contributor
| An entity responsible for making contributions to the resource || Stephen Askins || Descriptive
|-
! Date
| A point or period of time associated with an event in the lifecycle of the resource || 2019-07-23; Date will be associated with the creation or availability of the resource. || Administrative
|-
! Type
| The nature or genre of the resource || info:eu-repo/semantics/other; dataset || Descriptive
|-
! Format
| The file format, physical medium, or dimensions of the resource || image
|| Structural
|-
! Identifier
| An unambiguous reference to the resource within a given context || https://zenodo.org/record/3346823; 10.5281/zenodo.3346823; oai:zenodo.org:3346823 || Descriptive
|-
! Source
| A related resource from which the described resource is derived || RC607.A26W574 1996 [where "RC607.A26W574 1996" is the call number of the print version of the resource, from which the present version was scanned] || Descriptive
|-
! Language
| A language of the resource || eng || Descriptive
|-
! Relation
| A related resource || info:eu-repo/grantAgreement/EC/H2020/787289/ || Descriptive
|-
! Coverage
| The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant || 1995-1996; Madrid, Spain || Descriptive
|-
! Rights
| Information about rights held in and over the resource || info:eu-repo/semantics/openAccess; https://creativecommons.org/licenses/by/4.0/legalcode || Administrative
|}

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Non-DC elements

! Non-DC element name !! Definition !! Example !! Category
|-
! Date of availability
| Since when is the data available
||
|| Impact/Exploitation

|-
! Number of users
| Is there any information on the number of accesses or users?
||
|| Impact/Exploitation

|-
! Number of scientific publications where data is cited/used
| Is there any information on the number of citations/uses?
||
|| Impact/Exploitation

|-
! Curation activities (is the versioning ongoing)
| Update, maintainance
||
|| Impact/Exploitation
|}

Attempting to put a value on Impact/Exploitation:
* '''ENTSO-E''': Found 172 results in Advanced Search (ALL) in Web of Science ([https://apps.webofknowledge.com/ WoSc]) (16/06/2020). Citations of all these search result add up to 714. This however does not mean, that all search results really explicitly used data from ENTSO-E. Some (for sure, for some were found) only mentioned it saying they adoped workflow etc. from the site.
* '''SMARD''': Found 82 results in Advanced Search (ALL=(SMARD)) in Web of Science ([https://apps.webofknowledge.com/ WoSc]) (16/06/2020). However: SMARD is also an acronym for ''Spinal muscular atrophy with respiratory distress (SMARD)''. This somewhat complicates results and needs some further discussion. All 82 search results accumumlated 2359 citations themselves.

Gap analysis

2020-08-28T08:26:46Z

Christopherbs: /* Results for SMILES and ShareWind */

<accesscontrol>Administrators,consortium</accesscontrol>

This is the page for the gap analysis.

= Tacit knowledge "FAIRification and opening of low carbon energy research data" =
 Consortium members can add any time, they feel that something is important to note even though it is not mentioned in a standard deliverable. In other words, this page collects uncodified, tacit knowledge. It will help us later to compile suggestions and lessons learned. This kind of knowledge is collected two-fold:
#Anytime if someone feels that this should be noted. Please write down: Issue, Date, Author (could also be "anonymous"), the issue described in a few words or maximal lines>
#During the final day of workshops

=== Learning process: ===

*People are hesitant to adopt new IT technologies, this is even the case among researchers heavily relying on data, algorithms and collaborative online software (e.g., R, platforms, online conferencing, HPC, ...). The effort to encourage change is not to underestimate. Reasons are several, notably, lack of time and uncertainty about potential benefit as well as overall risk aversion preferences. EERAdata is using the online software "Only Office" to facilitate collaboration (in particular also during the Covid-19 period).
 '''FAIR and open criteria:'''
*Consortium members have a fair understanding of what FAIR/O is, but there is little knowledge and/or technical experience on how to approach the FAIRification and opening. All, however, share the view that we are at a critical point in time, where we need to implement these criteria. 
*To deepen knowledge about FAIR/O criteria, it is useful to test the criteria on a database one is familiar with. For this purpose (and to start brainstorming about the platform), AIT has developed a questionnaire for application in the use cases.
'''Metadata:'''
*A good starting point is to think about metadata and to look into existing metadata concepts in one's field. The first step is to understand that also metadata need to adhere to the FAIR/O principles.
*The next step is to increase knowledge on IT specific terms, i.e. to understand what the difference is between different '''[https://energydata.onlyoffice.eu/Products/Files/doceditor.aspx?fileid=5404340 metadata frameworks] (taxonomy, thesaurus, ontology) as well as classification of metadata (high-level, medium-level, low-level OR administrative, structural and descriptive metadata).  '''WP 2 being in charge of aligning approaches between use cases, participates in all use case kickoffs to bring everybody on the same page. The presentation is linked with "metadata frameworks".
*It is useful to supply consortium members with read aheads and watch aheads on metadata to prepare the '''[https://energydata.onlyoffice.eu/Products/Files/doceditor.aspx?fileid=5296590 first EERAdata workshop]'''. The workshop starts applications and discussions in the use cases break out sessions (and bringing insights back to the plenary), using selected databases.

= FAIR Assessment=

== FAIR Guiding Principles ==
{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ This table shows the FAIR guiding principles described by [https://www.doi.org/10.1038/sdata.2016.18 Wilkinson et al., 2016].

! To be Findable
|
* F1. (meta)data are assigned a globally unique and persistent identifier
* F2. data are described with rich metadata (defined by R1 below)
* F3. metadata clearly and explicitly include the identifier of the data it describes
* F4. (meta)data are registered or indexed in a searchable resource

|-
! To be Accessible
|
* A1. (meta)data are retrievable by their identifier using a standardized communications protocol
** A1.1 the protocol is open, free, and universally implementable
** A1.2 the protocol allows for an authentication and authorization procedure, where necessary
* A2. metadata are accessible, even when the data are no longer available

|-
! To be Interoperable
|
* I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
* I2. (meta)data use vocabularies that follow FAIR principles
* I3. (meta)data include qualified references to other (meta)data

|-
! To be Reusable
|
* R1. meta(data) are richly described with a plurality of accurate and relevant attributes
* R1.1. (meta)data are released with a clear and accessible data usage license
* R1.2. (meta)data are associated with detailed provenance
* R1.3. (meta)data meet domain-relevant community standards

|}

== Identified gaps after Workshop 1 (02/06/2020 – 04/06/2020) ==

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ This table shows a summary of the specific issues that were identified in Workshop 1. The aim was to categorise different issues to get a better understanding of how to tackle these challenges.
! FAIR/O !! Specific FAIR/O !! Gaps !! Gaps for energy domain !! Use case specific gaps !! Consequences for researchers
|-
! F
| F2
|| Metadata scope
|| Different fields require different metadata (potentially very specific)
|| UC3: lack of additional metadata for applications of materials
|| Data are less useful for the specific field
|-
! F
|| F1/F3
|| Identifiers
||
* Missing identifiers on websites
* Identifier not in downloaded data files
||
|| Makes research more difficult.
|-
! I
| I2
|| Taxonomy/ontology/common vocabulary and language issues
||
* Lack of standardisation
* Heterogeneous data makes standardisation hard
* No vocabulary documentation on websites
* Words used for same term in other languages may differ
* Databases in other languages than English
||
|| Research costs more time
|-
! I
| I3
|| References to other (meta)data
||
* No linking to source documents and related publications (for contextual knowledge)
* Possible need for links to other fields
|| UC3: link between microscopic and macroscopic materials (e.g., turbine blades)
||
* Data cannot be connected to the source
* Makes research more difficult.

|-
! R
| R1.1
|| Licensing
||
* No licenses available may mean the data is not reusable for the researcher
* Licenses not clear and accessible
* Obtaining licenses may result in more effort and costs
||
|| Data potentially not reusable

|-
! R
| R1.2
|| Reliability and provenance of data
|| When the data is collected from different and high number of sources, its reliability decreases
||
|| Data potentially not reusable

|-
! O
|
|| Privacy concerns and expected disadvantages
|| Not publishing data due to privacy concerns (sensitive data)
|| UC2: In case of distribution network data this is relevant
|| Data not accessible

|-
! Other
|
|| Conducting FAIR assessments
||
* No qualitative assessment of the data itself (which may be limited)
* Discrepancies between results conducted by humans versus machines
* Sometimes not even DC standards are met
* Metadata is not updated
* Lacking encryption of websites (https)
* Problems assessing whether metadata will be available after data is unavailable
* Interface design/layout may be unclear, incomplete or not intuitive for humans
* Authentication details (user registration login / good or bad, clarify)

||
* UC1 lack of data availability for time-series
||
* Assessment is uncertain (human assessment may need more clarification and understanding)
* Makes research more time-intensive

|}

== FAIR Self-Assessment tool ==

To conduct a human FAIR evaluation for different databases and their respective datasets, different free online tools were considered. Some seemed to be quite useful (e.g. [https://satifyd.dans.knaw.nl/ SATISFYD] ) and some were simple self-evaluation PDFs to be printed and filled out.

We decided to chose the [https://ardc.edu.au/resources/working-with-data/fair-data/fair-self-assessment-tool/ self-assessment tool] provided by the Australian Research Data Commons (ARDC). The decision was based manly upon the fact that the tools questions closely matched the FAIR categories specified by [https://www.doi.org/10.1038/sdata.2016.18 Wilkinson et al., 2016] which would make it easily comparable to the existing preferable FAIR conditions and aid with the repeatability of the results, and the half-automated scoring features that show the extent of the "FAIRness" of the analysed sample as a simple bar chart.

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ All options for each question of the ARDCs self-assessment tool
!colspan="4"|Findable
!colspan="3"|Accessible
!colspan="3"|Interoperable
!colspan="3"|Reusable
|-
| F1. (meta)data are assigned a globally unique and persistent identifier
|| F2. data are described with rich metadata (defined by R1 below)
|| F3. metadata clearly and explicitly include the identifier of the data it describes
|| F4. (meta)data are registered or indexed in a searchable resource
|| A1. (meta)data are retrievable by their identifier using a standardized communications protocol
|| A1.1 the protocol is open, free, and universally implementable and A1.2 the protocol allows for an authentication and authorization procedure, where necessary
|| A2. metadata are accessible, even when the data are no longer available
|| I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
|| I2. (meta)data use vocabularies that follow FAIR principles
|| I3. (meta)data include qualified references to other (meta)data
|| R1.1. (meta)data are released with a clear and accessible data usage license
|| R1.2. (meta)data are associated with detailed provenance
|| R1.3. (meta)data meet domain-relevant community standards
|-
! Does the dataset have any identifiers assigned?
! Is the dataset identifier included in all metadata records/files describing the data?
! How is the data described with metadata?
! What type of repository or registry is the metadata record in?
! How accessible is the data?
! Is the data available online without requiring specialised protocols or tools once access has been approved?
! Will the metadata record be available even if the data is no longer available?
! What (file) format(s) is the data available in?
! What best describes the types of vocabularies/ontologies/tagging schemas used to define the data elements?
! How is the metadata linked to other data and metadata (to enhance context and clearly indicate relationships)?
! Which of the following best describes the license/usage rights attached to the data?
! How much provenance information has been captured to facilitate data reuse?
!
|-
| Globally unique, citable and persistant (e.g. DOI, PURL, ARK or Handle)
|| Yes
|| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
|| Data is in one place but discoverable through several registries
|| Publicly accessible
|| Standard web service API (e.g. OGC)
|| Yes
|| In a structured, open standard, machine-readable format
|| Standardised open and universal using resolvable global identifiers linking to explainations
|| Meadata is represented in a machine readable format, e.g. in a linked data format such as Resource Description Framework (RDF)
|| Standard machine-readable license (e.g. Creative Commons)
|| Fully recorded in a machine-readable format
||
|-
| Web adress (URL)
|| No
|| Comprehensively, but in a text-based, non-standard format
|| Generalist public repository
|| Fully accessible to person who meet explicitly stated conditions, e.g. ethics approval for sensitive data
|| Non-standard web service (e.g. OpenAPI/Swagger/informal API)
|| No
|| In a structured, open standard, non-machine-readable format
|| Standardised vocabularies/ontologies/schema without global identifiers
|| The metadata record includes URI links to related metadata, data and definitions
|| Standard text based license
|| Fully recorded in a text format
||
|-
| Local identifier
||
|| Brief title and description
|| Domain-specific repository
|| A de-identified / modified subset of the data is publicly accessible
|| File download from online location
|| Unsure
|| Mostly in a proprietary format
|| No standards have been applied in the description of data elements
|| There are no links to other metadata
|| Non-standard machine-readably license (clearly indicating under what conditions the data may be reused)
|| Partially recorded
||
|-
| No identifier
||
|| The data is not described
|| Local institutional repository
|| Embaged access after a specific date
|| By individual arrangement
||
||
|| Data elements not described
||
|| Non-standard text-based license
|| No provenance information is recorded
||
|-
|
||
||
|| The data is not described in any repository
|| Unspecified conditional access e.g. contact the data custodian for access
|| No access to data
||
||
||
||
|| No license
||
||
|-
|
||
||
||
|| Access to metadata only
||
||
||
||
||
||
||
||
|-
|
||
||
||
|| No access to data or metadata
||
||
||
||
||
||
||
||
|}

At this point it is important to note that the assessment of databases and/or datasets using this tool is solely considered a human assessment. This may lead to different evaluations by different users. To counteract this phenomenon, it was suggested that the evaluations should be completed using the 'four-eyes principle' to create more verifiable results. Also, it is planned to additionally evaluate the respective databases using an external tool to perform a machine-assessment.

In addition, not all questions are weighted equally and thus a worse result for one question may not have a large impact whereas only choosing the second-best option in another question will bring down the overall FAIR value considerably.

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ The ARDC tool provides detailed information about the requirements for each of the options of its tool. These descriptions are documented here.
|-
! rowspan="5" | F
|| F
|| About Findable
|| Making data Findable includes assigning a persistent identifier (like a DOI or Handle ), having rich metadata to describe the data and making sure it is findable through disciplinary and generalist discovery portals (local and international).

|-
| F1
|| About identifiers
|| Identifiers are essential for identifying, finding, retrieving, linking and citing datasets. A Web address (URL) can be used to specify the online location of a resource but over time URLs tend to change which leads to broken links to the data. To be useful, identifiers need to be persistent and unique. Digital Object Identifiers (DOIs) and other persistent identifiers (PIDs) provide a permanent citable reference to a particular dataset. The DOI is a permanent fixed reference to the dataset no matter where it is located online and enables citation and citation metrics. Services to create a persistent identifier are often offered by your affiliated institution or the repository you are using to describe your data. Talk to your library service for more information.
|-
| F2
|| About identifiers
|| The identifier (preferably a persistent identifier) needs to be clearly stated in the metadata record describing the data collection, and also in any associated data files or metadata.

|-
|F3
|| About metadata descriptions
|| Comprehensive metadata records will include descriptive content that facilitates discovery, access and reuse of the data being described. While there is no 'one size fits all' list, comprehensive metadata should include:
* a globally unique persistent identifier e.g. a DOI
* a title
* related people, i.e. the data creator or custodian
* how to access the data and file formats
* a description of how the data were created and how to interpret the data subject or keywords
* citation information that clearly indicates how the data should be cited
* a machine-readable data licence
* provenance and contextual information such as:
* links to related publications, projects, services and software
* methodology and processes involved in data production
* spatial and temporal coverage (if relevant)
* object-level data description

Providing metadata in a standard schema allows it to be read and used by machines as well as humans.

|-
| F4
|| About metadata repositories
|| A rich metadata description alone does not ensure a dataset’s ‘findability’ on the internet; the dataset needs to be registered or indexed in a searchable resource, such as a generalist (e.g. Figshare, Dryad, Zenodo domain-specific (e.g. PANGAEA, ADA, ICPSR), or institutional (e.g. ANU Research Repository , Sydney eScholarship Repository, data.gov.au) data repository or registry. Ideally these repositories/registries are indexed by search engines such as Google and/or Google Scholar.

|-
! rowspan = "4" | A
| A
|| About Accessible
|| To make data accessible may include making the data open using a standardised protocol. However the data does not necessarily have to be open. There are sometimes good reasons why data cannot be made open, for example privacy concerns, national security or commercial interests. If it is not open there should be clarity and transparency around the conditions governing access and reuse
|-
| A1
|| About data accessibility
|| Not all data that is discoverable can be freely accessed. Often there are embargoes, access controls, and access permissions associated with data due to a variety of issues such as privacy and commercial interests. Even with all these issues much sensitive data can be shared. Many other issues that could be perceived as blockers to sharing data may be overcome.
|-
| A1.1/A1.2
|| About protocols
|| Ideally users would like to retrieve appropriate internet content directly and unhindered once they have located it. Internet protocols (e.g. http and ftp) define rules and conventions for communication between devices, and tools and services are available to facilitate this process, e.g. APIs.

Protocols:
* HTTP (Hypertext Transfer Protocol) is the set of rules for transferring files (text, graphic images, sound, video, and other multimedia files) on the World Wide Web. As soon as a Web user opens their Web browser, the user is indirectly making use of HTTP.
* FTP (File Transfer Protocol) is a standard Internet protocol for transmitting files between computers on the Internet over TCP/IP connections. Read more: Web protocols
* API (Application Programming Interface): When information is made available in any machine readable format, it becomes possible to make that information directly available to programs that request that information over the web. An API is the way this information is made directly available to other machines.

|-
| A2
|| About metadata availability
|| Effort is often required to maintain data resources online which can often lead to it being neglected especially over long periods of time. This often leads to broken links between the metadata and the data. Having at least a description of the data in the form of a metadata record means there is a record of the data's existence allowing the possibility of someone tracking it down.

|-
! rowspan="4" | I
|| I
|| About Interoperability
|| To be interoperable (i.e. data that is interpretable by a computer, so that they can be automatically combined with other data) the data will need to use community agreed formats, language and vocabularies. The metadata will also need to use a community agreed standards and vocabularies, and contain links to related information using identifiers.
|-
|| I1
|| About file formats
|| When selecting file formats for archiving, the formats should ideally be:
* Non-proprietary
* Unencrypted
* Uncompressed
* In common usage by the research community
* Adherent to an open, documented standard, such as described by the State of California (see AB 1668, 2007)
* Interoperable among diverse platforms and applications
* Fully published and available royalty-free
* Fully and independently implementable by multiple software providers on multiple platforms without any intellectual property restrictions for necessary technology
* Developed and maintained by an open standards organization with a well-defined inclusive process for evolution of the standard.

From: Stanford University Library - Best practices for file formats
File format examples:
* Structured, open standard, machine-readable format e.g. (text) PDF/A, HTML, Plain text, (images) TIFF, JPEG 2000, GIF, (audio) MP3, AIFF, WAVE, (video) MOV, MPEG, AVI, (Tabular data) CSV
* structured, open standard, non-machine-readable format, e.g. PDF, HTML, JPG
* Proprietary format, e.g. doc (Word), .xls (Excel), .ppt (PowerPoint), .sav

Read more:
* ANDS
* European Data Portal

|-
|| I2
|| About schema standards
|| Schema standards are schemas that have gone through a formal validation process by a standards organisation, such as the International Standards Organisation (ISO) or an equivalent body such as the Dublin Core Metadata Initiative (DCMI); or, commonly used and consistently applied metadata schemas that are well documented, endorsed, and maintained can also become ‘de-facto standards’, e.g. RIF-CS for describing data collections and services used in Research Data Australia.

Read more: [https://www.ands.org.au/guides/metadata-working Metadata schema]

Standardised open and universal schemas, e.g, [https://www.dcc.ac.uk/resources/metadata-standards/datacite-metadata-schema DataCite Metadata Schema], [https://www.dcc.ac.uk/resources/metadata-standards/prov PROV], [https://www.dcc.ac.uk/resources/metadata-standards/dublin-core Dublin Core]

Domain-specific standards, e.g. [http://human-phenotype-ontology.github.io/ HPO - Human Phenotype Ontology], [https://www.nlm.nih.gov/mesh/ MeSH - Medical Subject Headings] , [https://mcp-profile-docs.readthedocs.io/en/stable/ Marine Community Profile], [https://ddialliance.org/ DDI - Data Documentation Initiative].

For more examples of standards: [http://rd-alliance.github.io/metadata-directory/ Here].

Resolvable global identifiers - An identifier is any label used to name something uniquely (whether online or offline). URLs are an example of an identifier. So are serial numbers, and personal names. A persistent identifier is guaranteed to be managed and kept up to date over a defined time period.

Read more: [https://www.ands.org.au/guides/persistent-identifiers-awareness Persistent Identifiers]

|-
||I3
|| About metadata links
|| The goal is to create as many meaningful linkages as possible between (meta)data resources to enrich the contextual knowledge about the data, balanced against the time/energy involved in making a good data model.

'''Machine-readable (meta)data'''

(Meta)data in a format that can be automatically read and processed by a computer, such as [http://opendatahandbook.org/glossary/en/terms/csv/ CSV], [http://opendatahandbook.org/glossary/en/terms/json/ JSON], [http://opendatahandbook.org/glossary/en/terms/xml/ XML] etc. For more information: [http://opendatahandbook.org/glossary/en/terms/machine-readable/ Open Data Handbook]

'''Linked Data'''

Linked Data (also known as Linking Data) can be applied to improve the exploitation of the “Web of data.” The expression refers to the publishing of structured data in a way that typed links are created between data from different sources to provide a higher level of usability. By using Linked Data, it is possible to find other, related data. Structured data should meet four requirements to be called
Linked Data:

* URIs should be assigned to all entities of the dataset.
* HTTP URIs are required to ensure that all entities can be referenced and cited by users and user agents.
* Entities should be described using standard formats such as RDF/XML.
* Links should be created to other, related entity URIs.

All data that fulfil these requirements and are released for the public are called Linked Open Data (LOD). http://www.lesliesikos.com/semantic-web-machine-readable-structured-data-with-meaningful-annotations/

Read more under the heading (meta)data include qualified references to other (meta)data in the following resource: https://www.dtls.nl/fair-data/fair-principles-explained/

For more information on Linked Data standards - RDF - https://www.w3.org/RDF/

Other related linked data standards:
* OWL - https://www.w3.org/OWL/
* SKOS - https://www.w3.org/2004/02/skos/
* SPARQL - https://www.w3.org/TR/rdf-sparql-query/

|-
! rowspan="3" | R
|| R
|| About Reusable
|| Reusable data should maintain its initial richness. For example, it should not be abridged for the purpose of explaining the findings in one particular publication. It needs a clear machine-readable [https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse licence] and [https://www.ands.org.au/partners-and-communities/ands-communities/data-provenance-interest-group provenance] information on how the data was formed. It should also use discipline-specific data and [https://www.ands.org.au/working-with-data/metadata metadata] standards to give it rich contextual information that will allow for accurate interpretation and reuse.

|-
|| R1.1
|| About licenses
|| If data is not licensed no-one else can use it. In Australia, no licence is regarded as the same as 'all rights reserved', confining any reuse to very limited circumstances. Applying a Creative Commons licence to your data is a simple way to ensure that your data can be reused. The less restrictive the licence, the more that can be done with the data.
To make it easy for the Web to know when a work is available under a particular license, a “machine readable” version of the license provides a summary of the key freedoms and obligations written into a format that software systems, search engines, and other kinds of technology can understand.
Example from the [https://creativecommons.org/choose/ Creative Commons License Choser]:

What appears in the metadata:

This work is licensed under a [https://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International License].

Code snippet:

<code>
<a rel="license" href="http://creativecommons.org/licenses/by/4.0/">

<img alt="Creative Commons License" style="border-width:0"

src="https://i.creativecommons.org/l/by/4.0/88x31.png"/>

</a> This work is licensed under a

<a rel="license" href="http://creativecommons.org/licenses/by/4.0/">

Creative Commons Attribution 4.0 International License</a>.
<\code>

Read more: https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse

|-
||R1.2
|| About provenance
|| Data provenance is used to document where a piece of data comes from and the process and methodology by which it is produced. It is important to confirm the authenticity of data enabling trust, credibility and reproducibility. This is becoming increasingly important, especially in the eScience community where research is data intensive and often involves complex data transformations and procedures ([https://www.ands.org.au/partners-and-communities/ands-communities/data-provenance-interest-group Read more]). Provenance vocabularies such as [https://www.w3.org/2005/Incubator/prov/XGR-prov-20101214/#Recommendations Provenir Ontology (PROV-O) and others].

|}

== Results for SMILES and ShareWind ==

{| class="mw-collapsible wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Datasets from the SMILES and ShareWind platforms was analysed using the self-assessment tool provided by ARDC.
! rowspan="5" |
| rowspan="5" |
! Database Code
| rowspan="24" style="background: #ABE" |
| ED4
| ED9
|-
! Database Title
|| SMILES
| ShareWind
|-
! Database Link
|| https://www.ecria-smiles.eu/
| https://sharewind.eu/
|-
! Sample Link
|| [https://www.ecria-smiles.eu/data-files/-/document_library/W32cCAfL2jMX/view_file664466?_com_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX_redirect=https%3A%2F%2Fwww.ecria-smiles.eu%3A443%2Fdata-files%2F-%2Fdocument_library%2FW32cCAfL2jMX%2Fview%2F664423%3F_com_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX_redirect%3Dhttps%253A%252F%252Fwww.ecria-smiles.eu%253A443%252Fdata-files%253Fp_p_id%253Dcom_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX%2526p_p_lifecycle%253D0%2526p_p_state%253Dnormal%2526p_p_mode%253Dview here] (link has 585 characters)
| https://sharewind.eu/records/f9d31abbc2824284b8c1d28894d9619a; https://zenodo.org/record/1162738
|-
! Sample Size
|| 4 KB
| 37 MB
|-
| colspan="6" style="background: #ABE" |
|-
! rowspan="4" | Findable
| F1. (meta)data are assigned a globally unique and persistent identifier
! Does the dataset have any identifiers assigned?
|| Web adress (URL)
| Globally unique, citable and persistant (e.g. DOI, PURL, ARK or Handle)
|-

| F2. data are described with rich metadata (defined by R1 below)
! Is the dataset identifier included in all metadata records/files describing the data?
|| No
| No
|-

| F3. metadata clearly and explicitly include the identifier of the data it describes
! How is the data described with metadata?
|| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
|-

| F4. (meta)data are registered or indexed in a searchable resource
! What type of repository or registry is the metadata record in?
|| Domain-specific repository
| Data is in one place but discoverable through several registries
|-

! rowspan="3" | Accessible

| A1. (meta)data are retrievable by their identifier using a standardized communications protocol
! How accessible is the data?
|| Publicly accessible
| Publicly accessible
|-
| A1.1 the protocol is open, free, and universally implementable; A1.2 the protocol allows for an authentication and authorization procedure, where necessary
! Is the data available online without requiring specialised protocols or tools once access has been approved?
|| File download from online location
| Standard web service API (e.g. OGC)
|-
| A2. metadata are accessible, even when the data are no longer available
! Will the metadata record be available even if the data is no longer available?
|| Unsure
| Yes
|-

! rowspan="3" | Interoperable
| I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
! What (file) format(s) is the data available in?
|| In a structured, open standard, machine-readable format
|| In a structured, open standard, non-machine-readable format
|-
| I2. (meta)data use vocabularies that follow FAIR principles
! What best describes the types of vocabularies/ontologies/tagging schemas used to define the data elements?
|| Standardised vocabularies/ontologies/schema without global identifiers
|| Standardised vocabularies/ontologies/schema without global identifiers
|-
| I3. (meta)data include qualified references to other (meta)data
! How is the metadata linked to other data and metadata (to enhance context and clearly indicate relationships)?
|| There are no links to other metadata
|| Metadata is represented in a machine readable format, e.g. in a linked data format such as Resource Description Framework (RDF)
|-

! rowspan="3" | Reusable
| R1.1. (meta)data are released with a clear and accessible data usage license
! Which of the following best describes the license/usage rights attached to the data?
|| No license
| Standard machine-readable license (e.g. Creative Commons)
|-
| R1.2. (meta)data are associated with detailed provenance
! How much provenance information has been captured to facilitate data reuse?
|| Partially recorded
|| Partially recorded
|-
| R1.3. (meta)data meet domain-relevant community standards
|
|
|
|-
| colspan="6" style="background: #ABE" |
|-
! Total across F.A.I.R
|
! Low: < 40%; Medium: 50-60%; High: > 60%
|| 45 %
| 85 %
|-
| colspan="6" style="background: #ABE" |
|-
! Remarks about database
||
||
||
|| The data is provided by the open-source platform Zenodo, not the project website itself. A very positive aspect is that information on different versions of the data as well as the number of views and downloads is available.
|-
! Identified gaps
||
||
||
* No DOI, only URL
* Extremely long URL link spanning several lines with 585 characters, that can easily become corrupted
* No links to other metadata
* No license information given
* Provenance not fully recorded

||
* Dataset identifiers should be included in the files describing the data
* Provenance details are only partially recorded with a note with contact name for further information
|}

= Dublin Core Evaluation =

== Dublin Core Elements ==
{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ The DCMI Metadata Terms lists the current set of the Dublin Core vocabulary. Source: [https://dublincore.org/specifications/dublin-core/dcmi-terms/ Dublin Core Metadata Initiative]
| 1 || abstract || 11 || contributor || 21 || extent || 31 || isReplacedBy || 41 || publisher || 51 || tableOfContents
|-
| 2 || accessRights || 12 || coverage || 22 || format || 32 || isRequiredBy || 42 || references || 52 || temporal
|-
| 3 || accrualMethod || 13 || created || 23 || hasFormat || 33 || issued || 43 || relation || 53 || title
|-
| 4 || accrualPeriodicity || 14 || creator || 24 || hasPart || 34 || isVersionOf || 44 || replaces || 54 || type
|-
| 5 || accrualPolicy || 15 || date || 25 || hasVersion || 35 || language || 45 || requires || 55 || valid
|-
| 6 || alternative || 16 || dateAccepted || 26 || identifier || 36 || license || 46 || rights || ||
|-
| 7 || audience || 17 || dateCopyrighted || 27 || instructionalMethod || 37 || mediator || 47 || rightsHolder || ||
|-
| 8 || available || 18 || dateSubmitted || 28 || isFormatOf || 38 || medium || 48 || source || ||
|-
| 9 || bibliographicCitation || 19 || description || 29 || isPartOf || 39 || modified || 49 || spatial || ||
|-
| 10 || conformsTo || 20 || educationLevel || 30 || isReferencedBy || 40 || provenance || 50 || subject || ||
|}

'''Metadata Category Types according to [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO]'''

There are three main types of metadata:
* Descriptive metadata describes a resource for purposes such as discovery and identification. It can include elements such as title, abstract, author, and keywords.
* Structural metadata indicates how compound objects are put together, for example, how pages are ordered to form chapters.
* Administrative metadata provides information to help manage a resource, such as when and how it was created, file type and other technical information, and who can access it. There are several subsets of administrative data; two that sometimes are listed as separate metadata types are:
** Rights management meta-data, which deals with intellectual property rights,and
** Preservation metadata, which contains information needed to archive and preserve a resource.
''

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Simple Dublin Core Elements. 15 DC elements with their (shortened) official definitions and examples.
! DC element name !! DC definition ([https://www.dublincore.org/specifications/dublin-core/dces/ DCMI]) !! Example (Energy Domain) !! Category (determined with the aid of [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO])
|-
! Title
| A name given to the resource || Outdoor monitoring of a hybrid micro-CPV solar panel with integrated micro-tracking and diffuse capture || Descriptive
|-
! Subject
| The topic of the resource || concentrator photovoltaics; CPV; rooftop photovoltaics; integrated tracking || Descriptive
|-
! Description
| An account of the resource || Dataset from the outdoor characterization of a B Series module from Insolight at the rooftop of the Instituto de Energía Solar - Universidad Politécnica de Madrid. …. || Descriptive
|-
! Creator
| An entity primarily responsible for making the resource || Stephen Askins; Gaël Nardin || Descriptive
|-
! Publisher
| An entity responsible for making the resource available || Stephen Askins || Descriptive
|-
! Contributor
| An entity responsible for making contributions to the resource || Stephen Askins || Descriptive
|-
! Date
| A point or period of time associated with an event in the lifecycle of the resource || 2019-07-23; Date will be associated with the creation or availability of the resource. || Administrative
|-
! Type
| The nature or genre of the resource || info:eu-repo/semantics/other; dataset || Descriptive
|-
! Format
| The file format, physical medium, or dimensions of the resource || image
|| Structural
|-
! Identifier
| An unambiguous reference to the resource within a given context || https://zenodo.org/record/3346823; 10.5281/zenodo.3346823; oai:zenodo.org:3346823 || Descriptive
|-
! Source
| A related resource from which the described resource is derived || RC607.A26W574 1996 [where "RC607.A26W574 1996" is the call number of the print version of the resource, from which the present version was scanned] || Descriptive
|-
! Language
| A language of the resource || eng || Descriptive
|-
! Relation
| A related resource || info:eu-repo/grantAgreement/EC/H2020/787289/ || Descriptive
|-
! Coverage
| The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant || 1995-1996; Madrid, Spain || Descriptive
|-
! Rights
| Information about rights held in and over the resource || info:eu-repo/semantics/openAccess; https://creativecommons.org/licenses/by/4.0/legalcode || Administrative
|}

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Non-DC elements

! Non-DC element name !! Definition !! Example !! Category
|-
! Date of availability
| Since when is the data available
||
|| Impact/Exploitation

|-
! Number of users
| Is there any information on the number of accesses or users?
||
|| Impact/Exploitation

|-
! Number of scientific publications where data is cited/used
| Is there any information on the number of citations/uses?
||
|| Impact/Exploitation

|-
! Curation activities (is the versioning ongoing)
| Update, maintainance
||
|| Impact/Exploitation
|}

Attempting to put a value on Impact/Exploitation:
* '''ENTSO-E''': Found 172 results in Advanced Search (ALL) in Web of Science ([https://apps.webofknowledge.com/ WoSc]) (16/06/2020). Citations of all these search result add up to 714. This however does not mean, that all search results really explicitly used data from ENTSO-E. Some (for sure, for some were found) only mentioned it saying they adoped workflow etc. from the site.
* '''SMARD''': Found 82 results in Advanced Search (ALL=(SMARD)) in Web of Science ([https://apps.webofknowledge.com/ WoSc]) (16/06/2020). However: SMARD is also an acronym for ''Spinal muscular atrophy with respiratory distress (SMARD)''. This somewhat complicates results and needs some further discussion. All 82 search results accumumlated 2359 citations themselves.

Gap analysis

2020-08-28T08:25:56Z

Christopherbs: /* Results for SMILES and ShareWind */

<accesscontrol>Administrators,consortium</accesscontrol>

This is the page for the gap analysis.

= Tacit knowledge "FAIRification and opening of low carbon energy research data" =
 Consortium members can add any time, they feel that something is important to note even though it is not mentioned in a standard deliverable. In other words, this page collects uncodified, tacit knowledge. It will help us later to compile suggestions and lessons learned. This kind of knowledge is collected two-fold:
#Anytime if someone feels that this should be noted. Please write down: Issue, Date, Author (could also be "anonymous"), the issue described in a few words or maximal lines>
#During the final day of workshops

=== Learning process: ===

*People are hesitant to adopt new IT technologies, this is even the case among researchers heavily relying on data, algorithms and collaborative online software (e.g., R, platforms, online conferencing, HPC, ...). The effort to encourage change is not to underestimate. Reasons are several, notably, lack of time and uncertainty about potential benefit as well as overall risk aversion preferences. EERAdata is using the online software "Only Office" to facilitate collaboration (in particular also during the Covid-19 period).
 '''FAIR and open criteria:'''
*Consortium members have a fair understanding of what FAIR/O is, but there is little knowledge and/or technical experience on how to approach the FAIRification and opening. All, however, share the view that we are at a critical point in time, where we need to implement these criteria. 
*To deepen knowledge about FAIR/O criteria, it is useful to test the criteria on a database one is familiar with. For this purpose (and to start brainstorming about the platform), AIT has developed a questionnaire for application in the use cases.
'''Metadata:'''
*A good starting point is to think about metadata and to look into existing metadata concepts in one's field. The first step is to understand that also metadata need to adhere to the FAIR/O principles.
*The next step is to increase knowledge on IT specific terms, i.e. to understand what the difference is between different '''[https://energydata.onlyoffice.eu/Products/Files/doceditor.aspx?fileid=5404340 metadata frameworks] (taxonomy, thesaurus, ontology) as well as classification of metadata (high-level, medium-level, low-level OR administrative, structural and descriptive metadata).  '''WP 2 being in charge of aligning approaches between use cases, participates in all use case kickoffs to bring everybody on the same page. The presentation is linked with "metadata frameworks".
*It is useful to supply consortium members with read aheads and watch aheads on metadata to prepare the '''[https://energydata.onlyoffice.eu/Products/Files/doceditor.aspx?fileid=5296590 first EERAdata workshop]'''. The workshop starts applications and discussions in the use cases break out sessions (and bringing insights back to the plenary), using selected databases.

= FAIR Assessment=

== FAIR Guiding Principles ==
{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ This table shows the FAIR guiding principles described by [https://www.doi.org/10.1038/sdata.2016.18 Wilkinson et al., 2016].

! To be Findable
|
* F1. (meta)data are assigned a globally unique and persistent identifier
* F2. data are described with rich metadata (defined by R1 below)
* F3. metadata clearly and explicitly include the identifier of the data it describes
* F4. (meta)data are registered or indexed in a searchable resource

|-
! To be Accessible
|
* A1. (meta)data are retrievable by their identifier using a standardized communications protocol
** A1.1 the protocol is open, free, and universally implementable
** A1.2 the protocol allows for an authentication and authorization procedure, where necessary
* A2. metadata are accessible, even when the data are no longer available

|-
! To be Interoperable
|
* I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
* I2. (meta)data use vocabularies that follow FAIR principles
* I3. (meta)data include qualified references to other (meta)data

|-
! To be Reusable
|
* R1. meta(data) are richly described with a plurality of accurate and relevant attributes
* R1.1. (meta)data are released with a clear and accessible data usage license
* R1.2. (meta)data are associated with detailed provenance
* R1.3. (meta)data meet domain-relevant community standards

|}

== Identified gaps after Workshop 1 (02/06/2020 – 04/06/2020) ==

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ This table shows a summary of the specific issues that were identified in Workshop 1. The aim was to categorise different issues to get a better understanding of how to tackle these challenges.
! FAIR/O !! Specific FAIR/O !! Gaps !! Gaps for energy domain !! Use case specific gaps !! Consequences for researchers
|-
! F
| F2
|| Metadata scope
|| Different fields require different metadata (potentially very specific)
|| UC3: lack of additional metadata for applications of materials
|| Data are less useful for the specific field
|-
! F
|| F1/F3
|| Identifiers
||
* Missing identifiers on websites
* Identifier not in downloaded data files
||
|| Makes research more difficult.
|-
! I
| I2
|| Taxonomy/ontology/common vocabulary and language issues
||
* Lack of standardisation
* Heterogeneous data makes standardisation hard
* No vocabulary documentation on websites
* Words used for same term in other languages may differ
* Databases in other languages than English
||
|| Research costs more time
|-
! I
| I3
|| References to other (meta)data
||
* No linking to source documents and related publications (for contextual knowledge)
* Possible need for links to other fields
|| UC3: link between microscopic and macroscopic materials (e.g., turbine blades)
||
* Data cannot be connected to the source
* Makes research more difficult.

|-
! R
| R1.1
|| Licensing
||
* No licenses available may mean the data is not reusable for the researcher
* Licenses not clear and accessible
* Obtaining licenses may result in more effort and costs
||
|| Data potentially not reusable

|-
! R
| R1.2
|| Reliability and provenance of data
|| When the data is collected from different and high number of sources, its reliability decreases
||
|| Data potentially not reusable

|-
! O
|
|| Privacy concerns and expected disadvantages
|| Not publishing data due to privacy concerns (sensitive data)
|| UC2: In case of distribution network data this is relevant
|| Data not accessible

|-
! Other
|
|| Conducting FAIR assessments
||
* No qualitative assessment of the data itself (which may be limited)
* Discrepancies between results conducted by humans versus machines
* Sometimes not even DC standards are met
* Metadata is not updated
* Lacking encryption of websites (https)
* Problems assessing whether metadata will be available after data is unavailable
* Interface design/layout may be unclear, incomplete or not intuitive for humans
* Authentication details (user registration login / good or bad, clarify)

||
* UC1 lack of data availability for time-series
||
* Assessment is uncertain (human assessment may need more clarification and understanding)
* Makes research more time-intensive

|}

== FAIR Self-Assessment tool ==

To conduct a human FAIR evaluation for different databases and their respective datasets, different free online tools were considered. Some seemed to be quite useful (e.g. [https://satifyd.dans.knaw.nl/ SATISFYD] ) and some were simple self-evaluation PDFs to be printed and filled out.

We decided to chose the [https://ardc.edu.au/resources/working-with-data/fair-data/fair-self-assessment-tool/ self-assessment tool] provided by the Australian Research Data Commons (ARDC). The decision was based manly upon the fact that the tools questions closely matched the FAIR categories specified by [https://www.doi.org/10.1038/sdata.2016.18 Wilkinson et al., 2016] which would make it easily comparable to the existing preferable FAIR conditions and aid with the repeatability of the results, and the half-automated scoring features that show the extent of the "FAIRness" of the analysed sample as a simple bar chart.

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ All options for each question of the ARDCs self-assessment tool
!colspan="4"|Findable
!colspan="3"|Accessible
!colspan="3"|Interoperable
!colspan="3"|Reusable
|-
| F1. (meta)data are assigned a globally unique and persistent identifier
|| F2. data are described with rich metadata (defined by R1 below)
|| F3. metadata clearly and explicitly include the identifier of the data it describes
|| F4. (meta)data are registered or indexed in a searchable resource
|| A1. (meta)data are retrievable by their identifier using a standardized communications protocol
|| A1.1 the protocol is open, free, and universally implementable and A1.2 the protocol allows for an authentication and authorization procedure, where necessary
|| A2. metadata are accessible, even when the data are no longer available
|| I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
|| I2. (meta)data use vocabularies that follow FAIR principles
|| I3. (meta)data include qualified references to other (meta)data
|| R1.1. (meta)data are released with a clear and accessible data usage license
|| R1.2. (meta)data are associated with detailed provenance
|| R1.3. (meta)data meet domain-relevant community standards
|-
! Does the dataset have any identifiers assigned?
! Is the dataset identifier included in all metadata records/files describing the data?
! How is the data described with metadata?
! What type of repository or registry is the metadata record in?
! How accessible is the data?
! Is the data available online without requiring specialised protocols or tools once access has been approved?
! Will the metadata record be available even if the data is no longer available?
! What (file) format(s) is the data available in?
! What best describes the types of vocabularies/ontologies/tagging schemas used to define the data elements?
! How is the metadata linked to other data and metadata (to enhance context and clearly indicate relationships)?
! Which of the following best describes the license/usage rights attached to the data?
! How much provenance information has been captured to facilitate data reuse?
!
|-
| Globally unique, citable and persistant (e.g. DOI, PURL, ARK or Handle)
|| Yes
|| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
|| Data is in one place but discoverable through several registries
|| Publicly accessible
|| Standard web service API (e.g. OGC)
|| Yes
|| In a structured, open standard, machine-readable format
|| Standardised open and universal using resolvable global identifiers linking to explainations
|| Meadata is represented in a machine readable format, e.g. in a linked data format such as Resource Description Framework (RDF)
|| Standard machine-readable license (e.g. Creative Commons)
|| Fully recorded in a machine-readable format
||
|-
| Web adress (URL)
|| No
|| Comprehensively, but in a text-based, non-standard format
|| Generalist public repository
|| Fully accessible to person who meet explicitly stated conditions, e.g. ethics approval for sensitive data
|| Non-standard web service (e.g. OpenAPI/Swagger/informal API)
|| No
|| In a structured, open standard, non-machine-readable format
|| Standardised vocabularies/ontologies/schema without global identifiers
|| The metadata record includes URI links to related metadata, data and definitions
|| Standard text based license
|| Fully recorded in a text format
||
|-
| Local identifier
||
|| Brief title and description
|| Domain-specific repository
|| A de-identified / modified subset of the data is publicly accessible
|| File download from online location
|| Unsure
|| Mostly in a proprietary format
|| No standards have been applied in the description of data elements
|| There are no links to other metadata
|| Non-standard machine-readably license (clearly indicating under what conditions the data may be reused)
|| Partially recorded
||
|-
| No identifier
||
|| The data is not described
|| Local institutional repository
|| Embaged access after a specific date
|| By individual arrangement
||
||
|| Data elements not described
||
|| Non-standard text-based license
|| No provenance information is recorded
||
|-
|
||
||
|| The data is not described in any repository
|| Unspecified conditional access e.g. contact the data custodian for access
|| No access to data
||
||
||
||
|| No license
||
||
|-
|
||
||
||
|| Access to metadata only
||
||
||
||
||
||
||
||
|-
|
||
||
||
|| No access to data or metadata
||
||
||
||
||
||
||
||
|}

At this point it is important to note that the assessment of databases and/or datasets using this tool is solely considered a human assessment. This may lead to different evaluations by different users. To counteract this phenomenon, it was suggested that the evaluations should be completed using the 'four-eyes principle' to create more verifiable results. Also, it is planned to additionally evaluate the respective databases using an external tool to perform a machine-assessment.

In addition, not all questions are weighted equally and thus a worse result for one question may not have a large impact whereas only choosing the second-best option in another question will bring down the overall FAIR value considerably.

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ The ARDC tool provides detailed information about the requirements for each of the options of its tool. These descriptions are documented here.
|-
! rowspan="5" | F
|| F
|| About Findable
|| Making data Findable includes assigning a persistent identifier (like a DOI or Handle ), having rich metadata to describe the data and making sure it is findable through disciplinary and generalist discovery portals (local and international).

|-
| F1
|| About identifiers
|| Identifiers are essential for identifying, finding, retrieving, linking and citing datasets. A Web address (URL) can be used to specify the online location of a resource but over time URLs tend to change which leads to broken links to the data. To be useful, identifiers need to be persistent and unique. Digital Object Identifiers (DOIs) and other persistent identifiers (PIDs) provide a permanent citable reference to a particular dataset. The DOI is a permanent fixed reference to the dataset no matter where it is located online and enables citation and citation metrics. Services to create a persistent identifier are often offered by your affiliated institution or the repository you are using to describe your data. Talk to your library service for more information.
|-
| F2
|| About identifiers
|| The identifier (preferably a persistent identifier) needs to be clearly stated in the metadata record describing the data collection, and also in any associated data files or metadata.

|-
|F3
|| About metadata descriptions
|| Comprehensive metadata records will include descriptive content that facilitates discovery, access and reuse of the data being described. While there is no 'one size fits all' list, comprehensive metadata should include:
* a globally unique persistent identifier e.g. a DOI
* a title
* related people, i.e. the data creator or custodian
* how to access the data and file formats
* a description of how the data were created and how to interpret the data subject or keywords
* citation information that clearly indicates how the data should be cited
* a machine-readable data licence
* provenance and contextual information such as:
* links to related publications, projects, services and software
* methodology and processes involved in data production
* spatial and temporal coverage (if relevant)
* object-level data description

Providing metadata in a standard schema allows it to be read and used by machines as well as humans.

|-
| F4
|| About metadata repositories
|| A rich metadata description alone does not ensure a dataset’s ‘findability’ on the internet; the dataset needs to be registered or indexed in a searchable resource, such as a generalist (e.g. Figshare, Dryad, Zenodo domain-specific (e.g. PANGAEA, ADA, ICPSR), or institutional (e.g. ANU Research Repository , Sydney eScholarship Repository, data.gov.au) data repository or registry. Ideally these repositories/registries are indexed by search engines such as Google and/or Google Scholar.

|-
! rowspan = "4" | A
| A
|| About Accessible
|| To make data accessible may include making the data open using a standardised protocol. However the data does not necessarily have to be open. There are sometimes good reasons why data cannot be made open, for example privacy concerns, national security or commercial interests. If it is not open there should be clarity and transparency around the conditions governing access and reuse
|-
| A1
|| About data accessibility
|| Not all data that is discoverable can be freely accessed. Often there are embargoes, access controls, and access permissions associated with data due to a variety of issues such as privacy and commercial interests. Even with all these issues much sensitive data can be shared. Many other issues that could be perceived as blockers to sharing data may be overcome.
|-
| A1.1/A1.2
|| About protocols
|| Ideally users would like to retrieve appropriate internet content directly and unhindered once they have located it. Internet protocols (e.g. http and ftp) define rules and conventions for communication between devices, and tools and services are available to facilitate this process, e.g. APIs.

Protocols:
* HTTP (Hypertext Transfer Protocol) is the set of rules for transferring files (text, graphic images, sound, video, and other multimedia files) on the World Wide Web. As soon as a Web user opens their Web browser, the user is indirectly making use of HTTP.
* FTP (File Transfer Protocol) is a standard Internet protocol for transmitting files between computers on the Internet over TCP/IP connections. Read more: Web protocols
* API (Application Programming Interface): When information is made available in any machine readable format, it becomes possible to make that information directly available to programs that request that information over the web. An API is the way this information is made directly available to other machines.

|-
| A2
|| About metadata availability
|| Effort is often required to maintain data resources online which can often lead to it being neglected especially over long periods of time. This often leads to broken links between the metadata and the data. Having at least a description of the data in the form of a metadata record means there is a record of the data's existence allowing the possibility of someone tracking it down.

|-
! rowspan="4" | I
|| I
|| About Interoperability
|| To be interoperable (i.e. data that is interpretable by a computer, so that they can be automatically combined with other data) the data will need to use community agreed formats, language and vocabularies. The metadata will also need to use a community agreed standards and vocabularies, and contain links to related information using identifiers.
|-
|| I1
|| About file formats
|| When selecting file formats for archiving, the formats should ideally be:
* Non-proprietary
* Unencrypted
* Uncompressed
* In common usage by the research community
* Adherent to an open, documented standard, such as described by the State of California (see AB 1668, 2007)
* Interoperable among diverse platforms and applications
* Fully published and available royalty-free
* Fully and independently implementable by multiple software providers on multiple platforms without any intellectual property restrictions for necessary technology
* Developed and maintained by an open standards organization with a well-defined inclusive process for evolution of the standard.

From: Stanford University Library - Best practices for file formats
File format examples:
* Structured, open standard, machine-readable format e.g. (text) PDF/A, HTML, Plain text, (images) TIFF, JPEG 2000, GIF, (audio) MP3, AIFF, WAVE, (video) MOV, MPEG, AVI, (Tabular data) CSV
* structured, open standard, non-machine-readable format, e.g. PDF, HTML, JPG
* Proprietary format, e.g. doc (Word), .xls (Excel), .ppt (PowerPoint), .sav

Read more:
* ANDS
* European Data Portal

|-
|| I2
|| About schema standards
|| Schema standards are schemas that have gone through a formal validation process by a standards organisation, such as the International Standards Organisation (ISO) or an equivalent body such as the Dublin Core Metadata Initiative (DCMI); or, commonly used and consistently applied metadata schemas that are well documented, endorsed, and maintained can also become ‘de-facto standards’, e.g. RIF-CS for describing data collections and services used in Research Data Australia.

Read more: [https://www.ands.org.au/guides/metadata-working Metadata schema]

Standardised open and universal schemas, e.g, [https://www.dcc.ac.uk/resources/metadata-standards/datacite-metadata-schema DataCite Metadata Schema], [https://www.dcc.ac.uk/resources/metadata-standards/prov PROV], [https://www.dcc.ac.uk/resources/metadata-standards/dublin-core Dublin Core]

Domain-specific standards, e.g. [http://human-phenotype-ontology.github.io/ HPO - Human Phenotype Ontology], [https://www.nlm.nih.gov/mesh/ MeSH - Medical Subject Headings] , [https://mcp-profile-docs.readthedocs.io/en/stable/ Marine Community Profile], [https://ddialliance.org/ DDI - Data Documentation Initiative].

For more examples of standards: [http://rd-alliance.github.io/metadata-directory/ Here].

Resolvable global identifiers - An identifier is any label used to name something uniquely (whether online or offline). URLs are an example of an identifier. So are serial numbers, and personal names. A persistent identifier is guaranteed to be managed and kept up to date over a defined time period.

Read more: [https://www.ands.org.au/guides/persistent-identifiers-awareness Persistent Identifiers]

|-
||I3
|| About metadata links
|| The goal is to create as many meaningful linkages as possible between (meta)data resources to enrich the contextual knowledge about the data, balanced against the time/energy involved in making a good data model.

'''Machine-readable (meta)data'''

(Meta)data in a format that can be automatically read and processed by a computer, such as [http://opendatahandbook.org/glossary/en/terms/csv/ CSV], [http://opendatahandbook.org/glossary/en/terms/json/ JSON], [http://opendatahandbook.org/glossary/en/terms/xml/ XML] etc. For more information: [http://opendatahandbook.org/glossary/en/terms/machine-readable/ Open Data Handbook]

'''Linked Data'''

Linked Data (also known as Linking Data) can be applied to improve the exploitation of the “Web of data.” The expression refers to the publishing of structured data in a way that typed links are created between data from different sources to provide a higher level of usability. By using Linked Data, it is possible to find other, related data. Structured data should meet four requirements to be called
Linked Data:

* URIs should be assigned to all entities of the dataset.
* HTTP URIs are required to ensure that all entities can be referenced and cited by users and user agents.
* Entities should be described using standard formats such as RDF/XML.
* Links should be created to other, related entity URIs.

All data that fulfil these requirements and are released for the public are called Linked Open Data (LOD). http://www.lesliesikos.com/semantic-web-machine-readable-structured-data-with-meaningful-annotations/

Read more under the heading (meta)data include qualified references to other (meta)data in the following resource: https://www.dtls.nl/fair-data/fair-principles-explained/

For more information on Linked Data standards - RDF - https://www.w3.org/RDF/

Other related linked data standards:
* OWL - https://www.w3.org/OWL/
* SKOS - https://www.w3.org/2004/02/skos/
* SPARQL - https://www.w3.org/TR/rdf-sparql-query/

|-
! rowspan="3" | R
|| R
|| About Reusable
|| Reusable data should maintain its initial richness. For example, it should not be abridged for the purpose of explaining the findings in one particular publication. It needs a clear machine-readable [https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse licence] and [https://www.ands.org.au/partners-and-communities/ands-communities/data-provenance-interest-group provenance] information on how the data was formed. It should also use discipline-specific data and [https://www.ands.org.au/working-with-data/metadata metadata] standards to give it rich contextual information that will allow for accurate interpretation and reuse.

|-
|| R1.1
|| About licenses
|| If data is not licensed no-one else can use it. In Australia, no licence is regarded as the same as 'all rights reserved', confining any reuse to very limited circumstances. Applying a Creative Commons licence to your data is a simple way to ensure that your data can be reused. The less restrictive the licence, the more that can be done with the data.
To make it easy for the Web to know when a work is available under a particular license, a “machine readable” version of the license provides a summary of the key freedoms and obligations written into a format that software systems, search engines, and other kinds of technology can understand.
Example from the [https://creativecommons.org/choose/ Creative Commons License Choser]:

What appears in the metadata:

This work is licensed under a [https://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International License].

Code snippet:

<code>
<a rel="license" href="http://creativecommons.org/licenses/by/4.0/">

<img alt="Creative Commons License" style="border-width:0"

src="https://i.creativecommons.org/l/by/4.0/88x31.png"/>

</a> This work is licensed under a

<a rel="license" href="http://creativecommons.org/licenses/by/4.0/">

Creative Commons Attribution 4.0 International License</a>.
<\code>

Read more: https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse

|-
||R1.2
|| About provenance
|| Data provenance is used to document where a piece of data comes from and the process and methodology by which it is produced. It is important to confirm the authenticity of data enabling trust, credibility and reproducibility. This is becoming increasingly important, especially in the eScience community where research is data intensive and often involves complex data transformations and procedures ([https://www.ands.org.au/partners-and-communities/ands-communities/data-provenance-interest-group Read more]). Provenance vocabularies such as [https://www.w3.org/2005/Incubator/prov/XGR-prov-20101214/#Recommendations Provenir Ontology (PROV-O) and others].

|}

== Results for SMILES and ShareWind ==

{| class="mw-collapsible wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Datasets from the SMILES and ShareWind platforms was analysed using the self-assessment tool provided by ARDC.
! rowspan="5" |
| rowspan="5" |
! Database Code
| rowspan="24" style="background: #ABE" |
| ED4
| ED9
|-
! Database Title
|| SMILES
| ShareWind
|-
! Subdata-Code
|| https://www.ecria-smiles.eu/
| https://sharewind.eu/
|-
! Database/Subdata
|| [https://www.ecria-smiles.eu/data-files/-/document_library/W32cCAfL2jMX/view_file664466?_com_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX_redirect=https%3A%2F%2Fwww.ecria-smiles.eu%3A443%2Fdata-files%2F-%2Fdocument_library%2FW32cCAfL2jMX%2Fview%2F664423%3F_com_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX_redirect%3Dhttps%253A%252F%252Fwww.ecria-smiles.eu%253A443%252Fdata-files%253Fp_p_id%253Dcom_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX%2526p_p_lifecycle%253D0%2526p_p_state%253Dnormal%2526p_p_mode%253Dview here] (link has 585 characters)
| https://sharewind.eu/records/f9d31abbc2824284b8c1d28894d9619a; https://zenodo.org/record/1162738
|-
! Sample Size
|| 4 KB
| 37 MB
|-
| colspan="6" style="background: #ABE" |
|-
! rowspan="4" | Findable
| F1. (meta)data are assigned a globally unique and persistent identifier
! Does the dataset have any identifiers assigned?
|| Web adress (URL)
| Globally unique, citable and persistant (e.g. DOI, PURL, ARK or Handle)
|-

| F2. data are described with rich metadata (defined by R1 below)
! Is the dataset identifier included in all metadata records/files describing the data?
|| No
| No
|-

| F3. metadata clearly and explicitly include the identifier of the data it describes
! How is the data described with metadata?
|| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
|-

| F4. (meta)data are registered or indexed in a searchable resource
! What type of repository or registry is the metadata record in?
|| Domain-specific repository
| Data is in one place but discoverable through several registries
|-

! rowspan="3" | Accessible

| A1. (meta)data are retrievable by their identifier using a standardized communications protocol
! How accessible is the data?
|| Publicly accessible
| Publicly accessible
|-
| A1.1 the protocol is open, free, and universally implementable; A1.2 the protocol allows for an authentication and authorization procedure, where necessary
! Is the data available online without requiring specialised protocols or tools once access has been approved?
|| File download from online location
| Standard web service API (e.g. OGC)
|-
| A2. metadata are accessible, even when the data are no longer available
! Will the metadata record be available even if the data is no longer available?
|| Unsure
| Yes
|-

! rowspan="3" | Interoperable
| I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
! What (file) format(s) is the data available in?
|| In a structured, open standard, machine-readable format
|| In a structured, open standard, non-machine-readable format
|-
| I2. (meta)data use vocabularies that follow FAIR principles
! What best describes the types of vocabularies/ontologies/tagging schemas used to define the data elements?
|| Standardised vocabularies/ontologies/schema without global identifiers
|| Standardised vocabularies/ontologies/schema without global identifiers
|-
| I3. (meta)data include qualified references to other (meta)data
! How is the metadata linked to other data and metadata (to enhance context and clearly indicate relationships)?
|| There are no links to other metadata
|| Metadata is represented in a machine readable format, e.g. in a linked data format such as Resource Description Framework (RDF)
|-

! rowspan="3" | Reusable
| R1.1. (meta)data are released with a clear and accessible data usage license
! Which of the following best describes the license/usage rights attached to the data?
|| No license
| Standard machine-readable license (e.g. Creative Commons)
|-
| R1.2. (meta)data are associated with detailed provenance
! How much provenance information has been captured to facilitate data reuse?
|| Partially recorded
|| Partially recorded
|-
| R1.3. (meta)data meet domain-relevant community standards
|
|
|
|-
| colspan="6" style="background: #ABE" |
|-
! Total across F.A.I.R
|
! Low: < 40%; Medium: 50-60%; High: > 60%
|| 45 %
| 85 %
|-
| colspan="6" style="background: #ABE" |
|-
! Remarks about database
||
||
||
|| The data is provided by the open-source platform Zenodo, not the project website itself. A very positive aspect is that information on different versions of the data as well as the number of views and downloads is available.
|-
! Identified gaps
||
||
||
* No DOI, only URL
* Extremely long URL link spanning several lines with 585 characters, that can easily become corrupted
* No links to other metadata
* No license information given
* Provenance not fully recorded

||
* Dataset identifiers should be included in the files describing the data
* Provenance details are only partially recorded with a note with contact name for further information
|}

= Dublin Core Evaluation =

== Dublin Core Elements ==
{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ The DCMI Metadata Terms lists the current set of the Dublin Core vocabulary. Source: [https://dublincore.org/specifications/dublin-core/dcmi-terms/ Dublin Core Metadata Initiative]
| 1 || abstract || 11 || contributor || 21 || extent || 31 || isReplacedBy || 41 || publisher || 51 || tableOfContents
|-
| 2 || accessRights || 12 || coverage || 22 || format || 32 || isRequiredBy || 42 || references || 52 || temporal
|-
| 3 || accrualMethod || 13 || created || 23 || hasFormat || 33 || issued || 43 || relation || 53 || title
|-
| 4 || accrualPeriodicity || 14 || creator || 24 || hasPart || 34 || isVersionOf || 44 || replaces || 54 || type
|-
| 5 || accrualPolicy || 15 || date || 25 || hasVersion || 35 || language || 45 || requires || 55 || valid
|-
| 6 || alternative || 16 || dateAccepted || 26 || identifier || 36 || license || 46 || rights || ||
|-
| 7 || audience || 17 || dateCopyrighted || 27 || instructionalMethod || 37 || mediator || 47 || rightsHolder || ||
|-
| 8 || available || 18 || dateSubmitted || 28 || isFormatOf || 38 || medium || 48 || source || ||
|-
| 9 || bibliographicCitation || 19 || description || 29 || isPartOf || 39 || modified || 49 || spatial || ||
|-
| 10 || conformsTo || 20 || educationLevel || 30 || isReferencedBy || 40 || provenance || 50 || subject || ||
|}

'''Metadata Category Types according to [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO]'''

There are three main types of metadata:
* Descriptive metadata describes a resource for purposes such as discovery and identification. It can include elements such as title, abstract, author, and keywords.
* Structural metadata indicates how compound objects are put together, for example, how pages are ordered to form chapters.
* Administrative metadata provides information to help manage a resource, such as when and how it was created, file type and other technical information, and who can access it. There are several subsets of administrative data; two that sometimes are listed as separate metadata types are:
** Rights management meta-data, which deals with intellectual property rights,and
** Preservation metadata, which contains information needed to archive and preserve a resource.
''

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Simple Dublin Core Elements. 15 DC elements with their (shortened) official definitions and examples.
! DC element name !! DC definition ([https://www.dublincore.org/specifications/dublin-core/dces/ DCMI]) !! Example (Energy Domain) !! Category (determined with the aid of [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO])
|-
! Title
| A name given to the resource || Outdoor monitoring of a hybrid micro-CPV solar panel with integrated micro-tracking and diffuse capture || Descriptive
|-
! Subject
| The topic of the resource || concentrator photovoltaics; CPV; rooftop photovoltaics; integrated tracking || Descriptive
|-
! Description
| An account of the resource || Dataset from the outdoor characterization of a B Series module from Insolight at the rooftop of the Instituto de Energía Solar - Universidad Politécnica de Madrid. …. || Descriptive
|-
! Creator
| An entity primarily responsible for making the resource || Stephen Askins; Gaël Nardin || Descriptive
|-
! Publisher
| An entity responsible for making the resource available || Stephen Askins || Descriptive
|-
! Contributor
| An entity responsible for making contributions to the resource || Stephen Askins || Descriptive
|-
! Date
| A point or period of time associated with an event in the lifecycle of the resource || 2019-07-23; Date will be associated with the creation or availability of the resource. || Administrative
|-
! Type
| The nature or genre of the resource || info:eu-repo/semantics/other; dataset || Descriptive
|-
! Format
| The file format, physical medium, or dimensions of the resource || image
|| Structural
|-
! Identifier
| An unambiguous reference to the resource within a given context || https://zenodo.org/record/3346823; 10.5281/zenodo.3346823; oai:zenodo.org:3346823 || Descriptive
|-
! Source
| A related resource from which the described resource is derived || RC607.A26W574 1996 [where "RC607.A26W574 1996" is the call number of the print version of the resource, from which the present version was scanned] || Descriptive
|-
! Language
| A language of the resource || eng || Descriptive
|-
! Relation
| A related resource || info:eu-repo/grantAgreement/EC/H2020/787289/ || Descriptive
|-
! Coverage
| The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant || 1995-1996; Madrid, Spain || Descriptive
|-
! Rights
| Information about rights held in and over the resource || info:eu-repo/semantics/openAccess; https://creativecommons.org/licenses/by/4.0/legalcode || Administrative
|}

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Non-DC elements

! Non-DC element name !! Definition !! Example !! Category
|-
! Date of availability
| Since when is the data available
||
|| Impact/Exploitation

|-
! Number of users
| Is there any information on the number of accesses or users?
||
|| Impact/Exploitation

|-
! Number of scientific publications where data is cited/used
| Is there any information on the number of citations/uses?
||
|| Impact/Exploitation

|-
! Curation activities (is the versioning ongoing)
| Update, maintainance
||
|| Impact/Exploitation
|}

Attempting to put a value on Impact/Exploitation:
* '''ENTSO-E''': Found 172 results in Advanced Search (ALL) in Web of Science ([https://apps.webofknowledge.com/ WoSc]) (16/06/2020). Citations of all these search result add up to 714. This however does not mean, that all search results really explicitly used data from ENTSO-E. Some (for sure, for some were found) only mentioned it saying they adoped workflow etc. from the site.
* '''SMARD''': Found 82 results in Advanced Search (ALL=(SMARD)) in Web of Science ([https://apps.webofknowledge.com/ WoSc]) (16/06/2020). However: SMARD is also an acronym for ''Spinal muscular atrophy with respiratory distress (SMARD)''. This somewhat complicates results and needs some further discussion. All 82 search results accumumlated 2359 citations themselves.

Gap analysis

2020-08-28T08:18:26Z

Christopherbs: /* FAIR Evaluation */

<accesscontrol>Administrators,consortium</accesscontrol>

This is the page for the gap analysis.

= Tacit knowledge "FAIRification and opening of low carbon energy research data" =
 Consortium members can add any time, they feel that something is important to note even though it is not mentioned in a standard deliverable. In other words, this page collects uncodified, tacit knowledge. It will help us later to compile suggestions and lessons learned. This kind of knowledge is collected two-fold:
#Anytime if someone feels that this should be noted. Please write down: Issue, Date, Author (could also be "anonymous"), the issue described in a few words or maximal lines>
#During the final day of workshops

=== Learning process: ===

*People are hesitant to adopt new IT technologies, this is even the case among researchers heavily relying on data, algorithms and collaborative online software (e.g., R, platforms, online conferencing, HPC, ...). The effort to encourage change is not to underestimate. Reasons are several, notably, lack of time and uncertainty about potential benefit as well as overall risk aversion preferences. EERAdata is using the online software "Only Office" to facilitate collaboration (in particular also during the Covid-19 period).
 '''FAIR and open criteria:'''
*Consortium members have a fair understanding of what FAIR/O is, but there is little knowledge and/or technical experience on how to approach the FAIRification and opening. All, however, share the view that we are at a critical point in time, where we need to implement these criteria. 
*To deepen knowledge about FAIR/O criteria, it is useful to test the criteria on a database one is familiar with. For this purpose (and to start brainstorming about the platform), AIT has developed a questionnaire for application in the use cases.
'''Metadata:'''
*A good starting point is to think about metadata and to look into existing metadata concepts in one's field. The first step is to understand that also metadata need to adhere to the FAIR/O principles.
*The next step is to increase knowledge on IT specific terms, i.e. to understand what the difference is between different '''[https://energydata.onlyoffice.eu/Products/Files/doceditor.aspx?fileid=5404340 metadata frameworks] (taxonomy, thesaurus, ontology) as well as classification of metadata (high-level, medium-level, low-level OR administrative, structural and descriptive metadata).  '''WP 2 being in charge of aligning approaches between use cases, participates in all use case kickoffs to bring everybody on the same page. The presentation is linked with "metadata frameworks".
*It is useful to supply consortium members with read aheads and watch aheads on metadata to prepare the '''[https://energydata.onlyoffice.eu/Products/Files/doceditor.aspx?fileid=5296590 first EERAdata workshop]'''. The workshop starts applications and discussions in the use cases break out sessions (and bringing insights back to the plenary), using selected databases.

= FAIR Assessment=

== FAIR Guiding Principles ==
{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ This table shows the FAIR guiding principles described by [https://www.doi.org/10.1038/sdata.2016.18 Wilkinson et al., 2016].

! To be Findable
|
* F1. (meta)data are assigned a globally unique and persistent identifier
* F2. data are described with rich metadata (defined by R1 below)
* F3. metadata clearly and explicitly include the identifier of the data it describes
* F4. (meta)data are registered or indexed in a searchable resource

|-
! To be Accessible
|
* A1. (meta)data are retrievable by their identifier using a standardized communications protocol
** A1.1 the protocol is open, free, and universally implementable
** A1.2 the protocol allows for an authentication and authorization procedure, where necessary
* A2. metadata are accessible, even when the data are no longer available

|-
! To be Interoperable
|
* I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
* I2. (meta)data use vocabularies that follow FAIR principles
* I3. (meta)data include qualified references to other (meta)data

|-
! To be Reusable
|
* R1. meta(data) are richly described with a plurality of accurate and relevant attributes
* R1.1. (meta)data are released with a clear and accessible data usage license
* R1.2. (meta)data are associated with detailed provenance
* R1.3. (meta)data meet domain-relevant community standards

|}

== Identified gaps after Workshop 1 (02/06/2020 – 04/06/2020) ==

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ This table shows a summary of the specific issues that were identified in Workshop 1. The aim was to categorise different issues to get a better understanding of how to tackle these challenges.
! FAIR/O !! Specific FAIR/O !! Gaps !! Gaps for energy domain !! Use case specific gaps !! Consequences for researchers
|-
! F
| F2
|| Metadata scope
|| Different fields require different metadata (potentially very specific)
|| UC3: lack of additional metadata for applications of materials
|| Data are less useful for the specific field
|-
! F
|| F1/F3
|| Identifiers
||
* Missing identifiers on websites
* Identifier not in downloaded data files
||
|| Makes research more difficult.
|-
! I
| I2
|| Taxonomy/ontology/common vocabulary and language issues
||
* Lack of standardisation
* Heterogeneous data makes standardisation hard
* No vocabulary documentation on websites
* Words used for same term in other languages may differ
* Databases in other languages than English
||
|| Research costs more time
|-
! I
| I3
|| References to other (meta)data
||
* No linking to source documents and related publications (for contextual knowledge)
* Possible need for links to other fields
|| UC3: link between microscopic and macroscopic materials (e.g., turbine blades)
||
* Data cannot be connected to the source
* Makes research more difficult.

|-
! R
| R1.1
|| Licensing
||
* No licenses available may mean the data is not reusable for the researcher
* Licenses not clear and accessible
* Obtaining licenses may result in more effort and costs
||
|| Data potentially not reusable

|-
! R
| R1.2
|| Reliability and provenance of data
|| When the data is collected from different and high number of sources, its reliability decreases
||
|| Data potentially not reusable

|-
! O
|
|| Privacy concerns and expected disadvantages
|| Not publishing data due to privacy concerns (sensitive data)
|| UC2: In case of distribution network data this is relevant
|| Data not accessible

|-
! Other
|
|| Conducting FAIR assessments
||
* No qualitative assessment of the data itself (which may be limited)
* Discrepancies between results conducted by humans versus machines
* Sometimes not even DC standards are met
* Metadata is not updated
* Lacking encryption of websites (https)
* Problems assessing whether metadata will be available after data is unavailable
* Interface design/layout may be unclear, incomplete or not intuitive for humans
* Authentication details (user registration login / good or bad, clarify)

||
* UC1 lack of data availability for time-series
||
* Assessment is uncertain (human assessment may need more clarification and understanding)
* Makes research more time-intensive

|}

== FAIR Self-Assessment tool ==

To conduct a human FAIR evaluation for different databases and their respective datasets, different free online tools were considered. Some seemed to be quite useful (e.g. [https://satifyd.dans.knaw.nl/ SATISFYD] ) and some were simple self-evaluation PDFs to be printed and filled out.

We decided to chose the [https://ardc.edu.au/resources/working-with-data/fair-data/fair-self-assessment-tool/ self-assessment tool] provided by the Australian Research Data Commons (ARDC). The decision was based manly upon the fact that the tools questions closely matched the FAIR categories specified by [https://www.doi.org/10.1038/sdata.2016.18 Wilkinson et al., 2016] which would make it easily comparable to the existing preferable FAIR conditions and aid with the repeatability of the results, and the half-automated scoring features that show the extent of the "FAIRness" of the analysed sample as a simple bar chart.

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ All options for each question of the ARDCs self-assessment tool
!colspan="4"|Findable
!colspan="3"|Accessible
!colspan="3"|Interoperable
!colspan="3"|Reusable
|-
| F1. (meta)data are assigned a globally unique and persistent identifier
|| F2. data are described with rich metadata (defined by R1 below)
|| F3. metadata clearly and explicitly include the identifier of the data it describes
|| F4. (meta)data are registered or indexed in a searchable resource
|| A1. (meta)data are retrievable by their identifier using a standardized communications protocol
|| A1.1 the protocol is open, free, and universally implementable and A1.2 the protocol allows for an authentication and authorization procedure, where necessary
|| A2. metadata are accessible, even when the data are no longer available
|| I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
|| I2. (meta)data use vocabularies that follow FAIR principles
|| I3. (meta)data include qualified references to other (meta)data
|| R1.1. (meta)data are released with a clear and accessible data usage license
|| R1.2. (meta)data are associated with detailed provenance
|| R1.3. (meta)data meet domain-relevant community standards
|-
! Does the dataset have any identifiers assigned?
! Is the dataset identifier included in all metadata records/files describing the data?
! How is the data described with metadata?
! What type of repository or registry is the metadata record in?
! How accessible is the data?
! Is the data available online without requiring specialised protocols or tools once access has been approved?
! Will the metadata record be available even if the data is no longer available?
! What (file) format(s) is the data available in?
! What best describes the types of vocabularies/ontologies/tagging schemas used to define the data elements?
! How is the metadata linked to other data and metadata (to enhance context and clearly indicate relationships)?
! Which of the following best describes the license/usage rights attached to the data?
! How much provenance information has been captured to facilitate data reuse?
!
|-
| Globally unique, citable and persistant (e.g. DOI, PURL, ARK or Handle)
|| Yes
|| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
|| Data is in one place but discoverable through several registries
|| Publicly accessible
|| Standard web service API (e.g. OGC)
|| Yes
|| In a structured, open standard, machine-readable format
|| Standardised open and universal using resolvable global identifiers linking to explainations
|| Meadata is represented in a machine readable format, e.g. in a linked data format such as Resource Description Framework (RDF)
|| Standard machine-readable license (e.g. Creative Commons)
|| Fully recorded in a machine-readable format
||
|-
| Web adress (URL)
|| No
|| Comprehensively, but in a text-based, non-standard format
|| Generalist public repository
|| Fully accessible to person who meet explicitly stated conditions, e.g. ethics approval for sensitive data
|| Non-standard web service (e.g. OpenAPI/Swagger/informal API)
|| No
|| In a structured, open standard, non-machine-readable format
|| Standardised vocabularies/ontologies/schema without global identifiers
|| The metadata record includes URI links to related metadata, data and definitions
|| Standard text based license
|| Fully recorded in a text format
||
|-
| Local identifier
||
|| Brief title and description
|| Domain-specific repository
|| A de-identified / modified subset of the data is publicly accessible
|| File download from online location
|| Unsure
|| Mostly in a proprietary format
|| No standards have been applied in the description of data elements
|| There are no links to other metadata
|| Non-standard machine-readably license (clearly indicating under what conditions the data may be reused)
|| Partially recorded
||
|-
| No identifier
||
|| The data is not described
|| Local institutional repository
|| Embaged access after a specific date
|| By individual arrangement
||
||
|| Data elements not described
||
|| Non-standard text-based license
|| No provenance information is recorded
||
|-
|
||
||
|| The data is not described in any repository
|| Unspecified conditional access e.g. contact the data custodian for access
|| No access to data
||
||
||
||
|| No license
||
||
|-
|
||
||
||
|| Access to metadata only
||
||
||
||
||
||
||
||
|-
|
||
||
||
|| No access to data or metadata
||
||
||
||
||
||
||
||
|}

At this point it is important to note that the assessment of databases and/or datasets using this tool is solely considered a human assessment. This may lead to different evaluations by different users. To counteract this phenomenon, it was suggested that the evaluations should be completed using the 'four-eyes principle' to create more verifiable results. Also, it is planned to additionally evaluate the respective databases using an external tool to perform a machine-assessment.

In addition, not all questions are weighted equally and thus a worse result for one question may not have a large impact whereas only choosing the second-best option in another question will bring down the overall FAIR value considerably.

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ The ARDC tool provides detailed information about the requirements for each of the options of its tool. These descriptions are documented here.
|-
! rowspan="5" | F
|| F
|| About Findable
|| Making data Findable includes assigning a persistent identifier (like a DOI or Handle ), having rich metadata to describe the data and making sure it is findable through disciplinary and generalist discovery portals (local and international).

|-
| F1
|| About identifiers
|| Identifiers are essential for identifying, finding, retrieving, linking and citing datasets. A Web address (URL) can be used to specify the online location of a resource but over time URLs tend to change which leads to broken links to the data. To be useful, identifiers need to be persistent and unique. Digital Object Identifiers (DOIs) and other persistent identifiers (PIDs) provide a permanent citable reference to a particular dataset. The DOI is a permanent fixed reference to the dataset no matter where it is located online and enables citation and citation metrics. Services to create a persistent identifier are often offered by your affiliated institution or the repository you are using to describe your data. Talk to your library service for more information.
|-
| F2
|| About identifiers
|| The identifier (preferably a persistent identifier) needs to be clearly stated in the metadata record describing the data collection, and also in any associated data files or metadata.

|-
|F3
|| About metadata descriptions
|| Comprehensive metadata records will include descriptive content that facilitates discovery, access and reuse of the data being described. While there is no 'one size fits all' list, comprehensive metadata should include:
* a globally unique persistent identifier e.g. a DOI
* a title
* related people, i.e. the data creator or custodian
* how to access the data and file formats
* a description of how the data were created and how to interpret the data subject or keywords
* citation information that clearly indicates how the data should be cited
* a machine-readable data licence
* provenance and contextual information such as:
* links to related publications, projects, services and software
* methodology and processes involved in data production
* spatial and temporal coverage (if relevant)
* object-level data description

Providing metadata in a standard schema allows it to be read and used by machines as well as humans.

|-
| F4
|| About metadata repositories
|| A rich metadata description alone does not ensure a dataset’s ‘findability’ on the internet; the dataset needs to be registered or indexed in a searchable resource, such as a generalist (e.g. Figshare, Dryad, Zenodo domain-specific (e.g. PANGAEA, ADA, ICPSR), or institutional (e.g. ANU Research Repository , Sydney eScholarship Repository, data.gov.au) data repository or registry. Ideally these repositories/registries are indexed by search engines such as Google and/or Google Scholar.

|-
! rowspan = "4" | A
| A
|| About Accessible
|| To make data accessible may include making the data open using a standardised protocol. However the data does not necessarily have to be open. There are sometimes good reasons why data cannot be made open, for example privacy concerns, national security or commercial interests. If it is not open there should be clarity and transparency around the conditions governing access and reuse
|-
| A1
|| About data accessibility
|| Not all data that is discoverable can be freely accessed. Often there are embargoes, access controls, and access permissions associated with data due to a variety of issues such as privacy and commercial interests. Even with all these issues much sensitive data can be shared. Many other issues that could be perceived as blockers to sharing data may be overcome.
|-
| A1.1/A1.2
|| About protocols
|| Ideally users would like to retrieve appropriate internet content directly and unhindered once they have located it. Internet protocols (e.g. http and ftp) define rules and conventions for communication between devices, and tools and services are available to facilitate this process, e.g. APIs.

Protocols:
* HTTP (Hypertext Transfer Protocol) is the set of rules for transferring files (text, graphic images, sound, video, and other multimedia files) on the World Wide Web. As soon as a Web user opens their Web browser, the user is indirectly making use of HTTP.
* FTP (File Transfer Protocol) is a standard Internet protocol for transmitting files between computers on the Internet over TCP/IP connections. Read more: Web protocols
* API (Application Programming Interface): When information is made available in any machine readable format, it becomes possible to make that information directly available to programs that request that information over the web. An API is the way this information is made directly available to other machines.

|-
| A2
|| About metadata availability
|| Effort is often required to maintain data resources online which can often lead to it being neglected especially over long periods of time. This often leads to broken links between the metadata and the data. Having at least a description of the data in the form of a metadata record means there is a record of the data's existence allowing the possibility of someone tracking it down.

|-
! rowspan="4" | I
|| I
|| About Interoperability
|| To be interoperable (i.e. data that is interpretable by a computer, so that they can be automatically combined with other data) the data will need to use community agreed formats, language and vocabularies. The metadata will also need to use a community agreed standards and vocabularies, and contain links to related information using identifiers.
|-
|| I1
|| About file formats
|| When selecting file formats for archiving, the formats should ideally be:
* Non-proprietary
* Unencrypted
* Uncompressed
* In common usage by the research community
* Adherent to an open, documented standard, such as described by the State of California (see AB 1668, 2007)
* Interoperable among diverse platforms and applications
* Fully published and available royalty-free
* Fully and independently implementable by multiple software providers on multiple platforms without any intellectual property restrictions for necessary technology
* Developed and maintained by an open standards organization with a well-defined inclusive process for evolution of the standard.

From: Stanford University Library - Best practices for file formats
File format examples:
* Structured, open standard, machine-readable format e.g. (text) PDF/A, HTML, Plain text, (images) TIFF, JPEG 2000, GIF, (audio) MP3, AIFF, WAVE, (video) MOV, MPEG, AVI, (Tabular data) CSV
* structured, open standard, non-machine-readable format, e.g. PDF, HTML, JPG
* Proprietary format, e.g. doc (Word), .xls (Excel), .ppt (PowerPoint), .sav

Read more:
* ANDS
* European Data Portal

|-
|| I2
|| About schema standards
|| Schema standards are schemas that have gone through a formal validation process by a standards organisation, such as the International Standards Organisation (ISO) or an equivalent body such as the Dublin Core Metadata Initiative (DCMI); or, commonly used and consistently applied metadata schemas that are well documented, endorsed, and maintained can also become ‘de-facto standards’, e.g. RIF-CS for describing data collections and services used in Research Data Australia.

Read more: [https://www.ands.org.au/guides/metadata-working Metadata schema]

Standardised open and universal schemas, e.g, [https://www.dcc.ac.uk/resources/metadata-standards/datacite-metadata-schema DataCite Metadata Schema], [https://www.dcc.ac.uk/resources/metadata-standards/prov PROV], [https://www.dcc.ac.uk/resources/metadata-standards/dublin-core Dublin Core]

Domain-specific standards, e.g. [http://human-phenotype-ontology.github.io/ HPO - Human Phenotype Ontology], [https://www.nlm.nih.gov/mesh/ MeSH - Medical Subject Headings] , [https://mcp-profile-docs.readthedocs.io/en/stable/ Marine Community Profile], [https://ddialliance.org/ DDI - Data Documentation Initiative].

For more examples of standards: [http://rd-alliance.github.io/metadata-directory/ Here].

Resolvable global identifiers - An identifier is any label used to name something uniquely (whether online or offline). URLs are an example of an identifier. So are serial numbers, and personal names. A persistent identifier is guaranteed to be managed and kept up to date over a defined time period.

Read more: [https://www.ands.org.au/guides/persistent-identifiers-awareness Persistent Identifiers]

|-
||I3
|| About metadata links
|| The goal is to create as many meaningful linkages as possible between (meta)data resources to enrich the contextual knowledge about the data, balanced against the time/energy involved in making a good data model.

'''Machine-readable (meta)data'''

(Meta)data in a format that can be automatically read and processed by a computer, such as [http://opendatahandbook.org/glossary/en/terms/csv/ CSV], [http://opendatahandbook.org/glossary/en/terms/json/ JSON], [http://opendatahandbook.org/glossary/en/terms/xml/ XML] etc. For more information: [http://opendatahandbook.org/glossary/en/terms/machine-readable/ Open Data Handbook]

'''Linked Data'''

Linked Data (also known as Linking Data) can be applied to improve the exploitation of the “Web of data.” The expression refers to the publishing of structured data in a way that typed links are created between data from different sources to provide a higher level of usability. By using Linked Data, it is possible to find other, related data. Structured data should meet four requirements to be called
Linked Data:

* URIs should be assigned to all entities of the dataset.
* HTTP URIs are required to ensure that all entities can be referenced and cited by users and user agents.
* Entities should be described using standard formats such as RDF/XML.
* Links should be created to other, related entity URIs.

All data that fulfil these requirements and are released for the public are called Linked Open Data (LOD). http://www.lesliesikos.com/semantic-web-machine-readable-structured-data-with-meaningful-annotations/

Read more under the heading (meta)data include qualified references to other (meta)data in the following resource: https://www.dtls.nl/fair-data/fair-principles-explained/

For more information on Linked Data standards - RDF - https://www.w3.org/RDF/

Other related linked data standards:
* OWL - https://www.w3.org/OWL/
* SKOS - https://www.w3.org/2004/02/skos/
* SPARQL - https://www.w3.org/TR/rdf-sparql-query/

|-
! rowspan="3" | R
|| R
|| About Reusable
|| Reusable data should maintain its initial richness. For example, it should not be abridged for the purpose of explaining the findings in one particular publication. It needs a clear machine-readable [https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse licence] and [https://www.ands.org.au/partners-and-communities/ands-communities/data-provenance-interest-group provenance] information on how the data was formed. It should also use discipline-specific data and [https://www.ands.org.au/working-with-data/metadata metadata] standards to give it rich contextual information that will allow for accurate interpretation and reuse.

|-
|| R1.1
|| About licenses
|| If data is not licensed no-one else can use it. In Australia, no licence is regarded as the same as 'all rights reserved', confining any reuse to very limited circumstances. Applying a Creative Commons licence to your data is a simple way to ensure that your data can be reused. The less restrictive the licence, the more that can be done with the data.
To make it easy for the Web to know when a work is available under a particular license, a “machine readable” version of the license provides a summary of the key freedoms and obligations written into a format that software systems, search engines, and other kinds of technology can understand.
Example from the [https://creativecommons.org/choose/ Creative Commons License Choser]:

What appears in the metadata:

This work is licensed under a [https://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International License].

Code snippet:

<code>
<a rel="license" href="http://creativecommons.org/licenses/by/4.0/">

<img alt="Creative Commons License" style="border-width:0"

src="https://i.creativecommons.org/l/by/4.0/88x31.png"/>

</a> This work is licensed under a

<a rel="license" href="http://creativecommons.org/licenses/by/4.0/">

Creative Commons Attribution 4.0 International License</a>.
<\code>

Read more: https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse

|-
||R1.2
|| About provenance
|| Data provenance is used to document where a piece of data comes from and the process and methodology by which it is produced. It is important to confirm the authenticity of data enabling trust, credibility and reproducibility. This is becoming increasingly important, especially in the eScience community where research is data intensive and often involves complex data transformations and procedures ([https://www.ands.org.au/partners-and-communities/ands-communities/data-provenance-interest-group Read more]). Provenance vocabularies such as [https://www.w3.org/2005/Incubator/prov/XGR-prov-20101214/#Recommendations Provenir Ontology (PROV-O) and others].

|}

== Results for SMILES and ShareWind ==

{| class="mw-collapsible wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Datasets from the SMILES and ShareWind platforms was analysed using the self-assessment tool provided by ARDC.
! rowspan="5" |
| rowspan="5" |
! Database Code
| rowspan="24" style="background: #ABE" |
| ED4
| ED9
|-
! Database Code
|| SMILES
| ShareWind
|-
! Subdata-Code
|| https://www.ecria-smiles.eu/
| https://sharewind.eu/
|-
! Database/Subdata
|| [https://www.ecria-smiles.eu/data-files/-/document_library/W32cCAfL2jMX/view_file664466?_com_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX_redirect=https%3A%2F%2Fwww.ecria-smiles.eu%3A443%2Fdata-files%2F-%2Fdocument_library%2FW32cCAfL2jMX%2Fview%2F664423%3F_com_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX_redirect%3Dhttps%253A%252F%252Fwww.ecria-smiles.eu%253A443%252Fdata-files%253Fp_p_id%253Dcom_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX%2526p_p_lifecycle%253D0%2526p_p_state%253Dnormal%2526p_p_mode%253Dview here] (link has 585 characters)
| https://sharewind.eu/records/f9d31abbc2824284b8c1d28894d9619a; https://zenodo.org/record/1162738
|-
! Sample Size
|| 4 KB
| 37 MB
|-
| colspan="6" style="background: #ABE" |
|-
! rowspan="4" | Findable
| F1. (meta)data are assigned a globally unique and persistent identifier
! Does the dataset have any identifiers assigned?
|| Web adress (URL)
| Globally unique, citable and persistant (e.g. DOI, PURL, ARK or Handle)
|-

| F2. data are described with rich metadata (defined by R1 below)
! Is the dataset identifier included in all metadata records/files describing the data?
|| No
| No
|-

| F3. metadata clearly and explicitly include the identifier of the data it describes
! How is the data described with metadata?
|| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
|-

| F4. (meta)data are registered or indexed in a searchable resource
! What type of repository or registry is the metadata record in?
|| Domain-specific repository
| Data is in one place but discoverable through several registries
|-

! rowspan="3" | Accessible

| A1. (meta)data are retrievable by their identifier using a standardized communications protocol
! How accessible is the data?
|| Publicly accessible
| Publicly accessible
|-
| A1.1 the protocol is open, free, and universally implementable; A1.2 the protocol allows for an authentication and authorization procedure, where necessary
! Is the data available online without requiring specialised protocols or tools once access has been approved?
|| File download from online location
| Standard web service API (e.g. OGC)
|-
| A2. metadata are accessible, even when the data are no longer available
! Will the metadata record be available even if the data is no longer available?
|| Unsure
| Yes
|-

! rowspan="3" | Interoperable
| I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
! What (file) format(s) is the data available in?
|| In a structured, open standard, machine-readable format
|| In a structured, open standard, non-machine-readable format
|-
| I2. (meta)data use vocabularies that follow FAIR principles
! What best describes the types of vocabularies/ontologies/tagging schemas used to define the data elements?
|| Standardised vocabularies/ontologies/schema without global identifiers
|| Standardised vocabularies/ontologies/schema without global identifiers
|-
| I3. (meta)data include qualified references to other (meta)data
! How is the metadata linked to other data and metadata (to enhance context and clearly indicate relationships)?
|| There are no links to other metadata
|| Metadata is represented in a machine readable format, e.g. in a linked data format such as Resource Description Framework (RDF)
|-

! rowspan="3" | Reusable
| R1.1. (meta)data are released with a clear and accessible data usage license
! Which of the following best describes the license/usage rights attached to the data?
|| No license
| Standard machine-readable license (e.g. Creative Commons)
|-
| R1.2. (meta)data are associated with detailed provenance
! How much provenance information has been captured to facilitate data reuse?
|| Partially recorded
|| Partially recorded
|-
| R1.3. (meta)data meet domain-relevant community standards
|
|
|
|-
| colspan="6" style="background: #ABE" |
|-
! Total across F.A.I.R
|
! Low: < 40%; Medium: 50-60%; High: > 60%
|| 45 %
| 85 %
|-
| colspan="6" style="background: #ABE" |
|-
! Remarks about database
||
||
||
|| The data is provided by the open-source platform Zenodo, not the project website itself. A very positive aspect is that information on different versions of the data as well as the number of views and downloads is available.
|-
! Identified gaps
||
||
||
* No DOI, only URL
* Extremely long URL link spanning several lines with 585 characters, that can easily become corrupted
* No links to other metadata
* No license information given
* Provenance not fully recorded

||
* Dataset identifiers should be included in the files describing the data
* Provenance details are only partially recorded with a note with contact name for further information
|}

= Dublin Core Evaluation =

== Dublin Core Elements ==
{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ The DCMI Metadata Terms lists the current set of the Dublin Core vocabulary. Source: [https://dublincore.org/specifications/dublin-core/dcmi-terms/ Dublin Core Metadata Initiative]
| 1 || abstract || 11 || contributor || 21 || extent || 31 || isReplacedBy || 41 || publisher || 51 || tableOfContents
|-
| 2 || accessRights || 12 || coverage || 22 || format || 32 || isRequiredBy || 42 || references || 52 || temporal
|-
| 3 || accrualMethod || 13 || created || 23 || hasFormat || 33 || issued || 43 || relation || 53 || title
|-
| 4 || accrualPeriodicity || 14 || creator || 24 || hasPart || 34 || isVersionOf || 44 || replaces || 54 || type
|-
| 5 || accrualPolicy || 15 || date || 25 || hasVersion || 35 || language || 45 || requires || 55 || valid
|-
| 6 || alternative || 16 || dateAccepted || 26 || identifier || 36 || license || 46 || rights || ||
|-
| 7 || audience || 17 || dateCopyrighted || 27 || instructionalMethod || 37 || mediator || 47 || rightsHolder || ||
|-
| 8 || available || 18 || dateSubmitted || 28 || isFormatOf || 38 || medium || 48 || source || ||
|-
| 9 || bibliographicCitation || 19 || description || 29 || isPartOf || 39 || modified || 49 || spatial || ||
|-
| 10 || conformsTo || 20 || educationLevel || 30 || isReferencedBy || 40 || provenance || 50 || subject || ||
|}

'''Metadata Category Types according to [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO]'''

There are three main types of metadata:
* Descriptive metadata describes a resource for purposes such as discovery and identification. It can include elements such as title, abstract, author, and keywords.
* Structural metadata indicates how compound objects are put together, for example, how pages are ordered to form chapters.
* Administrative metadata provides information to help manage a resource, such as when and how it was created, file type and other technical information, and who can access it. There are several subsets of administrative data; two that sometimes are listed as separate metadata types are:
** Rights management meta-data, which deals with intellectual property rights,and
** Preservation metadata, which contains information needed to archive and preserve a resource.
''

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Simple Dublin Core Elements. 15 DC elements with their (shortened) official definitions and examples.
! DC element name !! DC definition ([https://www.dublincore.org/specifications/dublin-core/dces/ DCMI]) !! Example (Energy Domain) !! Category (determined with the aid of [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO])
|-
! Title
| A name given to the resource || Outdoor monitoring of a hybrid micro-CPV solar panel with integrated micro-tracking and diffuse capture || Descriptive
|-
! Subject
| The topic of the resource || concentrator photovoltaics; CPV; rooftop photovoltaics; integrated tracking || Descriptive
|-
! Description
| An account of the resource || Dataset from the outdoor characterization of a B Series module from Insolight at the rooftop of the Instituto de Energía Solar - Universidad Politécnica de Madrid. …. || Descriptive
|-
! Creator
| An entity primarily responsible for making the resource || Stephen Askins; Gaël Nardin || Descriptive
|-
! Publisher
| An entity responsible for making the resource available || Stephen Askins || Descriptive
|-
! Contributor
| An entity responsible for making contributions to the resource || Stephen Askins || Descriptive
|-
! Date
| A point or period of time associated with an event in the lifecycle of the resource || 2019-07-23; Date will be associated with the creation or availability of the resource. || Administrative
|-
! Type
| The nature or genre of the resource || info:eu-repo/semantics/other; dataset || Descriptive
|-
! Format
| The file format, physical medium, or dimensions of the resource || image
|| Structural
|-
! Identifier
| An unambiguous reference to the resource within a given context || https://zenodo.org/record/3346823; 10.5281/zenodo.3346823; oai:zenodo.org:3346823 || Descriptive
|-
! Source
| A related resource from which the described resource is derived || RC607.A26W574 1996 [where "RC607.A26W574 1996" is the call number of the print version of the resource, from which the present version was scanned] || Descriptive
|-
! Language
| A language of the resource || eng || Descriptive
|-
! Relation
| A related resource || info:eu-repo/grantAgreement/EC/H2020/787289/ || Descriptive
|-
! Coverage
| The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant || 1995-1996; Madrid, Spain || Descriptive
|-
! Rights
| Information about rights held in and over the resource || info:eu-repo/semantics/openAccess; https://creativecommons.org/licenses/by/4.0/legalcode || Administrative
|}

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Non-DC elements

! Non-DC element name !! Definition !! Example !! Category
|-
! Date of availability
| Since when is the data available
||
|| Impact/Exploitation

|-
! Number of users
| Is there any information on the number of accesses or users?
||
|| Impact/Exploitation

|-
! Number of scientific publications where data is cited/used
| Is there any information on the number of citations/uses?
||
|| Impact/Exploitation

|-
! Curation activities (is the versioning ongoing)
| Update, maintainance
||
|| Impact/Exploitation
|}

Attempting to put a value on Impact/Exploitation:
* '''ENTSO-E''': Found 172 results in Advanced Search (ALL) in Web of Science ([https://apps.webofknowledge.com/ WoSc]) (16/06/2020). Citations of all these search result add up to 714. This however does not mean, that all search results really explicitly used data from ENTSO-E. Some (for sure, for some were found) only mentioned it saying they adoped workflow etc. from the site.
* '''SMARD''': Found 82 results in Advanced Search (ALL=(SMARD)) in Web of Science ([https://apps.webofknowledge.com/ WoSc]) (16/06/2020). However: SMARD is also an acronym for ''Spinal muscular atrophy with respiratory distress (SMARD)''. This somewhat complicates results and needs some further discussion. All 82 search results accumumlated 2359 citations themselves.

Gap analysis

2020-08-27T15:09:36Z

Christopherbs: /* FAIR Self-Assessment tool */

<accesscontrol>Administrators,consortium</accesscontrol>

This is the page for the gap analysis.

= Tacit knowledge "FAIRification and opening of low carbon energy research data" =
 Consortium members can add any time, they feel that something is important to note even though it is not mentioned in a standard deliverable. In other words, this page collects uncodified, tacit knowledge. It will help us later to compile suggestions and lessons learned. This kind of knowledge is collected two-fold:
#Anytime if someone feels that this should be noted. Please write down: Issue, Date, Author (could also be "anonymous"), the issue described in a few words or maximal lines>
#During the final day of workshops

=== Learning process: ===

*People are hesitant to adopt new IT technologies, this is even the case among researchers heavily relying on data, algorithms and collaborative online software (e.g., R, platforms, online conferencing, HPC, ...). The effort to encourage change is not to underestimate. Reasons are several, notably, lack of time and uncertainty about potential benefit as well as overall risk aversion preferences. EERAdata is using the online software "Only Office" to facilitate collaboration (in particular also during the Covid-19 period).
 '''FAIR and open criteria:'''
*Consortium members have a fair understanding of what FAIR/O is, but there is little knowledge and/or technical experience on how to approach the FAIRification and opening. All, however, share the view that we are at a critical point in time, where we need to implement these criteria. 
*To deepen knowledge about FAIR/O criteria, it is useful to test the criteria on a database one is familiar with. For this purpose (and to start brainstorming about the platform), AIT has developed a questionnaire for application in the use cases.
'''Metadata:'''
*A good starting point is to think about metadata and to look into existing metadata concepts in one's field. The first step is to understand that also metadata need to adhere to the FAIR/O principles.
*The next step is to increase knowledge on IT specific terms, i.e. to understand what the difference is between different '''[https://energydata.onlyoffice.eu/Products/Files/doceditor.aspx?fileid=5404340 metadata frameworks] (taxonomy, thesaurus, ontology) as well as classification of metadata (high-level, medium-level, low-level OR administrative, structural and descriptive metadata).  '''WP 2 being in charge of aligning approaches between use cases, participates in all use case kickoffs to bring everybody on the same page. The presentation is linked with "metadata frameworks".
*It is useful to supply consortium members with read aheads and watch aheads on metadata to prepare the '''[https://energydata.onlyoffice.eu/Products/Files/doceditor.aspx?fileid=5296590 first EERAdata workshop]'''. The workshop starts applications and discussions in the use cases break out sessions (and bringing insights back to the plenary), using selected databases.

= FAIR Evaluation=

== FAIR Guiding Principles ==
{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ This table shows the FAIR guiding principles described by [https://www.doi.org/10.1038/sdata.2016.18 Wilkinson et al., 2016].

! To be Findable
|
* F1. (meta)data are assigned a globally unique and persistent identifier
* F2. data are described with rich metadata (defined by R1 below)
* F3. metadata clearly and explicitly include the identifier of the data it describes
* F4. (meta)data are registered or indexed in a searchable resource

|-
! To be Accessible
|
* A1. (meta)data are retrievable by their identifier using a standardized communications protocol
** A1.1 the protocol is open, free, and universally implementable
** A1.2 the protocol allows for an authentication and authorization procedure, where necessary
* A2. metadata are accessible, even when the data are no longer available

|-
! To be Interoperable
|
* I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
* I2. (meta)data use vocabularies that follow FAIR principles
* I3. (meta)data include qualified references to other (meta)data

|-
! To be Reusable
|
* R1. meta(data) are richly described with a plurality of accurate and relevant attributes
* R1.1. (meta)data are released with a clear and accessible data usage license
* R1.2. (meta)data are associated with detailed provenance
* R1.3. (meta)data meet domain-relevant community standards

|}

== Identified gaps after Workshop 1 (02/06/2020 – 04/06/2020) ==

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ This table shows a summary of the specific issues that were identified in Workshop 1. The aim was to categorise different issues to get a better understanding of how to tackle these challenges.
! FAIR/O !! Specific FAIR/O !! Gaps !! Gaps for energy domain !! Use case specific gaps !! Consequences for researchers
|-
! F
| F2
|| Metadata scope
|| Different fields require different metadata (potentially very specific)
|| UC3: lack of additional metadata for applications of materials
|| Data are less useful for the specific field
|-
! F
|| F1/F3
|| Identifiers
||
* Missing identifiers on websites
* Identifier not in downloaded data files
||
|| Makes research more difficult.
|-
! I
| I2
|| Taxonomy/ontology/common vocabulary and language issues
||
* Lack of standardisation
* Heterogeneous data makes standardisation hard
* No vocabulary documentation on websites
* Words used for same term in other languages may differ
* Databases in other languages than English
||
|| Research costs more time
|-
! I
| I3
|| References to other (meta)data
||
* No linking to source documents and related publications (for contextual knowledge)
* Possible need for links to other fields
|| UC3: link between microscopic and macroscopic materials (e.g., turbine blades)
||
* Data cannot be connected to the source
* Makes research more difficult.

|-
! R
| R1.1
|| Licensing
||
* No licenses available may mean the data is not reusable for the researcher
* Licenses not clear and accessible
* Obtaining licenses may result in more effort and costs
||
|| Data potentially not reusable

|-
! R
| R1.2
|| Reliability and provenance of data
|| When the data is collected from different and high number of sources, its reliability decreases
||
|| Data potentially not reusable

|-
! O
|
|| Privacy concerns and expected disadvantages
|| Not publishing data due to privacy concerns (sensitive data)
|| UC2: In case of distribution network data this is relevant
|| Data not accessible

|-
! Other
|
|| Conducting FAIR assessments
||
* No qualitative assessment of the data itself (which may be limited)
* Discrepancies between results conducted by humans versus machines
* Sometimes not even DC standards are met
* Metadata is not updated
* Lacking encryption of websites (https)
* Problems assessing whether metadata will be available after data is unavailable
* Interface design/layout may be unclear, incomplete or not intuitive for humans
* Authentication details (user registration login / good or bad, clarify)

||
* UC1 lack of data availability for time-series
||
* Assessment is uncertain (human assessment may need more clarification and understanding)
* Makes research more time-intensive

|}

== FAIR Self-Assessment tool ==

To conduct a human FAIR evaluation for different databases and their respective datasets, different free online tools were considered. Some seemed to be quite useful (e.g. [https://satifyd.dans.knaw.nl/ SATISFYD] ) and some were simple self-evaluation PDFs to be printed and filled out.

We decided to chose the [https://ardc.edu.au/resources/working-with-data/fair-data/fair-self-assessment-tool/ self-assessment tool] provided by the Australian Research Data Commons (ARDC). The decision was based manly upon the fact that the tools questions closely matched the FAIR categories specified by [https://www.doi.org/10.1038/sdata.2016.18 Wilkinson et al., 2016] which would make it easily comparable to the existing preferable FAIR conditions and aid with the repeatability of the results, and the half-automated scoring features that show the extent of the "FAIRness" of the analysed sample as a simple bar chart.

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ All options for each question of the ARDCs self-assessment tool
!colspan="4"|Findable
!colspan="3"|Accessible
!colspan="3"|Interoperable
!colspan="3"|Reusable
|-
| F1. (meta)data are assigned a globally unique and persistent identifier
|| F2. data are described with rich metadata (defined by R1 below)
|| F3. metadata clearly and explicitly include the identifier of the data it describes
|| F4. (meta)data are registered or indexed in a searchable resource
|| A1. (meta)data are retrievable by their identifier using a standardized communications protocol
|| A1.1 the protocol is open, free, and universally implementable and A1.2 the protocol allows for an authentication and authorization procedure, where necessary
|| A2. metadata are accessible, even when the data are no longer available
|| I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
|| I2. (meta)data use vocabularies that follow FAIR principles
|| I3. (meta)data include qualified references to other (meta)data
|| R1.1. (meta)data are released with a clear and accessible data usage license
|| R1.2. (meta)data are associated with detailed provenance
|| R1.3. (meta)data meet domain-relevant community standards
|-
! Does the dataset have any identifiers assigned?
! Is the dataset identifier included in all metadata records/files describing the data?
! How is the data described with metadata?
! What type of repository or registry is the metadata record in?
! How accessible is the data?
! Is the data available online without requiring specialised protocols or tools once access has been approved?
! Will the metadata record be available even if the data is no longer available?
! What (file) format(s) is the data available in?
! What best describes the types of vocabularies/ontologies/tagging schemas used to define the data elements?
! How is the metadata linked to other data and metadata (to enhance context and clearly indicate relationships)?
! Which of the following best describes the license/usage rights attached to the data?
! How much provenance information has been captured to facilitate data reuse?
!
|-
| Globally unique, citable and persistant (e.g. DOI, PURL, ARK or Handle)
|| Yes
|| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
|| Data is in one place but discoverable through several registries
|| Publicly accessible
|| Standard web service API (e.g. OGC)
|| Yes
|| In a structured, open standard, machine-readable format
|| Standardised open and universal using resolvable global identifiers linking to explainations
|| Meadata is represented in a machine readable format, e.g. in a linked data format such as Resource Description Framework (RDF)
|| Standard machine-readable license (e.g. Creative Commons)
|| Fully recorded in a machine-readable format
||
|-
| Web adress (URL)
|| No
|| Comprehensively, but in a text-based, non-standard format
|| Generalist public repository
|| Fully accessible to person who meet explicitly stated conditions, e.g. ethics approval for sensitive data
|| Non-standard web service (e.g. OpenAPI/Swagger/informal API)
|| No
|| In a structured, open standard, non-machine-readable format
|| Standardised vocabularies/ontologies/schema without global identifiers
|| The metadata record includes URI links to related metadata, data and definitions
|| Standard text based license
|| Fully recorded in a text format
||
|-
| Local identifier
||
|| Brief title and description
|| Domain-specific repository
|| A de-identified / modified subset of the data is publicly accessible
|| File download from online location
|| Unsure
|| Mostly in a proprietary format
|| No standards have been applied in the description of data elements
|| There are no links to other metadata
|| Non-standard machine-readably license (clearly indicating under what conditions the data may be reused)
|| Partially recorded
||
|-
| No identifier
||
|| The data is not described
|| Local institutional repository
|| Embaged access after a specific date
|| By individual arrangement
||
||
|| Data elements not described
||
|| Non-standard text-based license
|| No provenance information is recorded
||
|-
|
||
||
|| The data is not described in any repository
|| Unspecified conditional access e.g. contact the data custodian for access
|| No access to data
||
||
||
||
|| No license
||
||
|-
|
||
||
||
|| Access to metadata only
||
||
||
||
||
||
||
||
|-
|
||
||
||
|| No access to data or metadata
||
||
||
||
||
||
||
||
|}

At this point it is important to note that the assessment of databases and/or datasets using this tool is solely considered a human assessment. This may lead to different evaluations by different users. To counteract this phenomenon, it was suggested that the evaluations should be completed using the 'four-eyes principle' to create more verifiable results. Also, it is planned to additionally evaluate the respective databases using an external tool to perform a machine-assessment.

In addition, not all questions are weighted equally and thus a worse result for one question may not have a large impact whereas only choosing the second-best option in another question will bring down the overall FAIR value considerably.

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ The ARDC tool provides detailed information about the requirements for each of the options of its tool. These descriptions are documented here.
|-
! rowspan="5" | F
|| F
|| About Findable
|| Making data Findable includes assigning a persistent identifier (like a DOI or Handle ), having rich metadata to describe the data and making sure it is findable through disciplinary and generalist discovery portals (local and international).

|-
| F1
|| About identifiers
|| Identifiers are essential for identifying, finding, retrieving, linking and citing datasets. A Web address (URL) can be used to specify the online location of a resource but over time URLs tend to change which leads to broken links to the data. To be useful, identifiers need to be persistent and unique. Digital Object Identifiers (DOIs) and other persistent identifiers (PIDs) provide a permanent citable reference to a particular dataset. The DOI is a permanent fixed reference to the dataset no matter where it is located online and enables citation and citation metrics. Services to create a persistent identifier are often offered by your affiliated institution or the repository you are using to describe your data. Talk to your library service for more information.
|-
| F2
|| About identifiers
|| The identifier (preferably a persistent identifier) needs to be clearly stated in the metadata record describing the data collection, and also in any associated data files or metadata.

|-
|F3
|| About metadata descriptions
|| Comprehensive metadata records will include descriptive content that facilitates discovery, access and reuse of the data being described. While there is no 'one size fits all' list, comprehensive metadata should include:
* a globally unique persistent identifier e.g. a DOI
* a title
* related people, i.e. the data creator or custodian
* how to access the data and file formats
* a description of how the data were created and how to interpret the data subject or keywords
* citation information that clearly indicates how the data should be cited
* a machine-readable data licence
* provenance and contextual information such as:
* links to related publications, projects, services and software
* methodology and processes involved in data production
* spatial and temporal coverage (if relevant)
* object-level data description

Providing metadata in a standard schema allows it to be read and used by machines as well as humans.

|-
| F4
|| About metadata repositories
|| A rich metadata description alone does not ensure a dataset’s ‘findability’ on the internet; the dataset needs to be registered or indexed in a searchable resource, such as a generalist (e.g. Figshare, Dryad, Zenodo domain-specific (e.g. PANGAEA, ADA, ICPSR), or institutional (e.g. ANU Research Repository , Sydney eScholarship Repository, data.gov.au) data repository or registry. Ideally these repositories/registries are indexed by search engines such as Google and/or Google Scholar.

|-
! rowspan = "4" | A
| A
|| About Accessible
|| To make data accessible may include making the data open using a standardised protocol. However the data does not necessarily have to be open. There are sometimes good reasons why data cannot be made open, for example privacy concerns, national security or commercial interests. If it is not open there should be clarity and transparency around the conditions governing access and reuse
|-
| A1
|| About data accessibility
|| Not all data that is discoverable can be freely accessed. Often there are embargoes, access controls, and access permissions associated with data due to a variety of issues such as privacy and commercial interests. Even with all these issues much sensitive data can be shared. Many other issues that could be perceived as blockers to sharing data may be overcome.
|-
| A1.1/A1.2
|| About protocols
|| Ideally users would like to retrieve appropriate internet content directly and unhindered once they have located it. Internet protocols (e.g. http and ftp) define rules and conventions for communication between devices, and tools and services are available to facilitate this process, e.g. APIs.

Protocols:
* HTTP (Hypertext Transfer Protocol) is the set of rules for transferring files (text, graphic images, sound, video, and other multimedia files) on the World Wide Web. As soon as a Web user opens their Web browser, the user is indirectly making use of HTTP.
* FTP (File Transfer Protocol) is a standard Internet protocol for transmitting files between computers on the Internet over TCP/IP connections. Read more: Web protocols
* API (Application Programming Interface): When information is made available in any machine readable format, it becomes possible to make that information directly available to programs that request that information over the web. An API is the way this information is made directly available to other machines.

|-
| A2
|| About metadata availability
|| Effort is often required to maintain data resources online which can often lead to it being neglected especially over long periods of time. This often leads to broken links between the metadata and the data. Having at least a description of the data in the form of a metadata record means there is a record of the data's existence allowing the possibility of someone tracking it down.

|-
! rowspan="4" | I
|| I
|| About Interoperability
|| To be interoperable (i.e. data that is interpretable by a computer, so that they can be automatically combined with other data) the data will need to use community agreed formats, language and vocabularies. The metadata will also need to use a community agreed standards and vocabularies, and contain links to related information using identifiers.
|-
|| I1
|| About file formats
|| When selecting file formats for archiving, the formats should ideally be:
* Non-proprietary
* Unencrypted
* Uncompressed
* In common usage by the research community
* Adherent to an open, documented standard, such as described by the State of California (see AB 1668, 2007)
* Interoperable among diverse platforms and applications
* Fully published and available royalty-free
* Fully and independently implementable by multiple software providers on multiple platforms without any intellectual property restrictions for necessary technology
* Developed and maintained by an open standards organization with a well-defined inclusive process for evolution of the standard.

From: Stanford University Library - Best practices for file formats
File format examples:
* Structured, open standard, machine-readable format e.g. (text) PDF/A, HTML, Plain text, (images) TIFF, JPEG 2000, GIF, (audio) MP3, AIFF, WAVE, (video) MOV, MPEG, AVI, (Tabular data) CSV
* structured, open standard, non-machine-readable format, e.g. PDF, HTML, JPG
* Proprietary format, e.g. doc (Word), .xls (Excel), .ppt (PowerPoint), .sav

Read more:
* ANDS
* European Data Portal

|-
|| I2
|| About schema standards
|| Schema standards are schemas that have gone through a formal validation process by a standards organisation, such as the International Standards Organisation (ISO) or an equivalent body such as the Dublin Core Metadata Initiative (DCMI); or, commonly used and consistently applied metadata schemas that are well documented, endorsed, and maintained can also become ‘de-facto standards’, e.g. RIF-CS for describing data collections and services used in Research Data Australia.

Read more: [https://www.ands.org.au/guides/metadata-working Metadata schema]

Standardised open and universal schemas, e.g, [https://www.dcc.ac.uk/resources/metadata-standards/datacite-metadata-schema DataCite Metadata Schema], [https://www.dcc.ac.uk/resources/metadata-standards/prov PROV], [https://www.dcc.ac.uk/resources/metadata-standards/dublin-core Dublin Core]

Domain-specific standards, e.g. [http://human-phenotype-ontology.github.io/ HPO - Human Phenotype Ontology], [https://www.nlm.nih.gov/mesh/ MeSH - Medical Subject Headings] , [https://mcp-profile-docs.readthedocs.io/en/stable/ Marine Community Profile], [https://ddialliance.org/ DDI - Data Documentation Initiative].

For more examples of standards: [http://rd-alliance.github.io/metadata-directory/ Here].

Resolvable global identifiers - An identifier is any label used to name something uniquely (whether online or offline). URLs are an example of an identifier. So are serial numbers, and personal names. A persistent identifier is guaranteed to be managed and kept up to date over a defined time period.

Read more: [https://www.ands.org.au/guides/persistent-identifiers-awareness Persistent Identifiers]

|-
||I3
|| About metadata links
|| The goal is to create as many meaningful linkages as possible between (meta)data resources to enrich the contextual knowledge about the data, balanced against the time/energy involved in making a good data model.

'''Machine-readable (meta)data'''

(Meta)data in a format that can be automatically read and processed by a computer, such as [http://opendatahandbook.org/glossary/en/terms/csv/ CSV], [http://opendatahandbook.org/glossary/en/terms/json/ JSON], [http://opendatahandbook.org/glossary/en/terms/xml/ XML] etc. For more information: [http://opendatahandbook.org/glossary/en/terms/machine-readable/ Open Data Handbook]

'''Linked Data'''

Linked Data (also known as Linking Data) can be applied to improve the exploitation of the “Web of data.” The expression refers to the publishing of structured data in a way that typed links are created between data from different sources to provide a higher level of usability. By using Linked Data, it is possible to find other, related data. Structured data should meet four requirements to be called
Linked Data:

* URIs should be assigned to all entities of the dataset.
* HTTP URIs are required to ensure that all entities can be referenced and cited by users and user agents.
* Entities should be described using standard formats such as RDF/XML.
* Links should be created to other, related entity URIs.

All data that fulfil these requirements and are released for the public are called Linked Open Data (LOD). http://www.lesliesikos.com/semantic-web-machine-readable-structured-data-with-meaningful-annotations/

Read more under the heading (meta)data include qualified references to other (meta)data in the following resource: https://www.dtls.nl/fair-data/fair-principles-explained/

For more information on Linked Data standards - RDF - https://www.w3.org/RDF/

Other related linked data standards:
* OWL - https://www.w3.org/OWL/
* SKOS - https://www.w3.org/2004/02/skos/
* SPARQL - https://www.w3.org/TR/rdf-sparql-query/

|-
! rowspan="3" | R
|| R
|| About Reusable
|| Reusable data should maintain its initial richness. For example, it should not be abridged for the purpose of explaining the findings in one particular publication. It needs a clear machine-readable [https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse licence] and [https://www.ands.org.au/partners-and-communities/ands-communities/data-provenance-interest-group provenance] information on how the data was formed. It should also use discipline-specific data and [https://www.ands.org.au/working-with-data/metadata metadata] standards to give it rich contextual information that will allow for accurate interpretation and reuse.

|-
|| R1.1
|| About licenses
|| If data is not licensed no-one else can use it. In Australia, no licence is regarded as the same as 'all rights reserved', confining any reuse to very limited circumstances. Applying a Creative Commons licence to your data is a simple way to ensure that your data can be reused. The less restrictive the licence, the more that can be done with the data.
To make it easy for the Web to know when a work is available under a particular license, a “machine readable” version of the license provides a summary of the key freedoms and obligations written into a format that software systems, search engines, and other kinds of technology can understand.
Example from the [https://creativecommons.org/choose/ Creative Commons License Choser]:

What appears in the metadata:

This work is licensed under a [https://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International License].

Code snippet:

<code>
<a rel="license" href="http://creativecommons.org/licenses/by/4.0/">

<img alt="Creative Commons License" style="border-width:0"

src="https://i.creativecommons.org/l/by/4.0/88x31.png"/>

</a> This work is licensed under a

<a rel="license" href="http://creativecommons.org/licenses/by/4.0/">

Creative Commons Attribution 4.0 International License</a>.
<\code>

Read more: https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse

|-
||R1.2
|| About provenance
|| Data provenance is used to document where a piece of data comes from and the process and methodology by which it is produced. It is important to confirm the authenticity of data enabling trust, credibility and reproducibility. This is becoming increasingly important, especially in the eScience community where research is data intensive and often involves complex data transformations and procedures ([https://www.ands.org.au/partners-and-communities/ands-communities/data-provenance-interest-group Read more]). Provenance vocabularies such as [https://www.w3.org/2005/Incubator/prov/XGR-prov-20101214/#Recommendations Provenir Ontology (PROV-O) and others].

|}

== Results for SMILES and ShareWind ==

{| class="mw-collapsible wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Datasets from the SMILES and ShareWind platforms was analysed using the self-assessment tool provided by ARDC.
! rowspan="5" |
| rowspan="5" |
! Database Code
| rowspan="24" style="background: #ABE" |
| ED4
| ED9
|-
! Database Code
|| SMILES
| ShareWind
|-
! Subdata-Code
|| https://www.ecria-smiles.eu/
| https://sharewind.eu/
|-
! Database/Subdata
|| [https://www.ecria-smiles.eu/data-files/-/document_library/W32cCAfL2jMX/view_file664466?_com_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX_redirect=https%3A%2F%2Fwww.ecria-smiles.eu%3A443%2Fdata-files%2F-%2Fdocument_library%2FW32cCAfL2jMX%2Fview%2F664423%3F_com_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX_redirect%3Dhttps%253A%252F%252Fwww.ecria-smiles.eu%253A443%252Fdata-files%253Fp_p_id%253Dcom_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX%2526p_p_lifecycle%253D0%2526p_p_state%253Dnormal%2526p_p_mode%253Dview here] (link has 585 characters)
| https://sharewind.eu/records/f9d31abbc2824284b8c1d28894d9619a; https://zenodo.org/record/1162738
|-
! Sample Size
|| 4 KB
| 37 MB
|-
| colspan="6" style="background: #ABE" |
|-
! rowspan="4" | Findable
| F1. (meta)data are assigned a globally unique and persistent identifier
! Does the dataset have any identifiers assigned?
|| Web adress (URL)
| Globally unique, citable and persistant (e.g. DOI, PURL, ARK or Handle)
|-

| F2. data are described with rich metadata (defined by R1 below)
! Is the dataset identifier included in all metadata records/files describing the data?
|| No
| No
|-

| F3. metadata clearly and explicitly include the identifier of the data it describes
! How is the data described with metadata?
|| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
|-

| F4. (meta)data are registered or indexed in a searchable resource
! What type of repository or registry is the metadata record in?
|| Domain-specific repository
| Data is in one place but discoverable through several registries
|-

! rowspan="3" | Accessible

| A1. (meta)data are retrievable by their identifier using a standardized communications protocol
! How accessible is the data?
|| Publicly accessible
| Publicly accessible
|-
| A1.1 the protocol is open, free, and universally implementable; A1.2 the protocol allows for an authentication and authorization procedure, where necessary
! Is the data available online without requiring specialised protocols or tools once access has been approved?
|| File download from online location
| Standard web service API (e.g. OGC)
|-
| A2. metadata are accessible, even when the data are no longer available
! Will the metadata record be available even if the data is no longer available?
|| Unsure
| Yes
|-

! rowspan="3" | Interoperable
| I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
! What (file) format(s) is the data available in?
|| In a structured, open standard, machine-readable format
|| In a structured, open standard, non-machine-readable format
|-
| I2. (meta)data use vocabularies that follow FAIR principles
! What best describes the types of vocabularies/ontologies/tagging schemas used to define the data elements?
|| Standardised vocabularies/ontologies/schema without global identifiers
|| Standardised vocabularies/ontologies/schema without global identifiers
|-
| I3. (meta)data include qualified references to other (meta)data
! How is the metadata linked to other data and metadata (to enhance context and clearly indicate relationships)?
|| There are no links to other metadata
|| Metadata is represented in a machine readable format, e.g. in a linked data format such as Resource Description Framework (RDF)
|-

! rowspan="3" | Reusable
| R1.1. (meta)data are released with a clear and accessible data usage license
! Which of the following best describes the license/usage rights attached to the data?
|| No license
| Standard machine-readable license (e.g. Creative Commons)
|-
| R1.2. (meta)data are associated with detailed provenance
! How much provenance information has been captured to facilitate data reuse?
|| Partially recorded
|| Partially recorded
|-
| R1.3. (meta)data meet domain-relevant community standards
|
|
|
|-
| colspan="6" style="background: #ABE" |
|-
! Total across F.A.I.R
|
! Low: < 40%; Medium: 50-60%; High: > 60%
|| 45 %
| 85 %
|-
| colspan="6" style="background: #ABE" |
|-
! Remarks about database
||
||
||
|| The data is provided by the open-source platform Zenodo, not the project website itself. A very positive aspect is that information on different versions of the data as well as the number of views and downloads is available.
|-
! Identified gaps
||
||
||
* No DOI, only URL
* Extremely long URL link spanning several lines with 585 characters, that can easily become corrupted
* No links to other metadata
* No license information given
* Provenance not fully recorded

||
* Dataset identifiers should be included in the files describing the data
* Provenance details are only partially recorded with a note with contact name for further information
|}

= Dublin Core Evaluation =

== Dublin Core Elements ==
{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ The DCMI Metadata Terms lists the current set of the Dublin Core vocabulary. Source: [https://dublincore.org/specifications/dublin-core/dcmi-terms/ Dublin Core Metadata Initiative]
| 1 || abstract || 11 || contributor || 21 || extent || 31 || isReplacedBy || 41 || publisher || 51 || tableOfContents
|-
| 2 || accessRights || 12 || coverage || 22 || format || 32 || isRequiredBy || 42 || references || 52 || temporal
|-
| 3 || accrualMethod || 13 || created || 23 || hasFormat || 33 || issued || 43 || relation || 53 || title
|-
| 4 || accrualPeriodicity || 14 || creator || 24 || hasPart || 34 || isVersionOf || 44 || replaces || 54 || type
|-
| 5 || accrualPolicy || 15 || date || 25 || hasVersion || 35 || language || 45 || requires || 55 || valid
|-
| 6 || alternative || 16 || dateAccepted || 26 || identifier || 36 || license || 46 || rights || ||
|-
| 7 || audience || 17 || dateCopyrighted || 27 || instructionalMethod || 37 || mediator || 47 || rightsHolder || ||
|-
| 8 || available || 18 || dateSubmitted || 28 || isFormatOf || 38 || medium || 48 || source || ||
|-
| 9 || bibliographicCitation || 19 || description || 29 || isPartOf || 39 || modified || 49 || spatial || ||
|-
| 10 || conformsTo || 20 || educationLevel || 30 || isReferencedBy || 40 || provenance || 50 || subject || ||
|}

'''Metadata Category Types according to [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO]'''

There are three main types of metadata:
* Descriptive metadata describes a resource for purposes such as discovery and identification. It can include elements such as title, abstract, author, and keywords.
* Structural metadata indicates how compound objects are put together, for example, how pages are ordered to form chapters.
* Administrative metadata provides information to help manage a resource, such as when and how it was created, file type and other technical information, and who can access it. There are several subsets of administrative data; two that sometimes are listed as separate metadata types are:
** Rights management meta-data, which deals with intellectual property rights,and
** Preservation metadata, which contains information needed to archive and preserve a resource.
''

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Simple Dublin Core Elements. 15 DC elements with their (shortened) official definitions and examples.
! DC element name !! DC definition ([https://www.dublincore.org/specifications/dublin-core/dces/ DCMI]) !! Example (Energy Domain) !! Category (determined with the aid of [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO])
|-
! Title
| A name given to the resource || Outdoor monitoring of a hybrid micro-CPV solar panel with integrated micro-tracking and diffuse capture || Descriptive
|-
! Subject
| The topic of the resource || concentrator photovoltaics; CPV; rooftop photovoltaics; integrated tracking || Descriptive
|-
! Description
| An account of the resource || Dataset from the outdoor characterization of a B Series module from Insolight at the rooftop of the Instituto de Energía Solar - Universidad Politécnica de Madrid. …. || Descriptive
|-
! Creator
| An entity primarily responsible for making the resource || Stephen Askins; Gaël Nardin || Descriptive
|-
! Publisher
| An entity responsible for making the resource available || Stephen Askins || Descriptive
|-
! Contributor
| An entity responsible for making contributions to the resource || Stephen Askins || Descriptive
|-
! Date
| A point or period of time associated with an event in the lifecycle of the resource || 2019-07-23; Date will be associated with the creation or availability of the resource. || Administrative
|-
! Type
| The nature or genre of the resource || info:eu-repo/semantics/other; dataset || Descriptive
|-
! Format
| The file format, physical medium, or dimensions of the resource || image
|| Structural
|-
! Identifier
| An unambiguous reference to the resource within a given context || https://zenodo.org/record/3346823; 10.5281/zenodo.3346823; oai:zenodo.org:3346823 || Descriptive
|-
! Source
| A related resource from which the described resource is derived || RC607.A26W574 1996 [where "RC607.A26W574 1996" is the call number of the print version of the resource, from which the present version was scanned] || Descriptive
|-
! Language
| A language of the resource || eng || Descriptive
|-
! Relation
| A related resource || info:eu-repo/grantAgreement/EC/H2020/787289/ || Descriptive
|-
! Coverage
| The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant || 1995-1996; Madrid, Spain || Descriptive
|-
! Rights
| Information about rights held in and over the resource || info:eu-repo/semantics/openAccess; https://creativecommons.org/licenses/by/4.0/legalcode || Administrative
|}

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Non-DC elements

! Non-DC element name !! Definition !! Example !! Category
|-
! Date of availability
| Since when is the data available
||
|| Impact/Exploitation

|-
! Number of users
| Is there any information on the number of accesses or users?
||
|| Impact/Exploitation

|-
! Number of scientific publications where data is cited/used
| Is there any information on the number of citations/uses?
||
|| Impact/Exploitation

|-
! Curation activities (is the versioning ongoing)
| Update, maintainance
||
|| Impact/Exploitation
|}

Attempting to put a value on Impact/Exploitation:
* '''ENTSO-E''': Found 172 results in Advanced Search (ALL) in Web of Science ([https://apps.webofknowledge.com/ WoSc]) (16/06/2020). Citations of all these search result add up to 714. This however does not mean, that all search results really explicitly used data from ENTSO-E. Some (for sure, for some were found) only mentioned it saying they adoped workflow etc. from the site.
* '''SMARD''': Found 82 results in Advanced Search (ALL=(SMARD)) in Web of Science ([https://apps.webofknowledge.com/ WoSc]) (16/06/2020). However: SMARD is also an acronym for ''Spinal muscular atrophy with respiratory distress (SMARD)''. This somewhat complicates results and needs some further discussion. All 82 search results accumumlated 2359 citations themselves.

Gap analysis

2020-08-27T15:07:57Z

Christopherbs: /* Results for ShareWind */

<accesscontrol>Administrators,consortium</accesscontrol>

This is the page for the gap analysis.

= Tacit knowledge "FAIRification and opening of low carbon energy research data" =
 Consortium members can add any time, they feel that something is important to note even though it is not mentioned in a standard deliverable. In other words, this page collects uncodified, tacit knowledge. It will help us later to compile suggestions and lessons learned. This kind of knowledge is collected two-fold:
#Anytime if someone feels that this should be noted. Please write down: Issue, Date, Author (could also be "anonymous"), the issue described in a few words or maximal lines>
#During the final day of workshops

=== Learning process: ===

*People are hesitant to adopt new IT technologies, this is even the case among researchers heavily relying on data, algorithms and collaborative online software (e.g., R, platforms, online conferencing, HPC, ...). The effort to encourage change is not to underestimate. Reasons are several, notably, lack of time and uncertainty about potential benefit as well as overall risk aversion preferences. EERAdata is using the online software "Only Office" to facilitate collaboration (in particular also during the Covid-19 period).
 '''FAIR and open criteria:'''
*Consortium members have a fair understanding of what FAIR/O is, but there is little knowledge and/or technical experience on how to approach the FAIRification and opening. All, however, share the view that we are at a critical point in time, where we need to implement these criteria. 
*To deepen knowledge about FAIR/O criteria, it is useful to test the criteria on a database one is familiar with. For this purpose (and to start brainstorming about the platform), AIT has developed a questionnaire for application in the use cases.
'''Metadata:'''
*A good starting point is to think about metadata and to look into existing metadata concepts in one's field. The first step is to understand that also metadata need to adhere to the FAIR/O principles.
*The next step is to increase knowledge on IT specific terms, i.e. to understand what the difference is between different '''[https://energydata.onlyoffice.eu/Products/Files/doceditor.aspx?fileid=5404340 metadata frameworks] (taxonomy, thesaurus, ontology) as well as classification of metadata (high-level, medium-level, low-level OR administrative, structural and descriptive metadata).  '''WP 2 being in charge of aligning approaches between use cases, participates in all use case kickoffs to bring everybody on the same page. The presentation is linked with "metadata frameworks".
*It is useful to supply consortium members with read aheads and watch aheads on metadata to prepare the '''[https://energydata.onlyoffice.eu/Products/Files/doceditor.aspx?fileid=5296590 first EERAdata workshop]'''. The workshop starts applications and discussions in the use cases break out sessions (and bringing insights back to the plenary), using selected databases.

= FAIR Evaluation=

== FAIR Guiding Principles ==
{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ This table shows the FAIR guiding principles described by [https://www.doi.org/10.1038/sdata.2016.18 Wilkinson et al., 2016].

! To be Findable
|
* F1. (meta)data are assigned a globally unique and persistent identifier
* F2. data are described with rich metadata (defined by R1 below)
* F3. metadata clearly and explicitly include the identifier of the data it describes
* F4. (meta)data are registered or indexed in a searchable resource

|-
! To be Accessible
|
* A1. (meta)data are retrievable by their identifier using a standardized communications protocol
** A1.1 the protocol is open, free, and universally implementable
** A1.2 the protocol allows for an authentication and authorization procedure, where necessary
* A2. metadata are accessible, even when the data are no longer available

|-
! To be Interoperable
|
* I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
* I2. (meta)data use vocabularies that follow FAIR principles
* I3. (meta)data include qualified references to other (meta)data

|-
! To be Reusable
|
* R1. meta(data) are richly described with a plurality of accurate and relevant attributes
* R1.1. (meta)data are released with a clear and accessible data usage license
* R1.2. (meta)data are associated with detailed provenance
* R1.3. (meta)data meet domain-relevant community standards

|}

== Identified gaps after Workshop 1 (02/06/2020 – 04/06/2020) ==

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ This table shows a summary of the specific issues that were identified in Workshop 1. The aim was to categorise different issues to get a better understanding of how to tackle these challenges.
! FAIR/O !! Specific FAIR/O !! Gaps !! Gaps for energy domain !! Use case specific gaps !! Consequences for researchers
|-
! F
| F2
|| Metadata scope
|| Different fields require different metadata (potentially very specific)
|| UC3: lack of additional metadata for applications of materials
|| Data are less useful for the specific field
|-
! F
|| F1/F3
|| Identifiers
||
* Missing identifiers on websites
* Identifier not in downloaded data files
||
|| Makes research more difficult.
|-
! I
| I2
|| Taxonomy/ontology/common vocabulary and language issues
||
* Lack of standardisation
* Heterogeneous data makes standardisation hard
* No vocabulary documentation on websites
* Words used for same term in other languages may differ
* Databases in other languages than English
||
|| Research costs more time
|-
! I
| I3
|| References to other (meta)data
||
* No linking to source documents and related publications (for contextual knowledge)
* Possible need for links to other fields
|| UC3: link between microscopic and macroscopic materials (e.g., turbine blades)
||
* Data cannot be connected to the source
* Makes research more difficult.

|-
! R
| R1.1
|| Licensing
||
* No licenses available may mean the data is not reusable for the researcher
* Licenses not clear and accessible
* Obtaining licenses may result in more effort and costs
||
|| Data potentially not reusable

|-
! R
| R1.2
|| Reliability and provenance of data
|| When the data is collected from different and high number of sources, its reliability decreases
||
|| Data potentially not reusable

|-
! O
|
|| Privacy concerns and expected disadvantages
|| Not publishing data due to privacy concerns (sensitive data)
|| UC2: In case of distribution network data this is relevant
|| Data not accessible

|-
! Other
|
|| Conducting FAIR assessments
||
* No qualitative assessment of the data itself (which may be limited)
* Discrepancies between results conducted by humans versus machines
* Sometimes not even DC standards are met
* Metadata is not updated
* Lacking encryption of websites (https)
* Problems assessing whether metadata will be available after data is unavailable
* Interface design/layout may be unclear, incomplete or not intuitive for humans
* Authentication details (user registration login / good or bad, clarify)

||
* UC1 lack of data availability for time-series
||
* Assessment is uncertain (human assessment may need more clarification and understanding)
* Makes research more time-intensive

|}

== FAIR Self-Assessment tool ==

To conduct a human FAIR evaluation for different databases and their respective datasets, different free online tools were considered. Some seemed to be quite useful (e.g. [https://satifyd.dans.knaw.nl/ SATISFYD] ) and some were simple self-evaluation PDFs to be printed and filled out.

We decided to chose the [https://ardc.edu.au/resources/working-with-data/fair-data/fair-self-assessment-tool/ self-assessment tool] provided by the Australian Research Data Commons (ARDC). The decision was based manly upon the fact that the tools questions closely matched the FAIR categories specified by [https://www.doi.org/10.1038/sdata.2016.18 Wilkinson et al., 2016] which would make it easily comparable to the existing preferable FAIR conditions and aid with the repeatability of the results, and the half-automated scoring features that show the extent of the "FAIRness" of the analysed sample as a simple bar chart.

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ All options for each question of the ARDCs self-assessment tool
!colspan="4"|Findable
!colspan="3"|Accessible
!colspan="3"|Interoperable
!colspan="3"|Reusable
|-
| F1. (meta)data are assigned a globally unique and persistent identifier
|| F2. data are described with rich metadata (defined by R1 below)
|| F3. metadata clearly and explicitly include the identifier of the data it describes
|| F4. (meta)data are registered or indexed in a searchable resource
|| A1. (meta)data are retrievable by their identifier using a standardized communications protocol
|| A1.1 the protocol is open, free, and universally implementable and A1.2 the protocol allows for an authentication and authorization procedure, where necessary
|| A2. metadata are accessible, even when the data are no longer available
|| I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
|| I2. (meta)data use vocabularies that follow FAIR principles
|| I3. (meta)data include qualified references to other (meta)data
|| R1.1. (meta)data are released with a clear and accessible data usage license
|| R1.2. (meta)data are associated with detailed provenance
|| R1.3. (meta)data meet domain-relevant community standards
|-
! Does the dataset have any identifiers assigned?
! Is the dataset identifier included in all metadata records/files describing the data?
! How is the data described with metadata?
! What type of repository or registry is the metadata record in?
! How accessible is the data?
! Is the data available online without requiring specialised protocols or tools once access has been approved?
! Will the metadata record be available even if the data is no longer available?
! What (file) format(s) is the data available in?
! What best describes the types of vocabularies/ontologies/tagging schemas used to define the data elements?
! How is the metadata linked to other data and metadata (to enhance context and clearly indicate relationships)?
! Which of the following best describes the license/usage rights attached to the data?
! How much provenance information has been captured to facilitate data reuse?
!
|-
| Globally unique, citable and persistant (e.g. DOI, PURL, ARK or Handle)
|| Yes
|| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
|| Data is in one place but discoverable through several registries
|| Publicly accessible
|| Standard web service API (e.g. OGC)
|| Yes
|| In a structured, open standard, machine-readable format
|| Standardised open and universal using resolvable global identifiers linking to explainations
|| Meadata is represented in a machine readable format, e.g. in a linked data format such as Resource Description Framework (RDF)
|| Standard machine-readable license (e.g. Creative Commons)
|| Fully recorded in a machine-readable format
||
|-
| Web adress (URL)
|| No
|| Comprehensively, but in a text-based, non-standard format
|| Generalist public repository
|| Fully accessible to person who meet explicitly stated conditions, e.g. ethics approval for sensitive data
|| Non-standard web service (e.g. OpenAPI/Swagger/informal API)
|| No
|| In a structured, open standard, non-machine-readable format
|| Standardised vocabularies/ontologies/schema without global identifiers
|| The metadata record includes URI links to related metadata, data and definitions
|| Standard text based license
|| Fully recorded in a text format
||
|-
| Local identifier
||
|| Brief title and description
|| Domain-specific repository
|| A de-identified / modified subset of the data is publicly accessible
|| File download from online location
|| Unsure
|| Mostly in a proprietary format
|| No standards have been applied in the description of data elements
|| There are no links to other metadata
|| Non-standard machine-readably license (clearly indicating under what conditions the data may be reused)
|| Partially recorded
||
|-
| No identifier
||
|| The data is not described
|| Local institutional repository
|| Embaged access after a specific date
|| By individual arrangement
||
||
|| Data elements not described
||
|| Non-standard text-based license
|| No provenance information is recorded
||
|-
|
||
||
|| The data is not described in any repository
|| Unspecified conditional access e.g. contact the data custodian for access
|| No access to data
||
||
||
||
|| No license
||
||
|-
|
||
||
||
|| Access to metadata only
||
||
||
||
||
||
||
||
|-
|
||
||
||
|| No access to data or metadata
||
||
||
||
||
||
||
||
|}

At this point it is important to note that the assessment of databases and/or datasets using this tool is solely considered a human assessment. This may lead to different evaluations by different users. To counteract this phenomenon, it was suggested that the evaluations should be completed using the 'four-eyes principle' to create more verifiable results. Also, it is planned to additionally evaluate the respective databases using an external tool to perform a machine-assessment.

In addition, not all questions are weighted equally and thus a worse result for one question may not have a large impact whereas only choosing the second-best option in another question will bring down the overall FAIR value considerably.

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Description
|-
! rowspan="5" | F
|| F
|| About Findable
|| Making data Findable includes assigning a persistent identifier (like a DOI or Handle ), having rich metadata to describe the data and making sure it is findable through disciplinary and generalist discovery portals (local and international).

|-
| F1
|| About identifiers
|| Identifiers are essential for identifying, finding, retrieving, linking and citing datasets. A Web address (URL) can be used to specify the online location of a resource but over time URLs tend to change which leads to broken links to the data. To be useful, identifiers need to be persistent and unique. Digital Object Identifiers (DOIs) and other persistent identifiers (PIDs) provide a permanent citable reference to a particular dataset. The DOI is a permanent fixed reference to the dataset no matter where it is located online and enables citation and citation metrics. Services to create a persistent identifier are often offered by your affiliated institution or the repository you are using to describe your data. Talk to your library service for more information.
|-
| F2
|| About identifiers
|| The identifier (preferably a persistent identifier) needs to be clearly stated in the metadata record describing the data collection, and also in any associated data files or metadata.

|-
|F3
|| About metadata descriptions
|| Comprehensive metadata records will include descriptive content that facilitates discovery, access and reuse of the data being described. While there is no 'one size fits all' list, comprehensive metadata should include:
* a globally unique persistent identifier e.g. a DOI
* a title
* related people, i.e. the data creator or custodian
* how to access the data and file formats
* a description of how the data were created and how to interpret the data subject or keywords
* citation information that clearly indicates how the data should be cited
* a machine-readable data licence
* provenance and contextual information such as:
* links to related publications, projects, services and software
* methodology and processes involved in data production
* spatial and temporal coverage (if relevant)
* object-level data description

Providing metadata in a standard schema allows it to be read and used by machines as well as humans.

|-
| F4
|| About metadata repositories
|| A rich metadata description alone does not ensure a dataset’s ‘findability’ on the internet; the dataset needs to be registered or indexed in a searchable resource, such as a generalist (e.g. Figshare, Dryad, Zenodo domain-specific (e.g. PANGAEA, ADA, ICPSR), or institutional (e.g. ANU Research Repository , Sydney eScholarship Repository, data.gov.au) data repository or registry. Ideally these repositories/registries are indexed by search engines such as Google and/or Google Scholar.

|-
! rowspan = "4" | A
| A
|| About Accessible
|| To make data accessible may include making the data open using a standardised protocol. However the data does not necessarily have to be open. There are sometimes good reasons why data cannot be made open, for example privacy concerns, national security or commercial interests. If it is not open there should be clarity and transparency around the conditions governing access and reuse
|-
| A1
|| About data accessibility
|| Not all data that is discoverable can be freely accessed. Often there are embargoes, access controls, and access permissions associated with data due to a variety of issues such as privacy and commercial interests. Even with all these issues much sensitive data can be shared. Many other issues that could be perceived as blockers to sharing data may be overcome.
|-
| A1.1/A1.2
|| About protocols
|| Ideally users would like to retrieve appropriate internet content directly and unhindered once they have located it. Internet protocols (e.g. http and ftp) define rules and conventions for communication between devices, and tools and services are available to facilitate this process, e.g. APIs.

Protocols:
* HTTP (Hypertext Transfer Protocol) is the set of rules for transferring files (text, graphic images, sound, video, and other multimedia files) on the World Wide Web. As soon as a Web user opens their Web browser, the user is indirectly making use of HTTP.
* FTP (File Transfer Protocol) is a standard Internet protocol for transmitting files between computers on the Internet over TCP/IP connections. Read more: Web protocols
* API (Application Programming Interface): When information is made available in any machine readable format, it becomes possible to make that information directly available to programs that request that information over the web. An API is the way this information is made directly available to other machines.

|-
| A2
|| About metadata availability
|| Effort is often required to maintain data resources online which can often lead to it being neglected especially over long periods of time. This often leads to broken links between the metadata and the data. Having at least a description of the data in the form of a metadata record means there is a record of the data's existence allowing the possibility of someone tracking it down.

|-
! rowspan="4" | I
|| I
|| About Interoperability
|| To be interoperable (i.e. data that is interpretable by a computer, so that they can be automatically combined with other data) the data will need to use community agreed formats, language and vocabularies. The metadata will also need to use a community agreed standards and vocabularies, and contain links to related information using identifiers.
|-
|| I1
|| About file formats
|| When selecting file formats for archiving, the formats should ideally be:
* Non-proprietary
* Unencrypted
* Uncompressed
* In common usage by the research community
* Adherent to an open, documented standard, such as described by the State of California (see AB 1668, 2007)
* Interoperable among diverse platforms and applications
* Fully published and available royalty-free
* Fully and independently implementable by multiple software providers on multiple platforms without any intellectual property restrictions for necessary technology
* Developed and maintained by an open standards organization with a well-defined inclusive process for evolution of the standard.

From: Stanford University Library - Best practices for file formats
File format examples:
* Structured, open standard, machine-readable format e.g. (text) PDF/A, HTML, Plain text, (images) TIFF, JPEG 2000, GIF, (audio) MP3, AIFF, WAVE, (video) MOV, MPEG, AVI, (Tabular data) CSV
* structured, open standard, non-machine-readable format, e.g. PDF, HTML, JPG
* Proprietary format, e.g. doc (Word), .xls (Excel), .ppt (PowerPoint), .sav

Read more:
* ANDS
* European Data Portal

|-
|| I2
|| About schema standards
|| Schema standards are schemas that have gone through a formal validation process by a standards organisation, such as the International Standards Organisation (ISO) or an equivalent body such as the Dublin Core Metadata Initiative (DCMI); or, commonly used and consistently applied metadata schemas that are well documented, endorsed, and maintained can also become ‘de-facto standards’, e.g. RIF-CS for describing data collections and services used in Research Data Australia.

Read more: [https://www.ands.org.au/guides/metadata-working Metadata schema]

Standardised open and universal schemas, e.g, [https://www.dcc.ac.uk/resources/metadata-standards/datacite-metadata-schema DataCite Metadata Schema], [https://www.dcc.ac.uk/resources/metadata-standards/prov PROV], [https://www.dcc.ac.uk/resources/metadata-standards/dublin-core Dublin Core]

Domain-specific standards, e.g. [http://human-phenotype-ontology.github.io/ HPO - Human Phenotype Ontology], [https://www.nlm.nih.gov/mesh/ MeSH - Medical Subject Headings] , [https://mcp-profile-docs.readthedocs.io/en/stable/ Marine Community Profile], [https://ddialliance.org/ DDI - Data Documentation Initiative].

For more examples of standards: [http://rd-alliance.github.io/metadata-directory/ Here].

Resolvable global identifiers - An identifier is any label used to name something uniquely (whether online or offline). URLs are an example of an identifier. So are serial numbers, and personal names. A persistent identifier is guaranteed to be managed and kept up to date over a defined time period.

Read more: [https://www.ands.org.au/guides/persistent-identifiers-awareness Persistent Identifiers]

|-
||I3
|| About metadata links
|| The goal is to create as many meaningful linkages as possible between (meta)data resources to enrich the contextual knowledge about the data, balanced against the time/energy involved in making a good data model.

'''Machine-readable (meta)data'''

(Meta)data in a format that can be automatically read and processed by a computer, such as [http://opendatahandbook.org/glossary/en/terms/csv/ CSV], [http://opendatahandbook.org/glossary/en/terms/json/ JSON], [http://opendatahandbook.org/glossary/en/terms/xml/ XML] etc. For more information: [http://opendatahandbook.org/glossary/en/terms/machine-readable/ Open Data Handbook]

'''Linked Data'''

Linked Data (also known as Linking Data) can be applied to improve the exploitation of the “Web of data.” The expression refers to the publishing of structured data in a way that typed links are created between data from different sources to provide a higher level of usability. By using Linked Data, it is possible to find other, related data. Structured data should meet four requirements to be called
Linked Data:

* URIs should be assigned to all entities of the dataset.
* HTTP URIs are required to ensure that all entities can be referenced and cited by users and user agents.
* Entities should be described using standard formats such as RDF/XML.
* Links should be created to other, related entity URIs.

All data that fulfil these requirements and are released for the public are called Linked Open Data (LOD). http://www.lesliesikos.com/semantic-web-machine-readable-structured-data-with-meaningful-annotations/

Read more under the heading (meta)data include qualified references to other (meta)data in the following resource: https://www.dtls.nl/fair-data/fair-principles-explained/

For more information on Linked Data standards - RDF - https://www.w3.org/RDF/

Other related linked data standards:
* OWL - https://www.w3.org/OWL/
* SKOS - https://www.w3.org/2004/02/skos/
* SPARQL - https://www.w3.org/TR/rdf-sparql-query/

|-
! rowspan="3" | R
|| R
|| About Reusable
|| Reusable data should maintain its initial richness. For example, it should not be abridged for the purpose of explaining the findings in one particular publication. It needs a clear machine-readable [https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse licence] and [https://www.ands.org.au/partners-and-communities/ands-communities/data-provenance-interest-group provenance] information on how the data was formed. It should also use discipline-specific data and [https://www.ands.org.au/working-with-data/metadata metadata] standards to give it rich contextual information that will allow for accurate interpretation and reuse.

|-
|| R1.1
|| About licenses
|| If data is not licensed no-one else can use it. In Australia, no licence is regarded as the same as 'all rights reserved', confining any reuse to very limited circumstances. Applying a Creative Commons licence to your data is a simple way to ensure that your data can be reused. The less restrictive the licence, the more that can be done with the data.
To make it easy for the Web to know when a work is available under a particular license, a “machine readable” version of the license provides a summary of the key freedoms and obligations written into a format that software systems, search engines, and other kinds of technology can understand.
Example from the [https://creativecommons.org/choose/ Creative Commons License Choser]:

What appears in the metadata:

This work is licensed under a [https://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International License].

Code snippet:

<code>
<a rel="license" href="http://creativecommons.org/licenses/by/4.0/">

<img alt="Creative Commons License" style="border-width:0"

src="https://i.creativecommons.org/l/by/4.0/88x31.png"/>

</a> This work is licensed under a

<a rel="license" href="http://creativecommons.org/licenses/by/4.0/">

Creative Commons Attribution 4.0 International License</a>.
<\code>

Read more: https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse

|-
||R1.2
|| About provenance
|| Data provenance is used to document where a piece of data comes from and the process and methodology by which it is produced. It is important to confirm the authenticity of data enabling trust, credibility and reproducibility. This is becoming increasingly important, especially in the eScience community where research is data intensive and often involves complex data transformations and procedures ([https://www.ands.org.au/partners-and-communities/ands-communities/data-provenance-interest-group Read more]). Provenance vocabularies such as [https://www.w3.org/2005/Incubator/prov/XGR-prov-20101214/#Recommendations Provenir Ontology (PROV-O) and others].

|}

== Results for SMILES and ShareWind ==

{| class="mw-collapsible wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Datasets from the SMILES and ShareWind platforms was analysed using the self-assessment tool provided by ARDC.
! rowspan="5" |
| rowspan="5" |
! Database Code
| rowspan="24" style="background: #ABE" |
| ED4
| ED9
|-
! Database Code
|| SMILES
| ShareWind
|-
! Subdata-Code
|| https://www.ecria-smiles.eu/
| https://sharewind.eu/
|-
! Database/Subdata
|| [https://www.ecria-smiles.eu/data-files/-/document_library/W32cCAfL2jMX/view_file664466?_com_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX_redirect=https%3A%2F%2Fwww.ecria-smiles.eu%3A443%2Fdata-files%2F-%2Fdocument_library%2FW32cCAfL2jMX%2Fview%2F664423%3F_com_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX_redirect%3Dhttps%253A%252F%252Fwww.ecria-smiles.eu%253A443%252Fdata-files%253Fp_p_id%253Dcom_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX%2526p_p_lifecycle%253D0%2526p_p_state%253Dnormal%2526p_p_mode%253Dview here] (link has 585 characters)
| https://sharewind.eu/records/f9d31abbc2824284b8c1d28894d9619a; https://zenodo.org/record/1162738
|-
! Sample Size
|| 4 KB
| 37 MB
|-
| colspan="6" style="background: #ABE" |
|-
! rowspan="4" | Findable
| F1. (meta)data are assigned a globally unique and persistent identifier
! Does the dataset have any identifiers assigned?
|| Web adress (URL)
| Globally unique, citable and persistant (e.g. DOI, PURL, ARK or Handle)
|-

| F2. data are described with rich metadata (defined by R1 below)
! Is the dataset identifier included in all metadata records/files describing the data?
|| No
| No
|-

| F3. metadata clearly and explicitly include the identifier of the data it describes
! How is the data described with metadata?
|| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
|-

| F4. (meta)data are registered or indexed in a searchable resource
! What type of repository or registry is the metadata record in?
|| Domain-specific repository
| Data is in one place but discoverable through several registries
|-

! rowspan="3" | Accessible

| A1. (meta)data are retrievable by their identifier using a standardized communications protocol
! How accessible is the data?
|| Publicly accessible
| Publicly accessible
|-
| A1.1 the protocol is open, free, and universally implementable; A1.2 the protocol allows for an authentication and authorization procedure, where necessary
! Is the data available online without requiring specialised protocols or tools once access has been approved?
|| File download from online location
| Standard web service API (e.g. OGC)
|-
| A2. metadata are accessible, even when the data are no longer available
! Will the metadata record be available even if the data is no longer available?
|| Unsure
| Yes
|-

! rowspan="3" | Interoperable
| I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
! What (file) format(s) is the data available in?
|| In a structured, open standard, machine-readable format
|| In a structured, open standard, non-machine-readable format
|-
| I2. (meta)data use vocabularies that follow FAIR principles
! What best describes the types of vocabularies/ontologies/tagging schemas used to define the data elements?
|| Standardised vocabularies/ontologies/schema without global identifiers
|| Standardised vocabularies/ontologies/schema without global identifiers
|-
| I3. (meta)data include qualified references to other (meta)data
! How is the metadata linked to other data and metadata (to enhance context and clearly indicate relationships)?
|| There are no links to other metadata
|| Metadata is represented in a machine readable format, e.g. in a linked data format such as Resource Description Framework (RDF)
|-

! rowspan="3" | Reusable
| R1.1. (meta)data are released with a clear and accessible data usage license
! Which of the following best describes the license/usage rights attached to the data?
|| No license
| Standard machine-readable license (e.g. Creative Commons)
|-
| R1.2. (meta)data are associated with detailed provenance
! How much provenance information has been captured to facilitate data reuse?
|| Partially recorded
|| Partially recorded
|-
| R1.3. (meta)data meet domain-relevant community standards
|
|
|
|-
| colspan="6" style="background: #ABE" |
|-
! Total across F.A.I.R
|
! Low: < 40%; Medium: 50-60%; High: > 60%
|| 45 %
| 85 %
|-
| colspan="6" style="background: #ABE" |
|-
! Remarks about database
||
||
||
|| The data is provided by the open-source platform Zenodo, not the project website itself. A very positive aspect is that information on different versions of the data as well as the number of views and downloads is available.
|-
! Identified gaps
||
||
||
* No DOI, only URL
* Extremely long URL link spanning several lines with 585 characters, that can easily become corrupted
* No links to other metadata
* No license information given
* Provenance not fully recorded

||
* Dataset identifiers should be included in the files describing the data
* Provenance details are only partially recorded with a note with contact name for further information
|}

= Dublin Core Evaluation =

== Dublin Core Elements ==
{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ The DCMI Metadata Terms lists the current set of the Dublin Core vocabulary. Source: [https://dublincore.org/specifications/dublin-core/dcmi-terms/ Dublin Core Metadata Initiative]
| 1 || abstract || 11 || contributor || 21 || extent || 31 || isReplacedBy || 41 || publisher || 51 || tableOfContents
|-
| 2 || accessRights || 12 || coverage || 22 || format || 32 || isRequiredBy || 42 || references || 52 || temporal
|-
| 3 || accrualMethod || 13 || created || 23 || hasFormat || 33 || issued || 43 || relation || 53 || title
|-
| 4 || accrualPeriodicity || 14 || creator || 24 || hasPart || 34 || isVersionOf || 44 || replaces || 54 || type
|-
| 5 || accrualPolicy || 15 || date || 25 || hasVersion || 35 || language || 45 || requires || 55 || valid
|-
| 6 || alternative || 16 || dateAccepted || 26 || identifier || 36 || license || 46 || rights || ||
|-
| 7 || audience || 17 || dateCopyrighted || 27 || instructionalMethod || 37 || mediator || 47 || rightsHolder || ||
|-
| 8 || available || 18 || dateSubmitted || 28 || isFormatOf || 38 || medium || 48 || source || ||
|-
| 9 || bibliographicCitation || 19 || description || 29 || isPartOf || 39 || modified || 49 || spatial || ||
|-
| 10 || conformsTo || 20 || educationLevel || 30 || isReferencedBy || 40 || provenance || 50 || subject || ||
|}

'''Metadata Category Types according to [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO]'''

There are three main types of metadata:
* Descriptive metadata describes a resource for purposes such as discovery and identification. It can include elements such as title, abstract, author, and keywords.
* Structural metadata indicates how compound objects are put together, for example, how pages are ordered to form chapters.
* Administrative metadata provides information to help manage a resource, such as when and how it was created, file type and other technical information, and who can access it. There are several subsets of administrative data; two that sometimes are listed as separate metadata types are:
** Rights management meta-data, which deals with intellectual property rights,and
** Preservation metadata, which contains information needed to archive and preserve a resource.
''

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Simple Dublin Core Elements. 15 DC elements with their (shortened) official definitions and examples.
! DC element name !! DC definition ([https://www.dublincore.org/specifications/dublin-core/dces/ DCMI]) !! Example (Energy Domain) !! Category (determined with the aid of [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO])
|-
! Title
| A name given to the resource || Outdoor monitoring of a hybrid micro-CPV solar panel with integrated micro-tracking and diffuse capture || Descriptive
|-
! Subject
| The topic of the resource || concentrator photovoltaics; CPV; rooftop photovoltaics; integrated tracking || Descriptive
|-
! Description
| An account of the resource || Dataset from the outdoor characterization of a B Series module from Insolight at the rooftop of the Instituto de Energía Solar - Universidad Politécnica de Madrid. …. || Descriptive
|-
! Creator
| An entity primarily responsible for making the resource || Stephen Askins; Gaël Nardin || Descriptive
|-
! Publisher
| An entity responsible for making the resource available || Stephen Askins || Descriptive
|-
! Contributor
| An entity responsible for making contributions to the resource || Stephen Askins || Descriptive
|-
! Date
| A point or period of time associated with an event in the lifecycle of the resource || 2019-07-23; Date will be associated with the creation or availability of the resource. || Administrative
|-
! Type
| The nature or genre of the resource || info:eu-repo/semantics/other; dataset || Descriptive
|-
! Format
| The file format, physical medium, or dimensions of the resource || image
|| Structural
|-
! Identifier
| An unambiguous reference to the resource within a given context || https://zenodo.org/record/3346823; 10.5281/zenodo.3346823; oai:zenodo.org:3346823 || Descriptive
|-
! Source
| A related resource from which the described resource is derived || RC607.A26W574 1996 [where "RC607.A26W574 1996" is the call number of the print version of the resource, from which the present version was scanned] || Descriptive
|-
! Language
| A language of the resource || eng || Descriptive
|-
! Relation
| A related resource || info:eu-repo/grantAgreement/EC/H2020/787289/ || Descriptive
|-
! Coverage
| The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant || 1995-1996; Madrid, Spain || Descriptive
|-
! Rights
| Information about rights held in and over the resource || info:eu-repo/semantics/openAccess; https://creativecommons.org/licenses/by/4.0/legalcode || Administrative
|}

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Non-DC elements

! Non-DC element name !! Definition !! Example !! Category
|-
! Date of availability
| Since when is the data available
||
|| Impact/Exploitation

|-
! Number of users
| Is there any information on the number of accesses or users?
||
|| Impact/Exploitation

|-
! Number of scientific publications where data is cited/used
| Is there any information on the number of citations/uses?
||
|| Impact/Exploitation

|-
! Curation activities (is the versioning ongoing)
| Update, maintainance
||
|| Impact/Exploitation
|}

Attempting to put a value on Impact/Exploitation:
* '''ENTSO-E''': Found 172 results in Advanced Search (ALL) in Web of Science ([https://apps.webofknowledge.com/ WoSc]) (16/06/2020). Citations of all these search result add up to 714. This however does not mean, that all search results really explicitly used data from ENTSO-E. Some (for sure, for some were found) only mentioned it saying they adoped workflow etc. from the site.
* '''SMARD''': Found 82 results in Advanced Search (ALL=(SMARD)) in Web of Science ([https://apps.webofknowledge.com/ WoSc]) (16/06/2020). However: SMARD is also an acronym for ''Spinal muscular atrophy with respiratory distress (SMARD)''. This somewhat complicates results and needs some further discussion. All 82 search results accumumlated 2359 citations themselves.

Gap analysis

2020-08-27T15:06:25Z

Christopherbs: /* FAIR Self-Assessment tool */

<accesscontrol>Administrators,consortium</accesscontrol>

This is the page for the gap analysis.

= Tacit knowledge "FAIRification and opening of low carbon energy research data" =
 Consortium members can add any time, they feel that something is important to note even though it is not mentioned in a standard deliverable. In other words, this page collects uncodified, tacit knowledge. It will help us later to compile suggestions and lessons learned. This kind of knowledge is collected two-fold:
#Anytime if someone feels that this should be noted. Please write down: Issue, Date, Author (could also be "anonymous"), the issue described in a few words or maximal lines>
#During the final day of workshops

=== Learning process: ===

*People are hesitant to adopt new IT technologies, this is even the case among researchers heavily relying on data, algorithms and collaborative online software (e.g., R, platforms, online conferencing, HPC, ...). The effort to encourage change is not to underestimate. Reasons are several, notably, lack of time and uncertainty about potential benefit as well as overall risk aversion preferences. EERAdata is using the online software "Only Office" to facilitate collaboration (in particular also during the Covid-19 period).
 '''FAIR and open criteria:'''
*Consortium members have a fair understanding of what FAIR/O is, but there is little knowledge and/or technical experience on how to approach the FAIRification and opening. All, however, share the view that we are at a critical point in time, where we need to implement these criteria. 
*To deepen knowledge about FAIR/O criteria, it is useful to test the criteria on a database one is familiar with. For this purpose (and to start brainstorming about the platform), AIT has developed a questionnaire for application in the use cases.
'''Metadata:'''
*A good starting point is to think about metadata and to look into existing metadata concepts in one's field. The first step is to understand that also metadata need to adhere to the FAIR/O principles.
*The next step is to increase knowledge on IT specific terms, i.e. to understand what the difference is between different '''[https://energydata.onlyoffice.eu/Products/Files/doceditor.aspx?fileid=5404340 metadata frameworks] (taxonomy, thesaurus, ontology) as well as classification of metadata (high-level, medium-level, low-level OR administrative, structural and descriptive metadata).  '''WP 2 being in charge of aligning approaches between use cases, participates in all use case kickoffs to bring everybody on the same page. The presentation is linked with "metadata frameworks".
*It is useful to supply consortium members with read aheads and watch aheads on metadata to prepare the '''[https://energydata.onlyoffice.eu/Products/Files/doceditor.aspx?fileid=5296590 first EERAdata workshop]'''. The workshop starts applications and discussions in the use cases break out sessions (and bringing insights back to the plenary), using selected databases.

= FAIR Evaluation=

== FAIR Guiding Principles ==
{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ This table shows the FAIR guiding principles described by [https://www.doi.org/10.1038/sdata.2016.18 Wilkinson et al., 2016].

! To be Findable
|
* F1. (meta)data are assigned a globally unique and persistent identifier
* F2. data are described with rich metadata (defined by R1 below)
* F3. metadata clearly and explicitly include the identifier of the data it describes
* F4. (meta)data are registered or indexed in a searchable resource

|-
! To be Accessible
|
* A1. (meta)data are retrievable by their identifier using a standardized communications protocol
** A1.1 the protocol is open, free, and universally implementable
** A1.2 the protocol allows for an authentication and authorization procedure, where necessary
* A2. metadata are accessible, even when the data are no longer available

|-
! To be Interoperable
|
* I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
* I2. (meta)data use vocabularies that follow FAIR principles
* I3. (meta)data include qualified references to other (meta)data

|-
! To be Reusable
|
* R1. meta(data) are richly described with a plurality of accurate and relevant attributes
* R1.1. (meta)data are released with a clear and accessible data usage license
* R1.2. (meta)data are associated with detailed provenance
* R1.3. (meta)data meet domain-relevant community standards

|}

== Identified gaps after Workshop 1 (02/06/2020 – 04/06/2020) ==

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ This table shows a summary of the specific issues that were identified in Workshop 1. The aim was to categorise different issues to get a better understanding of how to tackle these challenges.
! FAIR/O !! Specific FAIR/O !! Gaps !! Gaps for energy domain !! Use case specific gaps !! Consequences for researchers
|-
! F
| F2
|| Metadata scope
|| Different fields require different metadata (potentially very specific)
|| UC3: lack of additional metadata for applications of materials
|| Data are less useful for the specific field
|-
! F
|| F1/F3
|| Identifiers
||
* Missing identifiers on websites
* Identifier not in downloaded data files
||
|| Makes research more difficult.
|-
! I
| I2
|| Taxonomy/ontology/common vocabulary and language issues
||
* Lack of standardisation
* Heterogeneous data makes standardisation hard
* No vocabulary documentation on websites
* Words used for same term in other languages may differ
* Databases in other languages than English
||
|| Research costs more time
|-
! I
| I3
|| References to other (meta)data
||
* No linking to source documents and related publications (for contextual knowledge)
* Possible need for links to other fields
|| UC3: link between microscopic and macroscopic materials (e.g., turbine blades)
||
* Data cannot be connected to the source
* Makes research more difficult.

|-
! R
| R1.1
|| Licensing
||
* No licenses available may mean the data is not reusable for the researcher
* Licenses not clear and accessible
* Obtaining licenses may result in more effort and costs
||
|| Data potentially not reusable

|-
! R
| R1.2
|| Reliability and provenance of data
|| When the data is collected from different and high number of sources, its reliability decreases
||
|| Data potentially not reusable

|-
! O
|
|| Privacy concerns and expected disadvantages
|| Not publishing data due to privacy concerns (sensitive data)
|| UC2: In case of distribution network data this is relevant
|| Data not accessible

|-
! Other
|
|| Conducting FAIR assessments
||
* No qualitative assessment of the data itself (which may be limited)
* Discrepancies between results conducted by humans versus machines
* Sometimes not even DC standards are met
* Metadata is not updated
* Lacking encryption of websites (https)
* Problems assessing whether metadata will be available after data is unavailable
* Interface design/layout may be unclear, incomplete or not intuitive for humans
* Authentication details (user registration login / good or bad, clarify)

||
* UC1 lack of data availability for time-series
||
* Assessment is uncertain (human assessment may need more clarification and understanding)
* Makes research more time-intensive

|}

== FAIR Self-Assessment tool ==

To conduct a human FAIR evaluation for different databases and their respective datasets, different free online tools were considered. Some seemed to be quite useful (e.g. [https://satifyd.dans.knaw.nl/ SATISFYD] ) and some were simple self-evaluation PDFs to be printed and filled out.

We decided to chose the [https://ardc.edu.au/resources/working-with-data/fair-data/fair-self-assessment-tool/ self-assessment tool] provided by the Australian Research Data Commons (ARDC). The decision was based manly upon the fact that the tools questions closely matched the FAIR categories specified by [https://www.doi.org/10.1038/sdata.2016.18 Wilkinson et al., 2016] which would make it easily comparable to the existing preferable FAIR conditions and aid with the repeatability of the results, and the half-automated scoring features that show the extent of the "FAIRness" of the analysed sample as a simple bar chart.

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ All options for each question of the ARDCs self-assessment tool
!colspan="4"|Findable
!colspan="3"|Accessible
!colspan="3"|Interoperable
!colspan="3"|Reusable
|-
| F1. (meta)data are assigned a globally unique and persistent identifier
|| F2. data are described with rich metadata (defined by R1 below)
|| F3. metadata clearly and explicitly include the identifier of the data it describes
|| F4. (meta)data are registered or indexed in a searchable resource
|| A1. (meta)data are retrievable by their identifier using a standardized communications protocol
|| A1.1 the protocol is open, free, and universally implementable and A1.2 the protocol allows for an authentication and authorization procedure, where necessary
|| A2. metadata are accessible, even when the data are no longer available
|| I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
|| I2. (meta)data use vocabularies that follow FAIR principles
|| I3. (meta)data include qualified references to other (meta)data
|| R1.1. (meta)data are released with a clear and accessible data usage license
|| R1.2. (meta)data are associated with detailed provenance
|| R1.3. (meta)data meet domain-relevant community standards
|-
! Does the dataset have any identifiers assigned?
! Is the dataset identifier included in all metadata records/files describing the data?
! How is the data described with metadata?
! What type of repository or registry is the metadata record in?
! How accessible is the data?
! Is the data available online without requiring specialised protocols or tools once access has been approved?
! Will the metadata record be available even if the data is no longer available?
! What (file) format(s) is the data available in?
! What best describes the types of vocabularies/ontologies/tagging schemas used to define the data elements?
! How is the metadata linked to other data and metadata (to enhance context and clearly indicate relationships)?
! Which of the following best describes the license/usage rights attached to the data?
! How much provenance information has been captured to facilitate data reuse?
!
|-
| Globally unique, citable and persistant (e.g. DOI, PURL, ARK or Handle)
|| Yes
|| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
|| Data is in one place but discoverable through several registries
|| Publicly accessible
|| Standard web service API (e.g. OGC)
|| Yes
|| In a structured, open standard, machine-readable format
|| Standardised open and universal using resolvable global identifiers linking to explainations
|| Meadata is represented in a machine readable format, e.g. in a linked data format such as Resource Description Framework (RDF)
|| Standard machine-readable license (e.g. Creative Commons)
|| Fully recorded in a machine-readable format
||
|-
| Web adress (URL)
|| No
|| Comprehensively, but in a text-based, non-standard format
|| Generalist public repository
|| Fully accessible to person who meet explicitly stated conditions, e.g. ethics approval for sensitive data
|| Non-standard web service (e.g. OpenAPI/Swagger/informal API)
|| No
|| In a structured, open standard, non-machine-readable format
|| Standardised vocabularies/ontologies/schema without global identifiers
|| The metadata record includes URI links to related metadata, data and definitions
|| Standard text based license
|| Fully recorded in a text format
||
|-
| Local identifier
||
|| Brief title and description
|| Domain-specific repository
|| A de-identified / modified subset of the data is publicly accessible
|| File download from online location
|| Unsure
|| Mostly in a proprietary format
|| No standards have been applied in the description of data elements
|| There are no links to other metadata
|| Non-standard machine-readably license (clearly indicating under what conditions the data may be reused)
|| Partially recorded
||
|-
| No identifier
||
|| The data is not described
|| Local institutional repository
|| Embaged access after a specific date
|| By individual arrangement
||
||
|| Data elements not described
||
|| Non-standard text-based license
|| No provenance information is recorded
||
|-
|
||
||
|| The data is not described in any repository
|| Unspecified conditional access e.g. contact the data custodian for access
|| No access to data
||
||
||
||
|| No license
||
||
|-
|
||
||
||
|| Access to metadata only
||
||
||
||
||
||
||
||
|-
|
||
||
||
|| No access to data or metadata
||
||
||
||
||
||
||
||
|}

At this point it is important to note that the assessment of databases and/or datasets using this tool is solely considered a human assessment. This may lead to different evaluations by different users. To counteract this phenomenon, it was suggested that the evaluations should be completed using the 'four-eyes principle' to create more verifiable results. Also, it is planned to additionally evaluate the respective databases using an external tool to perform a machine-assessment.

In addition, not all questions are weighted equally and thus a worse result for one question may not have a large impact whereas only choosing the second-best option in another question will bring down the overall FAIR value considerably.

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Description
|-
! rowspan="5" | F
|| F
|| About Findable
|| Making data Findable includes assigning a persistent identifier (like a DOI or Handle ), having rich metadata to describe the data and making sure it is findable through disciplinary and generalist discovery portals (local and international).

|-
| F1
|| About identifiers
|| Identifiers are essential for identifying, finding, retrieving, linking and citing datasets. A Web address (URL) can be used to specify the online location of a resource but over time URLs tend to change which leads to broken links to the data. To be useful, identifiers need to be persistent and unique. Digital Object Identifiers (DOIs) and other persistent identifiers (PIDs) provide a permanent citable reference to a particular dataset. The DOI is a permanent fixed reference to the dataset no matter where it is located online and enables citation and citation metrics. Services to create a persistent identifier are often offered by your affiliated institution or the repository you are using to describe your data. Talk to your library service for more information.
|-
| F2
|| About identifiers
|| The identifier (preferably a persistent identifier) needs to be clearly stated in the metadata record describing the data collection, and also in any associated data files or metadata.

|-
|F3
|| About metadata descriptions
|| Comprehensive metadata records will include descriptive content that facilitates discovery, access and reuse of the data being described. While there is no 'one size fits all' list, comprehensive metadata should include:
* a globally unique persistent identifier e.g. a DOI
* a title
* related people, i.e. the data creator or custodian
* how to access the data and file formats
* a description of how the data were created and how to interpret the data subject or keywords
* citation information that clearly indicates how the data should be cited
* a machine-readable data licence
* provenance and contextual information such as:
* links to related publications, projects, services and software
* methodology and processes involved in data production
* spatial and temporal coverage (if relevant)
* object-level data description

Providing metadata in a standard schema allows it to be read and used by machines as well as humans.

|-
| F4
|| About metadata repositories
|| A rich metadata description alone does not ensure a dataset’s ‘findability’ on the internet; the dataset needs to be registered or indexed in a searchable resource, such as a generalist (e.g. Figshare, Dryad, Zenodo domain-specific (e.g. PANGAEA, ADA, ICPSR), or institutional (e.g. ANU Research Repository , Sydney eScholarship Repository, data.gov.au) data repository or registry. Ideally these repositories/registries are indexed by search engines such as Google and/or Google Scholar.

|-
! rowspan = "4" | A
| A
|| About Accessible
|| To make data accessible may include making the data open using a standardised protocol. However the data does not necessarily have to be open. There are sometimes good reasons why data cannot be made open, for example privacy concerns, national security or commercial interests. If it is not open there should be clarity and transparency around the conditions governing access and reuse
|-
| A1
|| About data accessibility
|| Not all data that is discoverable can be freely accessed. Often there are embargoes, access controls, and access permissions associated with data due to a variety of issues such as privacy and commercial interests. Even with all these issues much sensitive data can be shared. Many other issues that could be perceived as blockers to sharing data may be overcome.
|-
| A1.1/A1.2
|| About protocols
|| Ideally users would like to retrieve appropriate internet content directly and unhindered once they have located it. Internet protocols (e.g. http and ftp) define rules and conventions for communication between devices, and tools and services are available to facilitate this process, e.g. APIs.

Protocols:
* HTTP (Hypertext Transfer Protocol) is the set of rules for transferring files (text, graphic images, sound, video, and other multimedia files) on the World Wide Web. As soon as a Web user opens their Web browser, the user is indirectly making use of HTTP.
* FTP (File Transfer Protocol) is a standard Internet protocol for transmitting files between computers on the Internet over TCP/IP connections. Read more: Web protocols
* API (Application Programming Interface): When information is made available in any machine readable format, it becomes possible to make that information directly available to programs that request that information over the web. An API is the way this information is made directly available to other machines.

|-
| A2
|| About metadata availability
|| Effort is often required to maintain data resources online which can often lead to it being neglected especially over long periods of time. This often leads to broken links between the metadata and the data. Having at least a description of the data in the form of a metadata record means there is a record of the data's existence allowing the possibility of someone tracking it down.

|-
! rowspan="4" | I
|| I
|| About Interoperability
|| To be interoperable (i.e. data that is interpretable by a computer, so that they can be automatically combined with other data) the data will need to use community agreed formats, language and vocabularies. The metadata will also need to use a community agreed standards and vocabularies, and contain links to related information using identifiers.
|-
|| I1
|| About file formats
|| When selecting file formats for archiving, the formats should ideally be:
* Non-proprietary
* Unencrypted
* Uncompressed
* In common usage by the research community
* Adherent to an open, documented standard, such as described by the State of California (see AB 1668, 2007)
* Interoperable among diverse platforms and applications
* Fully published and available royalty-free
* Fully and independently implementable by multiple software providers on multiple platforms without any intellectual property restrictions for necessary technology
* Developed and maintained by an open standards organization with a well-defined inclusive process for evolution of the standard.

From: Stanford University Library - Best practices for file formats
File format examples:
* Structured, open standard, machine-readable format e.g. (text) PDF/A, HTML, Plain text, (images) TIFF, JPEG 2000, GIF, (audio) MP3, AIFF, WAVE, (video) MOV, MPEG, AVI, (Tabular data) CSV
* structured, open standard, non-machine-readable format, e.g. PDF, HTML, JPG
* Proprietary format, e.g. doc (Word), .xls (Excel), .ppt (PowerPoint), .sav

Read more:
* ANDS
* European Data Portal

|-
|| I2
|| About schema standards
|| Schema standards are schemas that have gone through a formal validation process by a standards organisation, such as the International Standards Organisation (ISO) or an equivalent body such as the Dublin Core Metadata Initiative (DCMI); or, commonly used and consistently applied metadata schemas that are well documented, endorsed, and maintained can also become ‘de-facto standards’, e.g. RIF-CS for describing data collections and services used in Research Data Australia.

Read more: [https://www.ands.org.au/guides/metadata-working Metadata schema]

Standardised open and universal schemas, e.g, [https://www.dcc.ac.uk/resources/metadata-standards/datacite-metadata-schema DataCite Metadata Schema], [https://www.dcc.ac.uk/resources/metadata-standards/prov PROV], [https://www.dcc.ac.uk/resources/metadata-standards/dublin-core Dublin Core]

Domain-specific standards, e.g. [http://human-phenotype-ontology.github.io/ HPO - Human Phenotype Ontology], [https://www.nlm.nih.gov/mesh/ MeSH - Medical Subject Headings] , [https://mcp-profile-docs.readthedocs.io/en/stable/ Marine Community Profile], [https://ddialliance.org/ DDI - Data Documentation Initiative].

For more examples of standards: [http://rd-alliance.github.io/metadata-directory/ Here].

Resolvable global identifiers - An identifier is any label used to name something uniquely (whether online or offline). URLs are an example of an identifier. So are serial numbers, and personal names. A persistent identifier is guaranteed to be managed and kept up to date over a defined time period.

Read more: [https://www.ands.org.au/guides/persistent-identifiers-awareness Persistent Identifiers]

|-
||I3
|| About metadata links
|| The goal is to create as many meaningful linkages as possible between (meta)data resources to enrich the contextual knowledge about the data, balanced against the time/energy involved in making a good data model.

'''Machine-readable (meta)data'''

(Meta)data in a format that can be automatically read and processed by a computer, such as [http://opendatahandbook.org/glossary/en/terms/csv/ CSV], [http://opendatahandbook.org/glossary/en/terms/json/ JSON], [http://opendatahandbook.org/glossary/en/terms/xml/ XML] etc. For more information: [http://opendatahandbook.org/glossary/en/terms/machine-readable/ Open Data Handbook]

'''Linked Data'''

Linked Data (also known as Linking Data) can be applied to improve the exploitation of the “Web of data.” The expression refers to the publishing of structured data in a way that typed links are created between data from different sources to provide a higher level of usability. By using Linked Data, it is possible to find other, related data. Structured data should meet four requirements to be called
Linked Data:

* URIs should be assigned to all entities of the dataset.
* HTTP URIs are required to ensure that all entities can be referenced and cited by users and user agents.
* Entities should be described using standard formats such as RDF/XML.
* Links should be created to other, related entity URIs.

All data that fulfil these requirements and are released for the public are called Linked Open Data (LOD). http://www.lesliesikos.com/semantic-web-machine-readable-structured-data-with-meaningful-annotations/

Read more under the heading (meta)data include qualified references to other (meta)data in the following resource: https://www.dtls.nl/fair-data/fair-principles-explained/

For more information on Linked Data standards - RDF - https://www.w3.org/RDF/

Other related linked data standards:
* OWL - https://www.w3.org/OWL/
* SKOS - https://www.w3.org/2004/02/skos/
* SPARQL - https://www.w3.org/TR/rdf-sparql-query/

|-
! rowspan="3" | R
|| R
|| About Reusable
|| Reusable data should maintain its initial richness. For example, it should not be abridged for the purpose of explaining the findings in one particular publication. It needs a clear machine-readable [https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse licence] and [https://www.ands.org.au/partners-and-communities/ands-communities/data-provenance-interest-group provenance] information on how the data was formed. It should also use discipline-specific data and [https://www.ands.org.au/working-with-data/metadata metadata] standards to give it rich contextual information that will allow for accurate interpretation and reuse.

|-
|| R1.1
|| About licenses
|| If data is not licensed no-one else can use it. In Australia, no licence is regarded as the same as 'all rights reserved', confining any reuse to very limited circumstances. Applying a Creative Commons licence to your data is a simple way to ensure that your data can be reused. The less restrictive the licence, the more that can be done with the data.
To make it easy for the Web to know when a work is available under a particular license, a “machine readable” version of the license provides a summary of the key freedoms and obligations written into a format that software systems, search engines, and other kinds of technology can understand.
Example from the [https://creativecommons.org/choose/ Creative Commons License Choser]:

What appears in the metadata:

This work is licensed under a [https://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International License].

Code snippet:

<code>
<a rel="license" href="http://creativecommons.org/licenses/by/4.0/">

<img alt="Creative Commons License" style="border-width:0"

src="https://i.creativecommons.org/l/by/4.0/88x31.png"/>

</a> This work is licensed under a

<a rel="license" href="http://creativecommons.org/licenses/by/4.0/">

Creative Commons Attribution 4.0 International License</a>.
<\code>

Read more: https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse

|-
||R1.2
|| About provenance
|| Data provenance is used to document where a piece of data comes from and the process and methodology by which it is produced. It is important to confirm the authenticity of data enabling trust, credibility and reproducibility. This is becoming increasingly important, especially in the eScience community where research is data intensive and often involves complex data transformations and procedures ([https://www.ands.org.au/partners-and-communities/ands-communities/data-provenance-interest-group Read more]). Provenance vocabularies such as [https://www.w3.org/2005/Incubator/prov/XGR-prov-20101214/#Recommendations Provenir Ontology (PROV-O) and others].

|}

== Results for ShareWind ==

{| class="mw-collapsible wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ A dataset from the ShareWind platform was analysed using the self-assessment tool provided by ARDC.
! rowspan="5" |
| rowspan="5" |
! Database Code
| rowspan="24" style="background: #ABE" |
| ED4
| ED9
|-
! Database Code
|| SMILES
| ShareWind
|-
! Subdata-Code
|| https://www.ecria-smiles.eu/
| https://sharewind.eu/
|-
! Database/Subdata
|| [https://www.ecria-smiles.eu/data-files/-/document_library/W32cCAfL2jMX/view_file664466?_com_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX_redirect=https%3A%2F%2Fwww.ecria-smiles.eu%3A443%2Fdata-files%2F-%2Fdocument_library%2FW32cCAfL2jMX%2Fview%2F664423%3F_com_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX_redirect%3Dhttps%253A%252F%252Fwww.ecria-smiles.eu%253A443%252Fdata-files%253Fp_p_id%253Dcom_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX%2526p_p_lifecycle%253D0%2526p_p_state%253Dnormal%2526p_p_mode%253Dview here] (link has 585 characters)
| https://sharewind.eu/records/f9d31abbc2824284b8c1d28894d9619a; https://zenodo.org/record/1162738
|-
! Sample Size
|| 4 KB
| 37 MB
|-
| colspan="6" style="background: #ABE" |
|-
! rowspan="4" | Findable
| F1. (meta)data are assigned a globally unique and persistent identifier
! Does the dataset have any identifiers assigned?
|| Web adress (URL)
| Globally unique, citable and persistant (e.g. DOI, PURL, ARK or Handle)
|-

| F2. data are described with rich metadata (defined by R1 below)
! Is the dataset identifier included in all metadata records/files describing the data?
|| No
| No
|-

| F3. metadata clearly and explicitly include the identifier of the data it describes
! How is the data described with metadata?
|| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
|-

| F4. (meta)data are registered or indexed in a searchable resource
! What type of repository or registry is the metadata record in?
|| Domain-specific repository
| Data is in one place but discoverable through several registries
|-

! rowspan="3" | Accessible

| A1. (meta)data are retrievable by their identifier using a standardized communications protocol
! How accessible is the data?
|| Publicly accessible
| Publicly accessible
|-
| A1.1 the protocol is open, free, and universally implementable; A1.2 the protocol allows for an authentication and authorization procedure, where necessary
! Is the data available online without requiring specialised protocols or tools once access has been approved?
|| File download from online location
| Standard web service API (e.g. OGC)
|-
| A2. metadata are accessible, even when the data are no longer available
! Will the metadata record be available even if the data is no longer available?
|| Unsure
| Yes
|-

! rowspan="3" | Interoperable
| I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
! What (file) format(s) is the data available in?
|| In a structured, open standard, machine-readable format
|| In a structured, open standard, non-machine-readable format
|-
| I2. (meta)data use vocabularies that follow FAIR principles
! What best describes the types of vocabularies/ontologies/tagging schemas used to define the data elements?
|| Standardised vocabularies/ontologies/schema without global identifiers
|| Standardised vocabularies/ontologies/schema without global identifiers
|-
| I3. (meta)data include qualified references to other (meta)data
! How is the metadata linked to other data and metadata (to enhance context and clearly indicate relationships)?
|| There are no links to other metadata
|| Metadata is represented in a machine readable format, e.g. in a linked data format such as Resource Description Framework (RDF)
|-

! rowspan="3" | Reusable
| R1.1. (meta)data are released with a clear and accessible data usage license
! Which of the following best describes the license/usage rights attached to the data?
|| No license
| Standard machine-readable license (e.g. Creative Commons)
|-
| R1.2. (meta)data are associated with detailed provenance
! How much provenance information has been captured to facilitate data reuse?
|| Partially recorded
|| Partially recorded
|-
| R1.3. (meta)data meet domain-relevant community standards
|
|
|
|-
| colspan="6" style="background: #ABE" |
|-
! Total across F.A.I.R
|
! Low: < 40%; Medium: 50-60%; High: > 60%
|| 45 %
| 85 %
|-
| colspan="6" style="background: #ABE" |
|-
! Remarks about database
||
||
||
|| The data is provided by the open-source platform Zenodo, not the project website itself. A very positive aspect is that information on different versions of the data as well as the number of views and downloads is available.
|-
! Identified gaps
||
||
||
* No DOI, only URL
* Extremely long URL link spanning several lines with 585 characters, that can easily become corrupted
* No links to other metadata
* No license information given
* Provenance not fully recorded

||
* Dataset identifiers should be included in the files describing the data
* Provenance details are only partially recorded with a note with contact name for further information
|}

= Dublin Core Evaluation =

== Dublin Core Elements ==
{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ The DCMI Metadata Terms lists the current set of the Dublin Core vocabulary. Source: [https://dublincore.org/specifications/dublin-core/dcmi-terms/ Dublin Core Metadata Initiative]
| 1 || abstract || 11 || contributor || 21 || extent || 31 || isReplacedBy || 41 || publisher || 51 || tableOfContents
|-
| 2 || accessRights || 12 || coverage || 22 || format || 32 || isRequiredBy || 42 || references || 52 || temporal
|-
| 3 || accrualMethod || 13 || created || 23 || hasFormat || 33 || issued || 43 || relation || 53 || title
|-
| 4 || accrualPeriodicity || 14 || creator || 24 || hasPart || 34 || isVersionOf || 44 || replaces || 54 || type
|-
| 5 || accrualPolicy || 15 || date || 25 || hasVersion || 35 || language || 45 || requires || 55 || valid
|-
| 6 || alternative || 16 || dateAccepted || 26 || identifier || 36 || license || 46 || rights || ||
|-
| 7 || audience || 17 || dateCopyrighted || 27 || instructionalMethod || 37 || mediator || 47 || rightsHolder || ||
|-
| 8 || available || 18 || dateSubmitted || 28 || isFormatOf || 38 || medium || 48 || source || ||
|-
| 9 || bibliographicCitation || 19 || description || 29 || isPartOf || 39 || modified || 49 || spatial || ||
|-
| 10 || conformsTo || 20 || educationLevel || 30 || isReferencedBy || 40 || provenance || 50 || subject || ||
|}

'''Metadata Category Types according to [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO]'''

There are three main types of metadata:
* Descriptive metadata describes a resource for purposes such as discovery and identification. It can include elements such as title, abstract, author, and keywords.
* Structural metadata indicates how compound objects are put together, for example, how pages are ordered to form chapters.
* Administrative metadata provides information to help manage a resource, such as when and how it was created, file type and other technical information, and who can access it. There are several subsets of administrative data; two that sometimes are listed as separate metadata types are:
** Rights management meta-data, which deals with intellectual property rights,and
** Preservation metadata, which contains information needed to archive and preserve a resource.
''

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Simple Dublin Core Elements. 15 DC elements with their (shortened) official definitions and examples.
! DC element name !! DC definition ([https://www.dublincore.org/specifications/dublin-core/dces/ DCMI]) !! Example (Energy Domain) !! Category (determined with the aid of [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO])
|-
! Title
| A name given to the resource || Outdoor monitoring of a hybrid micro-CPV solar panel with integrated micro-tracking and diffuse capture || Descriptive
|-
! Subject
| The topic of the resource || concentrator photovoltaics; CPV; rooftop photovoltaics; integrated tracking || Descriptive
|-
! Description
| An account of the resource || Dataset from the outdoor characterization of a B Series module from Insolight at the rooftop of the Instituto de Energía Solar - Universidad Politécnica de Madrid. …. || Descriptive
|-
! Creator
| An entity primarily responsible for making the resource || Stephen Askins; Gaël Nardin || Descriptive
|-
! Publisher
| An entity responsible for making the resource available || Stephen Askins || Descriptive
|-
! Contributor
| An entity responsible for making contributions to the resource || Stephen Askins || Descriptive
|-
! Date
| A point or period of time associated with an event in the lifecycle of the resource || 2019-07-23; Date will be associated with the creation or availability of the resource. || Administrative
|-
! Type
| The nature or genre of the resource || info:eu-repo/semantics/other; dataset || Descriptive
|-
! Format
| The file format, physical medium, or dimensions of the resource || image
|| Structural
|-
! Identifier
| An unambiguous reference to the resource within a given context || https://zenodo.org/record/3346823; 10.5281/zenodo.3346823; oai:zenodo.org:3346823 || Descriptive
|-
! Source
| A related resource from which the described resource is derived || RC607.A26W574 1996 [where "RC607.A26W574 1996" is the call number of the print version of the resource, from which the present version was scanned] || Descriptive
|-
! Language
| A language of the resource || eng || Descriptive
|-
! Relation
| A related resource || info:eu-repo/grantAgreement/EC/H2020/787289/ || Descriptive
|-
! Coverage
| The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant || 1995-1996; Madrid, Spain || Descriptive
|-
! Rights
| Information about rights held in and over the resource || info:eu-repo/semantics/openAccess; https://creativecommons.org/licenses/by/4.0/legalcode || Administrative
|}

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Non-DC elements

! Non-DC element name !! Definition !! Example !! Category
|-
! Date of availability
| Since when is the data available
||
|| Impact/Exploitation

|-
! Number of users
| Is there any information on the number of accesses or users?
||
|| Impact/Exploitation

|-
! Number of scientific publications where data is cited/used
| Is there any information on the number of citations/uses?
||
|| Impact/Exploitation

|-
! Curation activities (is the versioning ongoing)
| Update, maintainance
||
|| Impact/Exploitation
|}

Attempting to put a value on Impact/Exploitation:
* '''ENTSO-E''': Found 172 results in Advanced Search (ALL) in Web of Science ([https://apps.webofknowledge.com/ WoSc]) (16/06/2020). Citations of all these search result add up to 714. This however does not mean, that all search results really explicitly used data from ENTSO-E. Some (for sure, for some were found) only mentioned it saying they adoped workflow etc. from the site.
* '''SMARD''': Found 82 results in Advanced Search (ALL=(SMARD)) in Web of Science ([https://apps.webofknowledge.com/ WoSc]) (16/06/2020). However: SMARD is also an acronym for ''Spinal muscular atrophy with respiratory distress (SMARD)''. This somewhat complicates results and needs some further discussion. All 82 search results accumumlated 2359 citations themselves.

Gap analysis

2020-08-27T14:54:50Z

Christopherbs: /* FAIR Self-Assessment tool */

<accesscontrol>Administrators,consortium</accesscontrol>

This is the page for the gap analysis.

= Tacit knowledge "FAIRification and opening of low carbon energy research data" =
 Consortium members can add any time, they feel that something is important to note even though it is not mentioned in a standard deliverable. In other words, this page collects uncodified, tacit knowledge. It will help us later to compile suggestions and lessons learned. This kind of knowledge is collected two-fold:
#Anytime if someone feels that this should be noted. Please write down: Issue, Date, Author (could also be "anonymous"), the issue described in a few words or maximal lines>
#During the final day of workshops

=== Learning process: ===

*People are hesitant to adopt new IT technologies, this is even the case among researchers heavily relying on data, algorithms and collaborative online software (e.g., R, platforms, online conferencing, HPC, ...). The effort to encourage change is not to underestimate. Reasons are several, notably, lack of time and uncertainty about potential benefit as well as overall risk aversion preferences. EERAdata is using the online software "Only Office" to facilitate collaboration (in particular also during the Covid-19 period).
 '''FAIR and open criteria:'''
*Consortium members have a fair understanding of what FAIR/O is, but there is little knowledge and/or technical experience on how to approach the FAIRification and opening. All, however, share the view that we are at a critical point in time, where we need to implement these criteria. 
*To deepen knowledge about FAIR/O criteria, it is useful to test the criteria on a database one is familiar with. For this purpose (and to start brainstorming about the platform), AIT has developed a questionnaire for application in the use cases.
'''Metadata:'''
*A good starting point is to think about metadata and to look into existing metadata concepts in one's field. The first step is to understand that also metadata need to adhere to the FAIR/O principles.
*The next step is to increase knowledge on IT specific terms, i.e. to understand what the difference is between different '''[https://energydata.onlyoffice.eu/Products/Files/doceditor.aspx?fileid=5404340 metadata frameworks] (taxonomy, thesaurus, ontology) as well as classification of metadata (high-level, medium-level, low-level OR administrative, structural and descriptive metadata).  '''WP 2 being in charge of aligning approaches between use cases, participates in all use case kickoffs to bring everybody on the same page. The presentation is linked with "metadata frameworks".
*It is useful to supply consortium members with read aheads and watch aheads on metadata to prepare the '''[https://energydata.onlyoffice.eu/Products/Files/doceditor.aspx?fileid=5296590 first EERAdata workshop]'''. The workshop starts applications and discussions in the use cases break out sessions (and bringing insights back to the plenary), using selected databases.

= FAIR Evaluation=

== FAIR Guiding Principles ==
{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ This table shows the FAIR guiding principles described by [https://www.doi.org/10.1038/sdata.2016.18 Wilkinson et al., 2016].

! To be Findable
|
* F1. (meta)data are assigned a globally unique and persistent identifier
* F2. data are described with rich metadata (defined by R1 below)
* F3. metadata clearly and explicitly include the identifier of the data it describes
* F4. (meta)data are registered or indexed in a searchable resource

|-
! To be Accessible
|
* A1. (meta)data are retrievable by their identifier using a standardized communications protocol
** A1.1 the protocol is open, free, and universally implementable
** A1.2 the protocol allows for an authentication and authorization procedure, where necessary
* A2. metadata are accessible, even when the data are no longer available

|-
! To be Interoperable
|
* I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
* I2. (meta)data use vocabularies that follow FAIR principles
* I3. (meta)data include qualified references to other (meta)data

|-
! To be Reusable
|
* R1. meta(data) are richly described with a plurality of accurate and relevant attributes
* R1.1. (meta)data are released with a clear and accessible data usage license
* R1.2. (meta)data are associated with detailed provenance
* R1.3. (meta)data meet domain-relevant community standards

|}

== Identified gaps after Workshop 1 (02/06/2020 – 04/06/2020) ==

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ This table shows a summary of the specific issues that were identified in Workshop 1. The aim was to categorise different issues to get a better understanding of how to tackle these challenges.
! FAIR/O !! Specific FAIR/O !! Gaps !! Gaps for energy domain !! Use case specific gaps !! Consequences for researchers
|-
! F
| F2
|| Metadata scope
|| Different fields require different metadata (potentially very specific)
|| UC3: lack of additional metadata for applications of materials
|| Data are less useful for the specific field
|-
! F
|| F1/F3
|| Identifiers
||
* Missing identifiers on websites
* Identifier not in downloaded data files
||
|| Makes research more difficult.
|-
! I
| I2
|| Taxonomy/ontology/common vocabulary and language issues
||
* Lack of standardisation
* Heterogeneous data makes standardisation hard
* No vocabulary documentation on websites
* Words used for same term in other languages may differ
* Databases in other languages than English
||
|| Research costs more time
|-
! I
| I3
|| References to other (meta)data
||
* No linking to source documents and related publications (for contextual knowledge)
* Possible need for links to other fields
|| UC3: link between microscopic and macroscopic materials (e.g., turbine blades)
||
* Data cannot be connected to the source
* Makes research more difficult.

|-
! R
| R1.1
|| Licensing
||
* No licenses available may mean the data is not reusable for the researcher
* Licenses not clear and accessible
* Obtaining licenses may result in more effort and costs
||
|| Data potentially not reusable

|-
! R
| R1.2
|| Reliability and provenance of data
|| When the data is collected from different and high number of sources, its reliability decreases
||
|| Data potentially not reusable

|-
! O
|
|| Privacy concerns and expected disadvantages
|| Not publishing data due to privacy concerns (sensitive data)
|| UC2: In case of distribution network data this is relevant
|| Data not accessible

|-
! Other
|
|| Conducting FAIR assessments
||
* No qualitative assessment of the data itself (which may be limited)
* Discrepancies between results conducted by humans versus machines
* Sometimes not even DC standards are met
* Metadata is not updated
* Lacking encryption of websites (https)
* Problems assessing whether metadata will be available after data is unavailable
* Interface design/layout may be unclear, incomplete or not intuitive for humans
* Authentication details (user registration login / good or bad, clarify)

||
* UC1 lack of data availability for time-series
||
* Assessment is uncertain (human assessment may need more clarification and understanding)
* Makes research more time-intensive

|}

== FAIR Self-Assessment tool ==

To conduct a human FAIR evaluation for different databases and their respective datasets, different free online tools were considered. Some seemed to be quite useful (e.g. [https://satifyd.dans.knaw.nl/ SATISFYD] ) and some were simple self-evaluation PDFs to be printed and filled out.

We decided to chose the [https://ardc.edu.au/resources/working-with-data/fair-data/fair-self-assessment-tool/ self-assessment tool] provided by the Australian Research Data Commons (ARDC). The decision was based manly upon the fact that the tools questions closely matched the FAIR categories specified by [https://www.doi.org/10.1038/sdata.2016.18 Wilkinson et al., 2016] which would make it easily comparable to the existing preferable FAIR conditions and aid with the repeatability of the results, and the half-automated scoring features that show the extent of the "FAIRness" of the analysed sample as a simple bar chart.

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ All options for each question of the ARDCs self-assessment tool
!colspan="4"|Findable
!colspan="3"|Accessible
!colspan="3"|Interoperable
!colspan="3"|Reusable
|-
| F1. (meta)data are assigned a globally unique and persistent identifier
|| F2. data are described with rich metadata (defined by R1 below)
|| F3. metadata clearly and explicitly include the identifier of the data it describes
|| F4. (meta)data are registered or indexed in a searchable resource
|| A1. (meta)data are retrievable by their identifier using a standardized communications protocol
|| A1.1 the protocol is open, free, and universally implementable and A1.2 the protocol allows for an authentication and authorization procedure, where necessary
|| A2. metadata are accessible, even when the data are no longer available
|| I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
|| I2. (meta)data use vocabularies that follow FAIR principles
|| I3. (meta)data include qualified references to other (meta)data
|| R1.1. (meta)data are released with a clear and accessible data usage license
|| R1.2. (meta)data are associated with detailed provenance
|| R1.3. (meta)data meet domain-relevant community standards
|-
! Does the dataset have any identifiers assigned?
! Is the dataset identifier included in all metadata records/files describing the data?
! How is the data described with metadata?
! What type of repository or registry is the metadata record in?
! How accessible is the data?
! Is the data available online without requiring specialised protocols or tools once access has been approved?
! Will the metadata record be available even if the data is no longer available?
! What (file) format(s) is the data available in?
! What best describes the types of vocabularies/ontologies/tagging schemas used to define the data elements?
! How is the metadata linked to other data and metadata (to enhance context and clearly indicate relationships)?
! Which of the following best describes the license/usage rights attached to the data?
! How much provenance information has been captured to facilitate data reuse?
!
|-
| Globally unique, citable and persistant (e.g. DOI, PURL, ARK or Handle)
|| Yes
|| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
|| Data is in one place but discoverable through several registries
|| Publicly accessible
|| Standard web service API (e.g. OGC)
|| Yes
|| In a structured, open standard, machine-readable format
|| Standardised open and universal using resolvable global identifiers linking to explainations
|| Meadata is represented in a machine readable format, e.g. in a linked data format such as Resource Description Framework (RDF)
|| Standard machine-readable license (e.g. Creative Commons)
|| Fully recorded in a machine-readable format
||
|-
| Web adress (URL)
|| No
|| Comprehensively, but in a text-based, non-standard format
|| Generalist public repository
|| Fully accessible to person who meet explicitly stated conditions, e.g. ethics approval for sensitive data
|| Non-standard web service (e.g. OpenAPI/Swagger/informal API)
|| No
|| In a structured, open standard, non-machine-readable format
|| Standardised vocabularies/ontologies/schema without global identifiers
|| The metadata record includes URI links to related metadata, data and definitions
|| Standard text based license
|| Fully recorded in a text format
||
|-
| Local identifier
||
|| Brief title and description
|| Domain-specific repository
|| A de-identified / modified subset of the data is publicly accessible
|| File download from online location
|| Unsure
|| Mostly in a proprietary format
|| No standards have been applied in the description of data elements
|| There are no links to other metadata
|| Non-standard machine-readably license (clearly indicating under what conditions the data may be reused)
|| Partially recorded
||
|-
| No identifier
||
|| The data is not described
|| Local institutional repository
|| Embaged access after a specific date
|| By individual arrangement
||
||
|| Data elements not described
||
|| Non-standard text-based license
|| No provenance information is recorded
||
|-
|
||
||
|| The data is not described in any repository
|| Unspecified conditional access e.g. contact the data custodian for access
|| No access to data
||
||
||
||
|| No license
||
||
|-
|
||
||
||
|| Access to metadata only
||
||
||
||
||
||
||
||
|-
|
||
||
||
|| No access to data or metadata
||
||
||
||
||
||
||
||
|}

At this point it is important to note that the assessment of databases and/or datasets using this tool is solely considered a human assessment. This may lead to different evaluations by different users. To counteract this phenomenon, it was suggested that the evaluations should be completed using the 'four-eyes principle' to create more verifiable results. Also, it is planned to additionally evaluate the respective databases using an external tool to perform a machine-assessment.

In addition, not all questions are weighted equally and thus a worse result for one question may not have a large impact whereas only choosing the second-best option in another question will bring down the overall FAIR value considerably.

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Description
|-
! rowspan="5" | F
|| F
|| About Findable
|| Making data Findable includes assigning a persistent identifier (like a DOI or Handle ), having rich metadata to describe the data and making sure it is findable through disciplinary and generalist discovery portals (local and international).

|-
| F1
|| About identifiers
|| Identifiers are essential for identifying, finding, retrieving, linking and citing datasets. A Web address (URL) can be used to specify the online location of a resource but over time URLs tend to change which leads to broken links to the data. To be useful, identifiers need to be persistent and unique. Digital Object Identifiers (DOIs) and other persistent identifiers (PIDs) provide a permanent citable reference to a particular dataset. The DOI is a permanent fixed reference to the dataset no matter where it is located online and enables citation and citation metrics. Services to create a persistent identifier are often offered by your affiliated institution or the repository you are using to describe your data. Talk to your library service for more information.
|-
| F2
|| About identifiers
|| The identifier (preferably a persistent identifier) needs to be clearly stated in the metadata record describing the data collection, and also in any associated data files or metadata.

|-
|F3
|| About metadata descriptions
|| Comprehensive metadata records will include descriptive content that facilitates discovery, access and reuse of the data being described. While there is no 'one size fits all' list, comprehensive metadata should include:
* a globally unique persistent identifier e.g. a DOI
* a title
* related people, i.e. the data creator or custodian
* how to access the data and file formats
* a description of how the data were created and how to interpret the data subject or keywords
* citation information that clearly indicates how the data should be cited
* a machine-readable data licence
* provenance and contextual information such as:
* links to related publications, projects, services and software
* methodology and processes involved in data production
* spatial and temporal coverage (if relevant)
* object-level data description

Providing metadata in a standard schema allows it to be read and used by machines as well as humans.

|-
| F4
|| About metadata repositories
|| A rich metadata description alone does not ensure a dataset’s ‘findability’ on the internet; the dataset needs to be registered or indexed in a searchable resource, such as a generalist (e.g. Figshare, Dryad, Zenodo domain-specific (e.g. PANGAEA, ADA, ICPSR), or institutional (e.g. ANU Research Repository , Sydney eScholarship Repository, data.gov.au) data repository or registry. Ideally these repositories/registries are indexed by search engines such as Google and/or Google Scholar.

|-
! rowspan = "4" | A
| A
|| About Accessible
|| To make data accessible may include making the data open using a standardised protocol. However the data does not necessarily have to be open. There are sometimes good reasons why data cannot be made open, for example privacy concerns, national security or commercial interests. If it is not open there should be clarity and transparency around the conditions governing access and reuse
|-
| A1
|| About data accessibility
|| Not all data that is discoverable can be freely accessed. Often there are embargoes, access controls, and access permissions associated with data due to a variety of issues such as privacy and commercial interests. Even with all these issues much sensitive data can be shared. Many other issues that could be perceived as blockers to sharing data may be overcome.
|-
| A1.1/A1.2
|| About protocols
|| Ideally users would like to retrieve appropriate internet content directly and unhindered once they have located it. Internet protocols (e.g. http and ftp) define rules and conventions for communication between devices, and tools and services are available to facilitate this process, e.g. APIs.

Protocols:
* HTTP (Hypertext Transfer Protocol) is the set of rules for transferring files (text, graphic images, sound, video, and other multimedia files) on the World Wide Web. As soon as a Web user opens their Web browser, the user is indirectly making use of HTTP.
* FTP (File Transfer Protocol) is a standard Internet protocol for transmitting files between computers on the Internet over TCP/IP connections. Read more: Web protocols
* API (Application Programming Interface): When information is made available in any machine readable format, it becomes possible to make that information directly available to programs that request that information over the web. An API is the way this information is made directly available to other machines.

|-
| A2
|| About metadata availability
|| Effort is often required to maintain data resources online which can often lead to it being neglected especially over long periods of time. This often leads to broken links between the metadata and the data. Having at least a description of the data in the form of a metadata record means there is a record of the data's existence allowing the possibility of someone tracking it down.

|-
! rowspan="4" | I
|| I
|| About Interoperability
|| To be interoperable (i.e. data that is interpretable by a computer, so that they can be automatically combined with other data) the data will need to use community agreed formats, language and vocabularies. The metadata will also need to use a community agreed standards and vocabularies, and contain links to related information using identifiers.
|-
|| I1
|| About file formats
|| When selecting file formats for archiving, the formats should ideally be:
* Non-proprietary
* Unencrypted
* Uncompressed
* In common usage by the research community
* Adherent to an open, documented standard, such as described by the State of California (see AB 1668, 2007)
* Interoperable among diverse platforms and applications
* Fully published and available royalty-free
* Fully and independently implementable by multiple software providers on multiple platforms without any intellectual property restrictions for necessary technology
* Developed and maintained by an open standards organization with a well-defined inclusive process for evolution of the standard.

From: Stanford University Library - Best practices for file formats
File format examples:
* Structured, open standard, machine-readable format e.g. (text) PDF/A, HTML, Plain text, (images) TIFF, JPEG 2000, GIF, (audio) MP3, AIFF, WAVE, (video) MOV, MPEG, AVI, (Tabular data) CSV
* structured, open standard, non-machine-readable format, e.g. PDF, HTML, JPG
* Proprietary format, e.g. doc (Word), .xls (Excel), .ppt (PowerPoint), .sav

Read more:
* ANDS
* European Data Portal

|-
|| I2
|| About schema standards
|| Schema standards are schemas that have gone through a formal validation process by a standards organisation, such as the International Standards Organisation (ISO) or an equivalent body such as the Dublin Core Metadata Initiative (DCMI); or, commonly used and consistently applied metadata schemas that are well documented, endorsed, and maintained can also become ‘de-facto standards’, e.g. RIF-CS for describing data collections and services used in Research Data Australia.

Read more: Metadata schema

Standardised open and universal schemas, e.g, DataCite Metadata Schema, PROV, Dublin Core
Domain-specific standards, e.g. . HPO - Human Phenotype Ontology, MeSH - Medical Subject Headings , Marine Community Profile, DDI - Data Documentation Initiative For more examples of standards...
Resolvable global identifiers - An identifier is any label used to name something uniquely (whether online or offline). URLs are an example of an identifier. So are serial numbers, and personal names. A persistent identifier is guaranteed to be managed and kept up to date over a defined time period.
Read more: Persistent Identifiers

|-
||I3
|| About metadata links
|| The goal is to create as many meaningful linkages as possible between (meta)data resources to enrich the contextual knowledge about the data, balanced against the time/energy involved in making a good data model.
Machine-readable (meta)data:
* (Meta)data in a format that can be automatically read and processed by a computer, such as [http://opendatahandbook.org/glossary/en/terms/csv/ CSV], [http://opendatahandbook.org/glossary/en/terms/json/ JSON], [http://opendatahandbook.org/glossary/en/terms/xml/ XML] etc. For more information: Open Data Handbook
Linked Data:
* Linked Data (also known as Linking Data) can be applied to improve the exploitation of the “Web of data.” The expression refers to the publishing of structured data in a way that typed links are created between data from different sources to provide a higher level of usability. By using Linked Data, it is possible to find other, related data. Structured data should meet four requirements to be called Linked Data:
* URIs should be assigned to all entities of the dataset.
* HTTP URIs are required to ensure that all entities can be referenced and cited by users and user agents.
* Entities should be described using standard formats such as RDF/XML.
* Links should be created to other, related entity URIs.

All data that fulfil these requirements and are released for the public are called Linked Open Data (LOD). http://www.lesliesikos.com/semantic-web-machine-readable-structured-data-with-meaningful-annotations/
Read more under the heading (meta)data include qualified references to other (meta)data in the following resource: https://www.dtls.nl/fair-data/fair-principles-explained/
For more information on Linked Data standards - RDF - https://www.w3.org/RDF/
Other related linked data standards: OWL - https://www.w3.org/OWL/ , SKOS - https://www.w3.org/2004/02/skos/ , SPARQL - https://www.w3.org/TR/rdf-sparql-query/

|-
! rowspan="3" | R
|| R
|| About Reusable
|| Reusable data should maintain its initial richness. For example, it should not be abridged for the purpose of explaining the findings in one particular publication. It needs a clear machine-readable [https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse licence] and [https://www.ands.org.au/partners-and-communities/ands-communities/data-provenance-interest-group provenance] information on how the data was formed. It should also use discipline-specific data and [https://www.ands.org.au/working-with-data/metadata metadata] standards to give it rich contextual information that will allow for accurate interpretation and reuse.

|-
|| R1.1
|| About licenses
|| If data is not licensed no-one else can use it. In Australia, no licence is regarded as the same as 'all rights reserved', confining any reuse to very limited circumstances. Applying a Creative Commons licence to your data is a simple way to ensure that your data can be reused. The less restrictive the licence, the more that can be done with the data.
To make it easy for the Web to know when a work is available under a particular license, a “machine readable” version of the license provides a summary of the key freedoms and obligations written into a format that software systems, search engines, and other kinds of technology can understand.
Example from the [https://creativecommons.org/choose/ Creative Commons License Choser]:

What appears in the metadata:

This work is licensed under a [https://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International License].

Code snippet:

<code>
<a rel="license" href="http://creativecommons.org/licenses/by/4.0/">

<img alt="Creative Commons License" style="border-width:0"

src="https://i.creativecommons.org/l/by/4.0/88x31.png"/>

</a> This work is licensed under a

<a rel="license" href="http://creativecommons.org/licenses/by/4.0/">

Creative Commons Attribution 4.0 International License</a>.
<\code>

Read more: https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse

|-
||R1.2
|| About provenance
|| Data provenance is used to document where a piece of data comes from and the process and methodology by which it is produced. It is important to confirm the authenticity of data enabling trust, credibility and reproducibility. This is becoming increasingly important, especially in the eScience community where research is data intensive and often involves complex data transformations and procedures ([https://www.ands.org.au/partners-and-communities/ands-communities/data-provenance-interest-group Read more]). Provenance vocabularies such as [https://www.w3.org/2005/Incubator/prov/XGR-prov-20101214/#Recommendations Provenir Ontology (PROV-O) and others].

|}

== Results for ShareWind ==

{| class="mw-collapsible wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ A dataset from the ShareWind platform was analysed using the self-assessment tool provided by ARDC.
! rowspan="5" |
| rowspan="5" |
! Database Code
| rowspan="24" style="background: #ABE" |
| ED4
| ED9
|-
! Database Code
|| SMILES
| ShareWind
|-
! Subdata-Code
|| https://www.ecria-smiles.eu/
| https://sharewind.eu/
|-
! Database/Subdata
|| [https://www.ecria-smiles.eu/data-files/-/document_library/W32cCAfL2jMX/view_file664466?_com_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX_redirect=https%3A%2F%2Fwww.ecria-smiles.eu%3A443%2Fdata-files%2F-%2Fdocument_library%2FW32cCAfL2jMX%2Fview%2F664423%3F_com_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX_redirect%3Dhttps%253A%252F%252Fwww.ecria-smiles.eu%253A443%252Fdata-files%253Fp_p_id%253Dcom_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX%2526p_p_lifecycle%253D0%2526p_p_state%253Dnormal%2526p_p_mode%253Dview here] (link has 585 characters)
| https://sharewind.eu/records/f9d31abbc2824284b8c1d28894d9619a; https://zenodo.org/record/1162738
|-
! Sample Size
|| 4 KB
| 37 MB
|-
| colspan="6" style="background: #ABE" |
|-
! rowspan="4" | Findable
| F1. (meta)data are assigned a globally unique and persistent identifier
! Does the dataset have any identifiers assigned?
|| Web adress (URL)
| Globally unique, citable and persistant (e.g. DOI, PURL, ARK or Handle)
|-

| F2. data are described with rich metadata (defined by R1 below)
! Is the dataset identifier included in all metadata records/files describing the data?
|| No
| No
|-

| F3. metadata clearly and explicitly include the identifier of the data it describes
! How is the data described with metadata?
|| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
|-

| F4. (meta)data are registered or indexed in a searchable resource
! What type of repository or registry is the metadata record in?
|| Domain-specific repository
| Data is in one place but discoverable through several registries
|-

! rowspan="3" | Accessible

| A1. (meta)data are retrievable by their identifier using a standardized communications protocol
! How accessible is the data?
|| Publicly accessible
| Publicly accessible
|-
| A1.1 the protocol is open, free, and universally implementable; A1.2 the protocol allows for an authentication and authorization procedure, where necessary
! Is the data available online without requiring specialised protocols or tools once access has been approved?
|| File download from online location
| Standard web service API (e.g. OGC)
|-
| A2. metadata are accessible, even when the data are no longer available
! Will the metadata record be available even if the data is no longer available?
|| Unsure
| Yes
|-

! rowspan="3" | Interoperable
| I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
! What (file) format(s) is the data available in?
|| In a structured, open standard, machine-readable format
|| In a structured, open standard, non-machine-readable format
|-
| I2. (meta)data use vocabularies that follow FAIR principles
! What best describes the types of vocabularies/ontologies/tagging schemas used to define the data elements?
|| Standardised vocabularies/ontologies/schema without global identifiers
|| Standardised vocabularies/ontologies/schema without global identifiers
|-
| I3. (meta)data include qualified references to other (meta)data
! How is the metadata linked to other data and metadata (to enhance context and clearly indicate relationships)?
|| There are no links to other metadata
|| Metadata is represented in a machine readable format, e.g. in a linked data format such as Resource Description Framework (RDF)
|-

! rowspan="3" | Reusable
| R1.1. (meta)data are released with a clear and accessible data usage license
! Which of the following best describes the license/usage rights attached to the data?
|| No license
| Standard machine-readable license (e.g. Creative Commons)
|-
| R1.2. (meta)data are associated with detailed provenance
! How much provenance information has been captured to facilitate data reuse?
|| Partially recorded
|| Partially recorded
|-
| R1.3. (meta)data meet domain-relevant community standards
|
|
|
|-
| colspan="6" style="background: #ABE" |
|-
! Total across F.A.I.R
|
! Low: < 40%; Medium: 50-60%; High: > 60%
|| 45 %
| 85 %
|-
| colspan="6" style="background: #ABE" |
|-
! Remarks about database
||
||
||
|| The data is provided by the open-source platform Zenodo, not the project website itself. A very positive aspect is that information on different versions of the data as well as the number of views and downloads is available.
|-
! Identified gaps
||
||
||
* No DOI, only URL
* Extremely long URL link spanning several lines with 585 characters, that can easily become corrupted
* No links to other metadata
* No license information given
* Provenance not fully recorded

||
* Dataset identifiers should be included in the files describing the data
* Provenance details are only partially recorded with a note with contact name for further information
|}

= Dublin Core Evaluation =

== Dublin Core Elements ==
{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ The DCMI Metadata Terms lists the current set of the Dublin Core vocabulary. Source: [https://dublincore.org/specifications/dublin-core/dcmi-terms/ Dublin Core Metadata Initiative]
| 1 || abstract || 11 || contributor || 21 || extent || 31 || isReplacedBy || 41 || publisher || 51 || tableOfContents
|-
| 2 || accessRights || 12 || coverage || 22 || format || 32 || isRequiredBy || 42 || references || 52 || temporal
|-
| 3 || accrualMethod || 13 || created || 23 || hasFormat || 33 || issued || 43 || relation || 53 || title
|-
| 4 || accrualPeriodicity || 14 || creator || 24 || hasPart || 34 || isVersionOf || 44 || replaces || 54 || type
|-
| 5 || accrualPolicy || 15 || date || 25 || hasVersion || 35 || language || 45 || requires || 55 || valid
|-
| 6 || alternative || 16 || dateAccepted || 26 || identifier || 36 || license || 46 || rights || ||
|-
| 7 || audience || 17 || dateCopyrighted || 27 || instructionalMethod || 37 || mediator || 47 || rightsHolder || ||
|-
| 8 || available || 18 || dateSubmitted || 28 || isFormatOf || 38 || medium || 48 || source || ||
|-
| 9 || bibliographicCitation || 19 || description || 29 || isPartOf || 39 || modified || 49 || spatial || ||
|-
| 10 || conformsTo || 20 || educationLevel || 30 || isReferencedBy || 40 || provenance || 50 || subject || ||
|}

'''Metadata Category Types according to [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO]'''

There are three main types of metadata:
* Descriptive metadata describes a resource for purposes such as discovery and identification. It can include elements such as title, abstract, author, and keywords.
* Structural metadata indicates how compound objects are put together, for example, how pages are ordered to form chapters.
* Administrative metadata provides information to help manage a resource, such as when and how it was created, file type and other technical information, and who can access it. There are several subsets of administrative data; two that sometimes are listed as separate metadata types are:
** Rights management meta-data, which deals with intellectual property rights,and
** Preservation metadata, which contains information needed to archive and preserve a resource.
''

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Simple Dublin Core Elements. 15 DC elements with their (shortened) official definitions and examples.
! DC element name !! DC definition ([https://www.dublincore.org/specifications/dublin-core/dces/ DCMI]) !! Example (Energy Domain) !! Category (determined with the aid of [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO])
|-
! Title
| A name given to the resource || Outdoor monitoring of a hybrid micro-CPV solar panel with integrated micro-tracking and diffuse capture || Descriptive
|-
! Subject
| The topic of the resource || concentrator photovoltaics; CPV; rooftop photovoltaics; integrated tracking || Descriptive
|-
! Description
| An account of the resource || Dataset from the outdoor characterization of a B Series module from Insolight at the rooftop of the Instituto de Energía Solar - Universidad Politécnica de Madrid. …. || Descriptive
|-
! Creator
| An entity primarily responsible for making the resource || Stephen Askins; Gaël Nardin || Descriptive
|-
! Publisher
| An entity responsible for making the resource available || Stephen Askins || Descriptive
|-
! Contributor
| An entity responsible for making contributions to the resource || Stephen Askins || Descriptive
|-
! Date
| A point or period of time associated with an event in the lifecycle of the resource || 2019-07-23; Date will be associated with the creation or availability of the resource. || Administrative
|-
! Type
| The nature or genre of the resource || info:eu-repo/semantics/other; dataset || Descriptive
|-
! Format
| The file format, physical medium, or dimensions of the resource || image
|| Structural
|-
! Identifier
| An unambiguous reference to the resource within a given context || https://zenodo.org/record/3346823; 10.5281/zenodo.3346823; oai:zenodo.org:3346823 || Descriptive
|-
! Source
| A related resource from which the described resource is derived || RC607.A26W574 1996 [where "RC607.A26W574 1996" is the call number of the print version of the resource, from which the present version was scanned] || Descriptive
|-
! Language
| A language of the resource || eng || Descriptive
|-
! Relation
| A related resource || info:eu-repo/grantAgreement/EC/H2020/787289/ || Descriptive
|-
! Coverage
| The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant || 1995-1996; Madrid, Spain || Descriptive
|-
! Rights
| Information about rights held in and over the resource || info:eu-repo/semantics/openAccess; https://creativecommons.org/licenses/by/4.0/legalcode || Administrative
|}

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Non-DC elements

! Non-DC element name !! Definition !! Example !! Category
|-
! Date of availability
| Since when is the data available
||
|| Impact/Exploitation

|-
! Number of users
| Is there any information on the number of accesses or users?
||
|| Impact/Exploitation

|-
! Number of scientific publications where data is cited/used
| Is there any information on the number of citations/uses?
||
|| Impact/Exploitation

|-
! Curation activities (is the versioning ongoing)
| Update, maintainance
||
|| Impact/Exploitation
|}

Attempting to put a value on Impact/Exploitation:
* '''ENTSO-E''': Found 172 results in Advanced Search (ALL) in Web of Science ([https://apps.webofknowledge.com/ WoSc]) (16/06/2020). Citations of all these search result add up to 714. This however does not mean, that all search results really explicitly used data from ENTSO-E. Some (for sure, for some were found) only mentioned it saying they adoped workflow etc. from the site.
* '''SMARD''': Found 82 results in Advanced Search (ALL=(SMARD)) in Web of Science ([https://apps.webofknowledge.com/ WoSc]) (16/06/2020). However: SMARD is also an acronym for ''Spinal muscular atrophy with respiratory distress (SMARD)''. This somewhat complicates results and needs some further discussion. All 82 search results accumumlated 2359 citations themselves.

Gap analysis

2020-08-27T14:52:04Z

Christopherbs: /* FAIR Self-Assessment tool */

<accesscontrol>Administrators,consortium</accesscontrol>

This is the page for the gap analysis.

= Tacit knowledge "FAIRification and opening of low carbon energy research data" =
 Consortium members can add any time, they feel that something is important to note even though it is not mentioned in a standard deliverable. In other words, this page collects uncodified, tacit knowledge. It will help us later to compile suggestions and lessons learned. This kind of knowledge is collected two-fold:
#Anytime if someone feels that this should be noted. Please write down: Issue, Date, Author (could also be "anonymous"), the issue described in a few words or maximal lines>
#During the final day of workshops

=== Learning process: ===

*People are hesitant to adopt new IT technologies, this is even the case among researchers heavily relying on data, algorithms and collaborative online software (e.g., R, platforms, online conferencing, HPC, ...). The effort to encourage change is not to underestimate. Reasons are several, notably, lack of time and uncertainty about potential benefit as well as overall risk aversion preferences. EERAdata is using the online software "Only Office" to facilitate collaboration (in particular also during the Covid-19 period).
 '''FAIR and open criteria:'''
*Consortium members have a fair understanding of what FAIR/O is, but there is little knowledge and/or technical experience on how to approach the FAIRification and opening. All, however, share the view that we are at a critical point in time, where we need to implement these criteria. 
*To deepen knowledge about FAIR/O criteria, it is useful to test the criteria on a database one is familiar with. For this purpose (and to start brainstorming about the platform), AIT has developed a questionnaire for application in the use cases.
'''Metadata:'''
*A good starting point is to think about metadata and to look into existing metadata concepts in one's field. The first step is to understand that also metadata need to adhere to the FAIR/O principles.
*The next step is to increase knowledge on IT specific terms, i.e. to understand what the difference is between different '''[https://energydata.onlyoffice.eu/Products/Files/doceditor.aspx?fileid=5404340 metadata frameworks] (taxonomy, thesaurus, ontology) as well as classification of metadata (high-level, medium-level, low-level OR administrative, structural and descriptive metadata).  '''WP 2 being in charge of aligning approaches between use cases, participates in all use case kickoffs to bring everybody on the same page. The presentation is linked with "metadata frameworks".
*It is useful to supply consortium members with read aheads and watch aheads on metadata to prepare the '''[https://energydata.onlyoffice.eu/Products/Files/doceditor.aspx?fileid=5296590 first EERAdata workshop]'''. The workshop starts applications and discussions in the use cases break out sessions (and bringing insights back to the plenary), using selected databases.

= FAIR Evaluation=

== FAIR Guiding Principles ==
{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ This table shows the FAIR guiding principles described by [https://www.doi.org/10.1038/sdata.2016.18 Wilkinson et al., 2016].

! To be Findable
|
* F1. (meta)data are assigned a globally unique and persistent identifier
* F2. data are described with rich metadata (defined by R1 below)
* F3. metadata clearly and explicitly include the identifier of the data it describes
* F4. (meta)data are registered or indexed in a searchable resource

|-
! To be Accessible
|
* A1. (meta)data are retrievable by their identifier using a standardized communications protocol
** A1.1 the protocol is open, free, and universally implementable
** A1.2 the protocol allows for an authentication and authorization procedure, where necessary
* A2. metadata are accessible, even when the data are no longer available

|-
! To be Interoperable
|
* I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
* I2. (meta)data use vocabularies that follow FAIR principles
* I3. (meta)data include qualified references to other (meta)data

|-
! To be Reusable
|
* R1. meta(data) are richly described with a plurality of accurate and relevant attributes
* R1.1. (meta)data are released with a clear and accessible data usage license
* R1.2. (meta)data are associated with detailed provenance
* R1.3. (meta)data meet domain-relevant community standards

|}

== Identified gaps after Workshop 1 (02/06/2020 – 04/06/2020) ==

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ This table shows a summary of the specific issues that were identified in Workshop 1. The aim was to categorise different issues to get a better understanding of how to tackle these challenges.
! FAIR/O !! Specific FAIR/O !! Gaps !! Gaps for energy domain !! Use case specific gaps !! Consequences for researchers
|-
! F
| F2
|| Metadata scope
|| Different fields require different metadata (potentially very specific)
|| UC3: lack of additional metadata for applications of materials
|| Data are less useful for the specific field
|-
! F
|| F1/F3
|| Identifiers
||
* Missing identifiers on websites
* Identifier not in downloaded data files
||
|| Makes research more difficult.
|-
! I
| I2
|| Taxonomy/ontology/common vocabulary and language issues
||
* Lack of standardisation
* Heterogeneous data makes standardisation hard
* No vocabulary documentation on websites
* Words used for same term in other languages may differ
* Databases in other languages than English
||
|| Research costs more time
|-
! I
| I3
|| References to other (meta)data
||
* No linking to source documents and related publications (for contextual knowledge)
* Possible need for links to other fields
|| UC3: link between microscopic and macroscopic materials (e.g., turbine blades)
||
* Data cannot be connected to the source
* Makes research more difficult.

|-
! R
| R1.1
|| Licensing
||
* No licenses available may mean the data is not reusable for the researcher
* Licenses not clear and accessible
* Obtaining licenses may result in more effort and costs
||
|| Data potentially not reusable

|-
! R
| R1.2
|| Reliability and provenance of data
|| When the data is collected from different and high number of sources, its reliability decreases
||
|| Data potentially not reusable

|-
! O
|
|| Privacy concerns and expected disadvantages
|| Not publishing data due to privacy concerns (sensitive data)
|| UC2: In case of distribution network data this is relevant
|| Data not accessible

|-
! Other
|
|| Conducting FAIR assessments
||
* No qualitative assessment of the data itself (which may be limited)
* Discrepancies between results conducted by humans versus machines
* Sometimes not even DC standards are met
* Metadata is not updated
* Lacking encryption of websites (https)
* Problems assessing whether metadata will be available after data is unavailable
* Interface design/layout may be unclear, incomplete or not intuitive for humans
* Authentication details (user registration login / good or bad, clarify)

||
* UC1 lack of data availability for time-series
||
* Assessment is uncertain (human assessment may need more clarification and understanding)
* Makes research more time-intensive

|}

== FAIR Self-Assessment tool ==

To conduct a human FAIR evaluation for different databases and their respective datasets, different free online tools were considered. Some seemed to be quite useful (e.g. [https://satifyd.dans.knaw.nl/ SATISFYD] ) and some were simple self-evaluation PDFs to be printed and filled out.

We decided to chose the [https://ardc.edu.au/resources/working-with-data/fair-data/fair-self-assessment-tool/ self-assessment tool] provided by the Australian Research Data Commons (ARDC). The decision was based manly upon the fact that the tools questions closely matched the FAIR categories specified by [https://www.doi.org/10.1038/sdata.2016.18 Wilkinson et al., 2016] which would make it easily comparable to the existing preferable FAIR conditions and aid with the repeatability of the results, and the half-automated scoring features that show the extent of the "FAIRness" of the analysed sample as a simple bar chart.

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ All options for each question of the ARDCs self-assessment tool
!colspan="4"|Findable
!colspan="3"|Accessible
!colspan="3"|Interoperable
!colspan="3"|Reusable
|-
| F1. (meta)data are assigned a globally unique and persistent identifier
|| F2. data are described with rich metadata (defined by R1 below)
|| F3. metadata clearly and explicitly include the identifier of the data it describes
|| F4. (meta)data are registered or indexed in a searchable resource
|| A1. (meta)data are retrievable by their identifier using a standardized communications protocol
|| A1.1 the protocol is open, free, and universally implementable and A1.2 the protocol allows for an authentication and authorization procedure, where necessary
|| A2. metadata are accessible, even when the data are no longer available
|| I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
|| I2. (meta)data use vocabularies that follow FAIR principles
|| I3. (meta)data include qualified references to other (meta)data
|| R1.1. (meta)data are released with a clear and accessible data usage license
|| R1.2. (meta)data are associated with detailed provenance
|| R1.3. (meta)data meet domain-relevant community standards
|-
! Does the dataset have any identifiers assigned?
! Is the dataset identifier included in all metadata records/files describing the data?
! How is the data described with metadata?
! What type of repository or registry is the metadata record in?
! How accessible is the data?
! Is the data available online without requiring specialised protocols or tools once access has been approved?
! Will the metadata record be available even if the data is no longer available?
! What (file) format(s) is the data available in?
! What best describes the types of vocabularies/ontologies/tagging schemas used to define the data elements?
! How is the metadata linked to other data and metadata (to enhance context and clearly indicate relationships)?
! Which of the following best describes the license/usage rights attached to the data?
! How much provenance information has been captured to facilitate data reuse?
!
|-
| Globally unique, citable and persistant (e.g. DOI, PURL, ARK or Handle)
|| Yes
|| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
|| Data is in one place but discoverable through several registries
|| Publicly accessible
|| Standard web service API (e.g. OGC)
|| Yes
|| In a structured, open standard, machine-readable format
|| Standardised open and universal using resolvable global identifiers linking to explainations
|| Meadata is represented in a machine readable format, e.g. in a linked data format such as Resource Description Framework (RDF)
|| Standard machine-readable license (e.g. Creative Commons)
|| Fully recorded in a machine-readable format
||
|-
| Web adress (URL)
|| No
|| Comprehensively, but in a text-based, non-standard format
|| Generalist public repository
|| Fully accessible to person who meet explicitly stated conditions, e.g. ethics approval for sensitive data
|| Non-standard web service (e.g. OpenAPI/Swagger/informal API)
|| No
|| In a structured, open standard, non-machine-readable format
|| Standardised vocabularies/ontologies/schema without global identifiers
|| The metadata record includes URI links to related metadata, data and definitions
|| Standard text based license
|| Fully recorded in a text format
||
|-
| Local identifier
||
|| Brief title and description
|| Domain-specific repository
|| A de-identified / modified subset of the data is publicly accessible
|| File download from online location
|| Unsure
|| Mostly in a proprietary format
|| No standards have been applied in the description of data elements
|| There are no links to other metadata
|| Non-standard machine-readably license (clearly indicating under what conditions the data may be reused)
|| Partially recorded
||
|-
| No identifier
||
|| The data is not described
|| Local institutional repository
|| Embaged access after a specific date
|| By individual arrangement
||
||
|| Data elements not described
||
|| Non-standard text-based license
|| No provenance information is recorded
||
|-
|
||
||
|| The data is not described in any repository
|| Unspecified conditional access e.g. contact the data custodian for access
|| No access to data
||
||
||
||
|| No license
||
||
|-
|
||
||
||
|| Access to metadata only
||
||
||
||
||
||
||
||
|-
|
||
||
||
|| No access to data or metadata
||
||
||
||
||
||
||
||
|}

At this point it is important to note that the assessment of databases and/or datasets using this tool is solely considered a human assessment. This may lead to different evaluations by different users. To counteract this phenomenon, it was suggested that the evaluations should be completed using the 'four-eyes principle' to create more verifiable results. Also, it is planned to additionally evaluate the respective databases using an external tool to perform a machine-assessment.

In addition, not all questions are weighted equally and thus a worse result for one question may not have a large impact whereas only choosing the second-best option in another question will bring down the overall FAIR value considerably.

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Description
|-
! rowspan="5" | F
|| F
|| About Findable
|| Making data Findable includes assigning a persistent identifier (like a DOI or Handle ), having rich metadata to describe the data and making sure it is findable through disciplinary and generalist discovery portals (local and international).

|-
| F1
|| About identifiers
|| Identifiers are essential for identifying, finding, retrieving, linking and citing datasets. A Web address (URL) can be used to specify the online location of a resource but over time URLs tend to change which leads to broken links to the data. To be useful, identifiers need to be persistent and unique. Digital Object Identifiers (DOIs) and other persistent identifiers (PIDs) provide a permanent citable reference to a particular dataset. The DOI is a permanent fixed reference to the dataset no matter where it is located online and enables citation and citation metrics. Services to create a persistent identifier are often offered by your affiliated institution or the repository you are using to describe your data. Talk to your library service for more information.
|-
| F2
|| About identifiers
|| The identifier (preferably a persistent identifier) needs to be clearly stated in the metadata record describing the data collection, and also in any associated data files or metadata.

|-
|F3
|| About metadata descriptions
|| Comprehensive metadata records will include descriptive content that facilitates discovery, access and reuse of the data being described. While there is no 'one size fits all' list, comprehensive metadata should include:
* a globally unique persistent identifier e.g. a DOI
* a title
* related people, i.e. the data creator or custodian
* how to access the data and file formats
* a description of how the data were created and how to interpret the data subject or keywords
* citation information that clearly indicates how the data should be cited
* a machine-readable data licence
* provenance and contextual information such as:
* links to related publications, projects, services and software
* methodology and processes involved in data production
* spatial and temporal coverage (if relevant)
* object-level data description

Providing metadata in a standard schema allows it to be read and used by machines as well as humans.

|-
| F4
|| About metadata repositories
|| A rich metadata description alone does not ensure a dataset’s ‘findability’ on the internet; the dataset needs to be registered or indexed in a searchable resource, such as a generalist (e.g. Figshare, Dryad, Zenodo domain-specific (e.g. PANGAEA, ADA, ICPSR), or institutional (e.g. ANU Research Repository , Sydney eScholarship Repository, data.gov.au) data repository or registry. Ideally these repositories/registries are indexed by search engines such as Google and/or Google Scholar.

|-
! rowspan = "4" | A
| A
|| About Accessible
|| To make data accessible may include making the data open using a standardised protocol. However the data does not necessarily have to be open. There are sometimes good reasons why data cannot be made open, for example privacy concerns, national security or commercial interests. If it is not open there should be clarity and transparency around the conditions governing access and reuse
|-
| A1
|| About data accessibility
|| Not all data that is discoverable can be freely accessed. Often there are embargoes, access controls, and access permissions associated with data due to a variety of issues such as privacy and commercial interests. Even with all these issues much sensitive data can be shared. Many other issues that could be perceived as blockers to sharing data may be overcome.
|-
| A1.1/A1.2
|| About protocols
|| Ideally users would like to retrieve appropriate internet content directly and unhindered once they have located it. Internet protocols (e.g. http and ftp) define rules and conventions for communication between devices, and tools and services are available to facilitate this process, e.g. APIs.

Protocols:
* HTTP (Hypertext Transfer Protocol) is the set of rules for transferring files (text, graphic images, sound, video, and other multimedia files) on the World Wide Web. As soon as a Web user opens their Web browser, the user is indirectly making use of HTTP.
* FTP (File Transfer Protocol) is a standard Internet protocol for transmitting files between computers on the Internet over TCP/IP connections. Read more: Web protocols
* API (Application Programming Interface): When information is made available in any machine readable format, it becomes possible to make that information directly available to programs that request that information over the web. An API is the way this information is made directly available to other machines.

|-
| A2
|| About metadata availability
|| Effort is often required to maintain data resources online which can often lead to it being neglected especially over long periods of time. This often leads to broken links between the metadata and the data. Having at least a description of the data in the form of a metadata record means there is a record of the data's existence allowing the possibility of someone tracking it down.

|-
! rowspan="4" | I
|| I
|| About Interoperability
|| To be interoperable (i.e. data that is interpretable by a computer, so that they can be automatically combined with other data) the data will need to use community agreed formats, language and vocabularies. The metadata will also need to use a community agreed standards and vocabularies, and contain links to related information using identifiers.
|-
|| I1
|| About file formats
|| When selecting file formats for archiving, the formats should ideally be:
* Non-proprietary
* Unencrypted
* Uncompressed
* In common usage by the research community
* Adherent to an open, documented standard, such as described by the State of California (see AB 1668, 2007)
* Interoperable among diverse platforms and applications
* Fully published and available royalty-free
* Fully and independently implementable by multiple software providers on multiple platforms without any intellectual property restrictions for necessary technology
* Developed and maintained by an open standards organization with a well-defined inclusive process for evolution of the standard.

From: Stanford University Library - Best practices for file formats
File format examples:
* Structured, open standard, machine-readable format e.g. (text) PDF/A, HTML, Plain text, (images) TIFF, JPEG 2000, GIF, (audio) MP3, AIFF, WAVE, (video) MOV, MPEG, AVI, (Tabular data) CSV
* structured, open standard, non-machine-readable format, e.g. PDF, HTML, JPG
* Proprietary format, e.g. doc (Word), .xls (Excel), .ppt (PowerPoint), .sav

Read more:
* ANDS
* European Data Portal

|-
|| I2
|| About schema standards
|| Schema standards are schemas that have gone through a formal validation process by a standards organisation, such as the International Standards Organisation (ISO) or an equivalent body such as the Dublin Core Metadata Initiative (DCMI); or, commonly used and consistently applied metadata schemas that are well documented, endorsed, and maintained can also become ‘de-facto standards’, e.g. RIF-CS for describing data collections and services used in Research Data Australia.

Read more: Metadata schema

Standardised open and universal schemas, e.g, DataCite Metadata Schema, PROV, Dublin Core
Domain-specific standards, e.g. . HPO - Human Phenotype Ontology, MeSH - Medical Subject Headings , Marine Community Profile, DDI - Data Documentation Initiative For more examples of standards...
Resolvable global identifiers - An identifier is any label used to name something uniquely (whether online or offline). URLs are an example of an identifier. So are serial numbers, and personal names. A persistent identifier is guaranteed to be managed and kept up to date over a defined time period.
Read more: Persistent Identifiers

|-
||I3
|| About metadata links
|| The goal is to create as many meaningful linkages as possible between (meta)data resources to enrich the contextual knowledge about the data, balanced against the time/energy involved in making a good data model.
Machine-readable (meta)data:
* (Meta)data in a format that can be automatically read and processed by a computer, such as CSV, JSON, XML etc. For more information: Open Data Handbook
Linked Data:
* Linked Data (also known as Linking Data) can be applied to improve the exploitation of the “Web of data.” The expression refers to the publishing of structured data in a way that typed links are created between data from different sources to provide a higher level of usability. By using Linked Data, it is possible to find other, related data. Structured data should meet four requirements to be called Linked Data:
* URIs should be assigned to all entities of the dataset.
* HTTP URIs are required to ensure that all entities can be referenced and cited by users and user agents.
* Entities should be described using standard formats such as RDF/XML.
* Links should be created to other, related entity URIs.

All data that fulfil these requirements and are released for the public are called Linked Open Data (LOD). http://www.lesliesikos.com/semantic-web-machine-readable-structured-data-with-meaningful-annotations/
Read more under the heading (meta)data include qualified references to other (meta)data in the following resource: https://www.dtls.nl/fair-data/fair-principles-explained/
For more information on Linked Data standards - RDF - https://www.w3.org/RDF/
Other related linked data standards: OWL - https://www.w3.org/OWL/ , SKOS - https://www.w3.org/2004/02/skos/ , SPARQL - https://www.w3.org/TR/rdf-sparql-query/

|-
! rowspan="3" | R
|| R
|| About Reusable
|| Reusable data should maintain its initial richness. For example, it should not be abridged for the purpose of explaining the findings in one particular publication. It needs a clear machine-readable [www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse licence] and [https://www.ands.org.au/partners-and-communities/ands-communities/data-provenance-interest-group provenance] information on how the data was formed. It should also use discipline-specific data and [https://www.ands.org.au/working-with-data/metadata metadata] standards to give it rich contextual information that will allow for accurate interpretation and reuse.

|-
|| R1.1
|| About licenses
|| If data is not licensed no-one else can use it. In Australia, no licence is regarded as the same as 'all rights reserved', confining any reuse to very limited circumstances. Applying a Creative Commons licence to your data is a simple way to ensure that your data can be reused. The less restrictive the licence, the more that can be done with the data.
To make it easy for the Web to know when a work is available under a particular license, a “machine readable” version of the license provides a summary of the key freedoms and obligations written into a format that software systems, search engines, and other kinds of technology can understand.
Example from the [https://creativecommons.org/choose/ Creative Commons License Choser]:

What appears in the metadata:

This work is licensed under a [https://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International License].

Code snippet:

<code>
<a rel="license" href="http://creativecommons.org/licenses/by/4.0/">

<img alt="Creative Commons License" style="border-width:0"

src="https://i.creativecommons.org/l/by/4.0/88x31.png"/>

</a> This work is licensed under a

<a rel="license" href="http://creativecommons.org/licenses/by/4.0/">

Creative Commons Attribution 4.0 International License</a>.
<\code>

Read more: https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse

|-
||R1.2
|| About provenance
|| Data provenance is used to document where a piece of data comes from and the process and methodology by which it is produced. It is important to confirm the authenticity of data enabling trust, credibility and reproducibility. This is becoming increasingly important, especially in the eScience community where research is data intensive and often involves complex data transformations and procedures ([https://www.ands.org.au/partners-and-communities/ands-communities/data-provenance-interest-group Read more]). Provenance vocabularies such as [https://www.w3.org/2005/Incubator/prov/XGR-prov-20101214/#Recommendations Provenir Ontology (PROV-O) and others].

|}

== Results for ShareWind ==

{| class="mw-collapsible wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ A dataset from the ShareWind platform was analysed using the self-assessment tool provided by ARDC.
! rowspan="5" |
| rowspan="5" |
! Database Code
| rowspan="24" style="background: #ABE" |
| ED4
| ED9
|-
! Database Code
|| SMILES
| ShareWind
|-
! Subdata-Code
|| https://www.ecria-smiles.eu/
| https://sharewind.eu/
|-
! Database/Subdata
|| [https://www.ecria-smiles.eu/data-files/-/document_library/W32cCAfL2jMX/view_file664466?_com_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX_redirect=https%3A%2F%2Fwww.ecria-smiles.eu%3A443%2Fdata-files%2F-%2Fdocument_library%2FW32cCAfL2jMX%2Fview%2F664423%3F_com_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX_redirect%3Dhttps%253A%252F%252Fwww.ecria-smiles.eu%253A443%252Fdata-files%253Fp_p_id%253Dcom_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX%2526p_p_lifecycle%253D0%2526p_p_state%253Dnormal%2526p_p_mode%253Dview here] (link has 585 characters)
| https://sharewind.eu/records/f9d31abbc2824284b8c1d28894d9619a; https://zenodo.org/record/1162738
|-
! Sample Size
|| 4 KB
| 37 MB
|-
| colspan="6" style="background: #ABE" |
|-
! rowspan="4" | Findable
| F1. (meta)data are assigned a globally unique and persistent identifier
! Does the dataset have any identifiers assigned?
|| Web adress (URL)
| Globally unique, citable and persistant (e.g. DOI, PURL, ARK or Handle)
|-

| F2. data are described with rich metadata (defined by R1 below)
! Is the dataset identifier included in all metadata records/files describing the data?
|| No
| No
|-

| F3. metadata clearly and explicitly include the identifier of the data it describes
! How is the data described with metadata?
|| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
|-

| F4. (meta)data are registered or indexed in a searchable resource
! What type of repository or registry is the metadata record in?
|| Domain-specific repository
| Data is in one place but discoverable through several registries
|-

! rowspan="3" | Accessible

| A1. (meta)data are retrievable by their identifier using a standardized communications protocol
! How accessible is the data?
|| Publicly accessible
| Publicly accessible
|-
| A1.1 the protocol is open, free, and universally implementable; A1.2 the protocol allows for an authentication and authorization procedure, where necessary
! Is the data available online without requiring specialised protocols or tools once access has been approved?
|| File download from online location
| Standard web service API (e.g. OGC)
|-
| A2. metadata are accessible, even when the data are no longer available
! Will the metadata record be available even if the data is no longer available?
|| Unsure
| Yes
|-

! rowspan="3" | Interoperable
| I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
! What (file) format(s) is the data available in?
|| In a structured, open standard, machine-readable format
|| In a structured, open standard, non-machine-readable format
|-
| I2. (meta)data use vocabularies that follow FAIR principles
! What best describes the types of vocabularies/ontologies/tagging schemas used to define the data elements?
|| Standardised vocabularies/ontologies/schema without global identifiers
|| Standardised vocabularies/ontologies/schema without global identifiers
|-
| I3. (meta)data include qualified references to other (meta)data
! How is the metadata linked to other data and metadata (to enhance context and clearly indicate relationships)?
|| There are no links to other metadata
|| Metadata is represented in a machine readable format, e.g. in a linked data format such as Resource Description Framework (RDF)
|-

! rowspan="3" | Reusable
| R1.1. (meta)data are released with a clear and accessible data usage license
! Which of the following best describes the license/usage rights attached to the data?
|| No license
| Standard machine-readable license (e.g. Creative Commons)
|-
| R1.2. (meta)data are associated with detailed provenance
! How much provenance information has been captured to facilitate data reuse?
|| Partially recorded
|| Partially recorded
|-
| R1.3. (meta)data meet domain-relevant community standards
|
|
|
|-
| colspan="6" style="background: #ABE" |
|-
! Total across F.A.I.R
|
! Low: < 40%; Medium: 50-60%; High: > 60%
|| 45 %
| 85 %
|-
| colspan="6" style="background: #ABE" |
|-
! Remarks about database
||
||
||
|| The data is provided by the open-source platform Zenodo, not the project website itself. A very positive aspect is that information on different versions of the data as well as the number of views and downloads is available.
|-
! Identified gaps
||
||
||
* No DOI, only URL
* Extremely long URL link spanning several lines with 585 characters, that can easily become corrupted
* No links to other metadata
* No license information given
* Provenance not fully recorded

||
* Dataset identifiers should be included in the files describing the data
* Provenance details are only partially recorded with a note with contact name for further information
|}

= Dublin Core Evaluation =

== Dublin Core Elements ==
{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ The DCMI Metadata Terms lists the current set of the Dublin Core vocabulary. Source: [https://dublincore.org/specifications/dublin-core/dcmi-terms/ Dublin Core Metadata Initiative]
| 1 || abstract || 11 || contributor || 21 || extent || 31 || isReplacedBy || 41 || publisher || 51 || tableOfContents
|-
| 2 || accessRights || 12 || coverage || 22 || format || 32 || isRequiredBy || 42 || references || 52 || temporal
|-
| 3 || accrualMethod || 13 || created || 23 || hasFormat || 33 || issued || 43 || relation || 53 || title
|-
| 4 || accrualPeriodicity || 14 || creator || 24 || hasPart || 34 || isVersionOf || 44 || replaces || 54 || type
|-
| 5 || accrualPolicy || 15 || date || 25 || hasVersion || 35 || language || 45 || requires || 55 || valid
|-
| 6 || alternative || 16 || dateAccepted || 26 || identifier || 36 || license || 46 || rights || ||
|-
| 7 || audience || 17 || dateCopyrighted || 27 || instructionalMethod || 37 || mediator || 47 || rightsHolder || ||
|-
| 8 || available || 18 || dateSubmitted || 28 || isFormatOf || 38 || medium || 48 || source || ||
|-
| 9 || bibliographicCitation || 19 || description || 29 || isPartOf || 39 || modified || 49 || spatial || ||
|-
| 10 || conformsTo || 20 || educationLevel || 30 || isReferencedBy || 40 || provenance || 50 || subject || ||
|}

'''Metadata Category Types according to [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO]'''

There are three main types of metadata:
* Descriptive metadata describes a resource for purposes such as discovery and identification. It can include elements such as title, abstract, author, and keywords.
* Structural metadata indicates how compound objects are put together, for example, how pages are ordered to form chapters.
* Administrative metadata provides information to help manage a resource, such as when and how it was created, file type and other technical information, and who can access it. There are several subsets of administrative data; two that sometimes are listed as separate metadata types are:
** Rights management meta-data, which deals with intellectual property rights,and
** Preservation metadata, which contains information needed to archive and preserve a resource.
''

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Simple Dublin Core Elements. 15 DC elements with their (shortened) official definitions and examples.
! DC element name !! DC definition ([https://www.dublincore.org/specifications/dublin-core/dces/ DCMI]) !! Example (Energy Domain) !! Category (determined with the aid of [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO])
|-
! Title
| A name given to the resource || Outdoor monitoring of a hybrid micro-CPV solar panel with integrated micro-tracking and diffuse capture || Descriptive
|-
! Subject
| The topic of the resource || concentrator photovoltaics; CPV; rooftop photovoltaics; integrated tracking || Descriptive
|-
! Description
| An account of the resource || Dataset from the outdoor characterization of a B Series module from Insolight at the rooftop of the Instituto de Energía Solar - Universidad Politécnica de Madrid. …. || Descriptive
|-
! Creator
| An entity primarily responsible for making the resource || Stephen Askins; Gaël Nardin || Descriptive
|-
! Publisher
| An entity responsible for making the resource available || Stephen Askins || Descriptive
|-
! Contributor
| An entity responsible for making contributions to the resource || Stephen Askins || Descriptive
|-
! Date
| A point or period of time associated with an event in the lifecycle of the resource || 2019-07-23; Date will be associated with the creation or availability of the resource. || Administrative
|-
! Type
| The nature or genre of the resource || info:eu-repo/semantics/other; dataset || Descriptive
|-
! Format
| The file format, physical medium, or dimensions of the resource || image
|| Structural
|-
! Identifier
| An unambiguous reference to the resource within a given context || https://zenodo.org/record/3346823; 10.5281/zenodo.3346823; oai:zenodo.org:3346823 || Descriptive
|-
! Source
| A related resource from which the described resource is derived || RC607.A26W574 1996 [where "RC607.A26W574 1996" is the call number of the print version of the resource, from which the present version was scanned] || Descriptive
|-
! Language
| A language of the resource || eng || Descriptive
|-
! Relation
| A related resource || info:eu-repo/grantAgreement/EC/H2020/787289/ || Descriptive
|-
! Coverage
| The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant || 1995-1996; Madrid, Spain || Descriptive
|-
! Rights
| Information about rights held in and over the resource || info:eu-repo/semantics/openAccess; https://creativecommons.org/licenses/by/4.0/legalcode || Administrative
|}

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Non-DC elements

! Non-DC element name !! Definition !! Example !! Category
|-
! Date of availability
| Since when is the data available
||
|| Impact/Exploitation

|-
! Number of users
| Is there any information on the number of accesses or users?
||
|| Impact/Exploitation

|-
! Number of scientific publications where data is cited/used
| Is there any information on the number of citations/uses?
||
|| Impact/Exploitation

|-
! Curation activities (is the versioning ongoing)
| Update, maintainance
||
|| Impact/Exploitation
|}

Attempting to put a value on Impact/Exploitation:
* '''ENTSO-E''': Found 172 results in Advanced Search (ALL) in Web of Science ([https://apps.webofknowledge.com/ WoSc]) (16/06/2020). Citations of all these search result add up to 714. This however does not mean, that all search results really explicitly used data from ENTSO-E. Some (for sure, for some were found) only mentioned it saying they adoped workflow etc. from the site.
* '''SMARD''': Found 82 results in Advanced Search (ALL=(SMARD)) in Web of Science ([https://apps.webofknowledge.com/ WoSc]) (16/06/2020). However: SMARD is also an acronym for ''Spinal muscular atrophy with respiratory distress (SMARD)''. This somewhat complicates results and needs some further discussion. All 82 search results accumumlated 2359 citations themselves.

Gap analysis

2020-08-27T14:27:27Z

Christopherbs: /* FAIR Self-Assessment tool */ Added descriptions of ARDC FAIR options

<accesscontrol>Administrators,consortium</accesscontrol>

This is the page for the gap analysis.

= Tacit knowledge "FAIRification and opening of low carbon energy research data" =
 Consortium members can add any time, they feel that something is important to note even though it is not mentioned in a standard deliverable. In other words, this page collects uncodified, tacit knowledge. It will help us later to compile suggestions and lessons learned. This kind of knowledge is collected two-fold:
#Anytime if someone feels that this should be noted. Please write down: Issue, Date, Author (could also be "anonymous"), the issue described in a few words or maximal lines>
#During the final day of workshops

=== Learning process: ===

*People are hesitant to adopt new IT technologies, this is even the case among researchers heavily relying on data, algorithms and collaborative online software (e.g., R, platforms, online conferencing, HPC, ...). The effort to encourage change is not to underestimate. Reasons are several, notably, lack of time and uncertainty about potential benefit as well as overall risk aversion preferences. EERAdata is using the online software "Only Office" to facilitate collaboration (in particular also during the Covid-19 period).
 '''FAIR and open criteria:'''
*Consortium members have a fair understanding of what FAIR/O is, but there is little knowledge and/or technical experience on how to approach the FAIRification and opening. All, however, share the view that we are at a critical point in time, where we need to implement these criteria. 
*To deepen knowledge about FAIR/O criteria, it is useful to test the criteria on a database one is familiar with. For this purpose (and to start brainstorming about the platform), AIT has developed a questionnaire for application in the use cases.
'''Metadata:'''
*A good starting point is to think about metadata and to look into existing metadata concepts in one's field. The first step is to understand that also metadata need to adhere to the FAIR/O principles.
*The next step is to increase knowledge on IT specific terms, i.e. to understand what the difference is between different '''[https://energydata.onlyoffice.eu/Products/Files/doceditor.aspx?fileid=5404340 metadata frameworks] (taxonomy, thesaurus, ontology) as well as classification of metadata (high-level, medium-level, low-level OR administrative, structural and descriptive metadata).  '''WP 2 being in charge of aligning approaches between use cases, participates in all use case kickoffs to bring everybody on the same page. The presentation is linked with "metadata frameworks".
*It is useful to supply consortium members with read aheads and watch aheads on metadata to prepare the '''[https://energydata.onlyoffice.eu/Products/Files/doceditor.aspx?fileid=5296590 first EERAdata workshop]'''. The workshop starts applications and discussions in the use cases break out sessions (and bringing insights back to the plenary), using selected databases.

= FAIR Evaluation=

== FAIR Guiding Principles ==
{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ This table shows the FAIR guiding principles described by [https://www.doi.org/10.1038/sdata.2016.18 Wilkinson et al., 2016].

! To be Findable
|
* F1. (meta)data are assigned a globally unique and persistent identifier
* F2. data are described with rich metadata (defined by R1 below)
* F3. metadata clearly and explicitly include the identifier of the data it describes
* F4. (meta)data are registered or indexed in a searchable resource

|-
! To be Accessible
|
* A1. (meta)data are retrievable by their identifier using a standardized communications protocol
** A1.1 the protocol is open, free, and universally implementable
** A1.2 the protocol allows for an authentication and authorization procedure, where necessary
* A2. metadata are accessible, even when the data are no longer available

|-
! To be Interoperable
|
* I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
* I2. (meta)data use vocabularies that follow FAIR principles
* I3. (meta)data include qualified references to other (meta)data

|-
! To be Reusable
|
* R1. meta(data) are richly described with a plurality of accurate and relevant attributes
* R1.1. (meta)data are released with a clear and accessible data usage license
* R1.2. (meta)data are associated with detailed provenance
* R1.3. (meta)data meet domain-relevant community standards

|}

== Identified gaps after Workshop 1 (02/06/2020 – 04/06/2020) ==

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ This table shows a summary of the specific issues that were identified in Workshop 1. The aim was to categorise different issues to get a better understanding of how to tackle these challenges.
! FAIR/O !! Specific FAIR/O !! Gaps !! Gaps for energy domain !! Use case specific gaps !! Consequences for researchers
|-
! F
| F2
|| Metadata scope
|| Different fields require different metadata (potentially very specific)
|| UC3: lack of additional metadata for applications of materials
|| Data are less useful for the specific field
|-
! F
|| F1/F3
|| Identifiers
||
* Missing identifiers on websites
* Identifier not in downloaded data files
||
|| Makes research more difficult.
|-
! I
| I2
|| Taxonomy/ontology/common vocabulary and language issues
||
* Lack of standardisation
* Heterogeneous data makes standardisation hard
* No vocabulary documentation on websites
* Words used for same term in other languages may differ
* Databases in other languages than English
||
|| Research costs more time
|-
! I
| I3
|| References to other (meta)data
||
* No linking to source documents and related publications (for contextual knowledge)
* Possible need for links to other fields
|| UC3: link between microscopic and macroscopic materials (e.g., turbine blades)
||
* Data cannot be connected to the source
* Makes research more difficult.

|-
! R
| R1.1
|| Licensing
||
* No licenses available may mean the data is not reusable for the researcher
* Licenses not clear and accessible
* Obtaining licenses may result in more effort and costs
||
|| Data potentially not reusable

|-
! R
| R1.2
|| Reliability and provenance of data
|| When the data is collected from different and high number of sources, its reliability decreases
||
|| Data potentially not reusable

|-
! O
|
|| Privacy concerns and expected disadvantages
|| Not publishing data due to privacy concerns (sensitive data)
|| UC2: In case of distribution network data this is relevant
|| Data not accessible

|-
! Other
|
|| Conducting FAIR assessments
||
* No qualitative assessment of the data itself (which may be limited)
* Discrepancies between results conducted by humans versus machines
* Sometimes not even DC standards are met
* Metadata is not updated
* Lacking encryption of websites (https)
* Problems assessing whether metadata will be available after data is unavailable
* Interface design/layout may be unclear, incomplete or not intuitive for humans
* Authentication details (user registration login / good or bad, clarify)

||
* UC1 lack of data availability for time-series
||
* Assessment is uncertain (human assessment may need more clarification and understanding)
* Makes research more time-intensive

|}

== FAIR Self-Assessment tool ==

To conduct a human FAIR evaluation for different databases and their respective datasets, different free online tools were considered. Some seemed to be quite useful (e.g. [https://satifyd.dans.knaw.nl/ SATISFYD] ) and some were simple self-evaluation PDFs to be printed and filled out.

We decided to chose the [https://ardc.edu.au/resources/working-with-data/fair-data/fair-self-assessment-tool/ self-assessment tool] provided by the Australian Research Data Commons (ARDC). The decision was based manly upon the fact that the tools questions closely matched the FAIR categories specified by [https://www.doi.org/10.1038/sdata.2016.18 Wilkinson et al., 2016] which would make it easily comparable to the existing preferable FAIR conditions and aid with the repeatability of the results, and the half-automated scoring features that show the extent of the "FAIRness" of the analysed sample as a simple bar chart.

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ All options for each question of the ARDCs self-assessment tool
!colspan="4"|Findable
!colspan="3"|Accessible
!colspan="3"|Interoperable
!colspan="3"|Reusable
|-
| F1. (meta)data are assigned a globally unique and persistent identifier
|| F2. data are described with rich metadata (defined by R1 below)
|| F3. metadata clearly and explicitly include the identifier of the data it describes
|| F4. (meta)data are registered or indexed in a searchable resource
|| A1. (meta)data are retrievable by their identifier using a standardized communications protocol
|| A1.1 the protocol is open, free, and universally implementable and A1.2 the protocol allows for an authentication and authorization procedure, where necessary
|| A2. metadata are accessible, even when the data are no longer available
|| I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
|| I2. (meta)data use vocabularies that follow FAIR principles
|| I3. (meta)data include qualified references to other (meta)data
|| R1.1. (meta)data are released with a clear and accessible data usage license
|| R1.2. (meta)data are associated with detailed provenance
|| R1.3. (meta)data meet domain-relevant community standards
|-
! Does the dataset have any identifiers assigned?
! Is the dataset identifier included in all metadata records/files describing the data?
! How is the data described with metadata?
! What type of repository or registry is the metadata record in?
! How accessible is the data?
! Is the data available online without requiring specialised protocols or tools once access has been approved?
! Will the metadata record be available even if the data is no longer available?
! What (file) format(s) is the data available in?
! What best describes the types of vocabularies/ontologies/tagging schemas used to define the data elements?
! How is the metadata linked to other data and metadata (to enhance context and clearly indicate relationships)?
! Which of the following best describes the license/usage rights attached to the data?
! How much provenance information has been captured to facilitate data reuse?
!
|-
| Globally unique, citable and persistant (e.g. DOI, PURL, ARK or Handle)
|| Yes
|| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
|| Data is in one place but discoverable through several registries
|| Publicly accessible
|| Standard web service API (e.g. OGC)
|| Yes
|| In a structured, open standard, machine-readable format
|| Standardised open and universal using resolvable global identifiers linking to explainations
|| Meadata is represented in a machine readable format, e.g. in a linked data format such as Resource Description Framework (RDF)
|| Standard machine-readable license (e.g. Creative Commons)
|| Fully recorded in a machine-readable format
||
|-
| Web adress (URL)
|| No
|| Comprehensively, but in a text-based, non-standard format
|| Generalist public repository
|| Fully accessible to person who meet explicitly stated conditions, e.g. ethics approval for sensitive data
|| Non-standard web service (e.g. OpenAPI/Swagger/informal API)
|| No
|| In a structured, open standard, non-machine-readable format
|| Standardised vocabularies/ontologies/schema without global identifiers
|| The metadata record includes URI links to related metadata, data and definitions
|| Standard text based license
|| Fully recorded in a text format
||
|-
| Local identifier
||
|| Brief title and description
|| Domain-specific repository
|| A de-identified / modified subset of the data is publicly accessible
|| File download from online location
|| Unsure
|| Mostly in a proprietary format
|| No standards have been applied in the description of data elements
|| There are no links to other metadata
|| Non-standard machine-readably license (clearly indicating under what conditions the data may be reused)
|| Partially recorded
||
|-
| No identifier
||
|| The data is not described
|| Local institutional repository
|| Embaged access after a specific date
|| By individual arrangement
||
||
|| Data elements not described
||
|| Non-standard text-based license
|| No provenance information is recorded
||
|-
|
||
||
|| The data is not described in any repository
|| Unspecified conditional access e.g. contact the data custodian for access
|| No access to data
||
||
||
||
|| No license
||
||
|-
|
||
||
||
|| Access to metadata only
||
||
||
||
||
||
||
||
|-
|
||
||
||
|| No access to data or metadata
||
||
||
||
||
||
||
||
|}

At this point it is important to note that the assessment of databases and/or datasets using this tool is solely considered a human assessment. This may lead to different evaluations by different users. To counteract this phenomenon, it was suggested that the evaluations should be completed using the 'four-eyes principle' to create more verifiable results. Also, it is planned to additionally evaluate the respective databases using an external tool to perform a machine-assessment.

In addition, not all questions are weighted equally and thus a worse result for one question may not have a large impact whereas only choosing the second-best option in another question will bring down the overall FAIR value considerably.

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Description
|-
! rowspan="5" | F
|| F
|| About Findable
|| Making data Findable includes assigning a persistent identifier (like a DOI or Handle ), having rich metadata to describe the data and making sure it is findable through disciplinary and generalist discovery portals (local and international).

|-
| F1
|| About identifiers
|| Identifiers are essential for identifying, finding, retrieving, linking and citing datasets. A Web address (URL) can be used to specify the online location of a resource but over time URLs tend to change which leads to broken links to the data. To be useful, identifiers need to be persistent and unique. Digital Object Identifiers (DOIs) and other persistent identifiers (PIDs) provide a permanent citable reference to a particular dataset. The DOI is a permanent fixed reference to the dataset no matter where it is located online and enables citation and citation metrics. Services to create a persistent identifier are often offered by your affiliated institution or the repository you are using to describe your data. Talk to your library service for more information.
|-
| F2
|| About identifiers
|| The identifier (preferably a persistent identifier) needs to be clearly stated in the metadata record describing the data collection, and also in any associated data files or metadata.

|-
|F3
|| About metadata descriptions
|| Comprehensive metadata records will include descriptive content that facilitates discovery, access and reuse of the data being described. While there is no 'one size fits all' list, comprehensive metadata should include:
* a globally unique persistent identifier e.g. a DOI
* a title
* related people, i.e. the data creator or custodian
* how to access the data and file formats
* a description of how the data were created and how to interpret the data subject or keywords
* citation information that clearly indicates how the data should be cited
* a machine-readable data licence
* provenance and contextual information such as:
* links to related publications, projects, services and software
* methodology and processes involved in data production
* spatial and temporal coverage (if relevant)
* object-level data description

Providing metadata in a standard schema allows it to be read and used by machines as well as humans.

|-
| F4
|| About metadata repositories
|| A rich metadata description alone does not ensure a dataset’s ‘findability’ on the internet; the dataset needs to be registered or indexed in a searchable resource, such as a generalist (e.g. Figshare, Dryad, Zenodo domain-specific (e.g. PANGAEA, ADA, ICPSR), or institutional (e.g. ANU Research Repository , Sydney eScholarship Repository, data.gov.au) data repository or registry. Ideally these repositories/registries are indexed by search engines such as Google and/or Google Scholar.

|-
! rowspan = "4" | A
| A
|| About Accessible
|| To make data accessible may include making the data open using a standardised protocol. However the data does not necessarily have to be open. There are sometimes good reasons why data cannot be made open, for example privacy concerns, national security or commercial interests. If it is not open there should be clarity and transparency around the conditions governing access and reuse
|-
| A1
|| About data accessibility
|| Not all data that is discoverable can be freely accessed. Often there are embargoes, access controls, and access permissions associated with data due to a variety of issues such as privacy and commercial interests. Even with all these issues much sensitive data can be shared. Many other issues that could be perceived as blockers to sharing data may be overcome.
|-
| A1.1/A1.2
|| About protocols
|| Ideally users would like to retrieve appropriate internet content directly and unhindered once they have located it. Internet protocols (e.g. http and ftp) define rules and conventions for communication between devices, and tools and services are available to facilitate this process, e.g. APIs.

Protocols:
* HTTP (Hypertext Transfer Protocol) is the set of rules for transferring files (text, graphic images, sound, video, and other multimedia files) on the World Wide Web. As soon as a Web user opens their Web browser, the user is indirectly making use of HTTP.
* FTP (File Transfer Protocol) is a standard Internet protocol for transmitting files between computers on the Internet over TCP/IP connections. Read more: Web protocols
* API (Application Programming Interface): When information is made available in any machine readable format, it becomes possible to make that information directly available to programs that request that information over the web. An API is the way this information is made directly available to other machines.

|-
| A2
|| About metadata availability
|| Effort is often required to maintain data resources online which can often lead to it being neglected especially over long periods of time. This often leads to broken links between the metadata and the data. Having at least a description of the data in the form of a metadata record means there is a record of the data's existence allowing the possibility of someone tracking it down.

|-
! rowspan="4" | I
|| I
|| About Interoperability
|| To be interoperable (i.e. data that is interpretable by a computer, so that they can be automatically combined with other data) the data will need to use community agreed formats, language and vocabularies. The metadata will also need to use a community agreed standards and vocabularies, and contain links to related information using identifiers.
|-
|| I1
|| About file formats
|| When selecting file formats for archiving, the formats should ideally be:
* Non-proprietary
* Unencrypted
* Uncompressed
* In common usage by the research community
* Adherent to an open, documented standard, such as described by the State of California (see AB 1668, 2007)
* Interoperable among diverse platforms and applications
* Fully published and available royalty-free
* Fully and independently implementable by multiple software providers on multiple platforms without any intellectual property restrictions for necessary technology
* Developed and maintained by an open standards organization with a well-defined inclusive process for evolution of the standard.

From: Stanford University Library - Best practices for file formats
File format examples:
* Structured, open standard, machine-readable format e.g. (text) PDF/A, HTML, Plain text, (images) TIFF, JPEG 2000, GIF, (audio) MP3, AIFF, WAVE, (video) MOV, MPEG, AVI, (Tabular data) CSV
* structured, open standard, non-machine-readable format, e.g. PDF, HTML, JPG
* Proprietary format, e.g. doc (Word), .xls (Excel), .ppt (PowerPoint), .sav

Read more:
* ANDS
* European Data Portal

|-
|| I2
|| About schema standards
|| Schema standards are schemas that have gone through a formal validation process by a standards organisation, such as the International Standards Organisation (ISO) or an equivalent body such as the Dublin Core Metadata Initiative (DCMI); or, commonly used and consistently applied metadata schemas that are well documented, endorsed, and maintained can also become ‘de-facto standards’, e.g. RIF-CS for describing data collections and services used in Research Data Australia.

Read more: Metadata schema

Standardised open and universal schemas, e.g, DataCite Metadata Schema, PROV, Dublin Core
Domain-specific standards, e.g. . HPO - Human Phenotype Ontology, MeSH - Medical Subject Headings , Marine Community Profile, DDI - Data Documentation Initiative For more examples of standards...
Resolvable global identifiers - An identifier is any label used to name something uniquely (whether online or offline). URLs are an example of an identifier. So are serial numbers, and personal names. A persistent identifier is guaranteed to be managed and kept up to date over a defined time period.
Read more: Persistent Identifiers

|-
||I3
|| About metadata links
|| The goal is to create as many meaningful linkages as possible between (meta)data resources to enrich the contextual knowledge about the data, balanced against the time/energy involved in making a good data model.
Machine-readable (meta)data:
* (Meta)data in a format that can be automatically read and processed by a computer, such as CSV, JSON, XML etc. For more information: Open Data Handbook
Linked Data:
* Linked Data (also known as Linking Data) can be applied to improve the exploitation of the “Web of data.” The expression refers to the publishing of structured data in a way that typed links are created between data from different sources to provide a higher level of usability. By using Linked Data, it is possible to find other, related data. Structured data should meet four requirements to be called Linked Data:
* URIs should be assigned to all entities of the dataset.
* HTTP URIs are required to ensure that all entities can be referenced and cited by users and user agents.
* Entities should be described using standard formats such as RDF/XML.
* Links should be created to other, related entity URIs.

All data that fulfil these requirements and are released for the public are called Linked Open Data (LOD). http://www.lesliesikos.com/semantic-web-machine-readable-structured-data-with-meaningful-annotations/
Read more under the heading (meta)data include qualified references to other (meta)data in the following resource: https://www.dtls.nl/fair-data/fair-principles-explained/
For more information on Linked Data standards - RDF - https://www.w3.org/RDF/
Other related linked data standards: OWL - https://www.w3.org/OWL/ , SKOS - https://www.w3.org/2004/02/skos/ , SPARQL - https://www.w3.org/TR/rdf-sparql-query/

|-
! rowspan="3" | R
|| R
|| About Reusable
|| Reusable data should maintain its initial richness. For example, it should not be abridged for the purpose of explaining the findings in one particular publication. It needs a clear machine-readable licence and provenance information on how the data was formed. It should also use discipline-specific data and metadata standards to give it rich contextual information that will allow for accurate interpretation and reuse.

|-
|| R1.1
|| About licenses
|| If data is not licensed no-one else can use it. In Australia, no licence is regarded as the same as 'all rights reserved', confining any reuse to very limited circumstances. Applying a Creative Commons licence to your data is a simple way to ensure that your data can be reused. The less restrictive the licence, the more that can be done with the data.
To make it easy for the Web to know when a work is available under a particular license, a “machine readable” version of the license provides a summary of the key freedoms and obligations written into a format that software systems, search engines, and other kinds of technology can understand.
What appears in the metadata:
This work is licensed under a Creative Commons Attribution 4.0 International License.

Code snippet:

<code>
<a rel="license" href="http://creativecommons.org/licenses/by/4.0/">

<img alt="Creative Commons License" style="border-width:0"

src="https://i.creativecommons.org/l/by/4.0/88x31.png"/>

</a> This work is licensed under a

<a rel="license" href="http://creativecommons.org/licenses/by/4.0/">

Creative Commons Attribution 4.0 International License</a>.
<\code>

Read more: https://www.ands.org.au/working-with-data/publishing-and-reusing-data/licensing-for-reuse

|-
||R1.2
|| About provenance
|| Data provenance is used to document where a piece of data comes from and the process and methodology by which it is produced. It is important to confirm the authenticity of data enabling trust, credibility and reproducibility. This is becoming increasingly important, especially in the eScience community where research is data intensive and often involves complex data transformations and procedures. Read more… Provenance vocabularies such as Provenir Ontology (PROV-O) and others

|}

== Results for ShareWind ==

{| class="mw-collapsible wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ A dataset from the ShareWind platform was analysed using the self-assessment tool provided by ARDC.
! rowspan="5" |
| rowspan="5" |
! Database Code
| rowspan="24" style="background: #ABE" |
| ED4
| ED9
|-
! Database Code
|| SMILES
| ShareWind
|-
! Subdata-Code
|| https://www.ecria-smiles.eu/
| https://sharewind.eu/
|-
! Database/Subdata
|| [https://www.ecria-smiles.eu/data-files/-/document_library/W32cCAfL2jMX/view_file664466?_com_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX_redirect=https%3A%2F%2Fwww.ecria-smiles.eu%3A443%2Fdata-files%2F-%2Fdocument_library%2FW32cCAfL2jMX%2Fview%2F664423%3F_com_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX_redirect%3Dhttps%253A%252F%252Fwww.ecria-smiles.eu%253A443%252Fdata-files%253Fp_p_id%253Dcom_liferay_document_library_web_portlet_DLPortlet_INSTANCE_W32cCAfL2jMX%2526p_p_lifecycle%253D0%2526p_p_state%253Dnormal%2526p_p_mode%253Dview here] (link has 585 characters)
| https://sharewind.eu/records/f9d31abbc2824284b8c1d28894d9619a; https://zenodo.org/record/1162738
|-
! Sample Size
|| 4 KB
| 37 MB
|-
| colspan="6" style="background: #ABE" |
|-
! rowspan="4" | Findable
| F1. (meta)data are assigned a globally unique and persistent identifier
! Does the dataset have any identifiers assigned?
|| Web adress (URL)
| Globally unique, citable and persistant (e.g. DOI, PURL, ARK or Handle)
|-

| F2. data are described with rich metadata (defined by R1 below)
! Is the dataset identifier included in all metadata records/files describing the data?
|| No
| No
|-

| F3. metadata clearly and explicitly include the identifier of the data it describes
! How is the data described with metadata?
|| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
| Comprehensively (see suggestion) using a recognised, formal machine-readable metadata schema
|-

| F4. (meta)data are registered or indexed in a searchable resource
! What type of repository or registry is the metadata record in?
|| Domain-specific repository
| Data is in one place but discoverable through several registries
|-

! rowspan="3" | Accessible

| A1. (meta)data are retrievable by their identifier using a standardized communications protocol
! How accessible is the data?
|| Publicly accessible
| Publicly accessible
|-
| A1.1 the protocol is open, free, and universally implementable; A1.2 the protocol allows for an authentication and authorization procedure, where necessary
! Is the data available online without requiring specialised protocols or tools once access has been approved?
|| File download from online location
| Standard web service API (e.g. OGC)
|-
| A2. metadata are accessible, even when the data are no longer available
! Will the metadata record be available even if the data is no longer available?
|| Unsure
| Yes
|-

! rowspan="3" | Interoperable
| I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
! What (file) format(s) is the data available in?
|| In a structured, open standard, machine-readable format
|| In a structured, open standard, non-machine-readable format
|-
| I2. (meta)data use vocabularies that follow FAIR principles
! What best describes the types of vocabularies/ontologies/tagging schemas used to define the data elements?
|| Standardised vocabularies/ontologies/schema without global identifiers
|| Standardised vocabularies/ontologies/schema without global identifiers
|-
| I3. (meta)data include qualified references to other (meta)data
! How is the metadata linked to other data and metadata (to enhance context and clearly indicate relationships)?
|| There are no links to other metadata
|| Metadata is represented in a machine readable format, e.g. in a linked data format such as Resource Description Framework (RDF)
|-

! rowspan="3" | Reusable
| R1.1. (meta)data are released with a clear and accessible data usage license
! Which of the following best describes the license/usage rights attached to the data?
|| No license
| Standard machine-readable license (e.g. Creative Commons)
|-
| R1.2. (meta)data are associated with detailed provenance
! How much provenance information has been captured to facilitate data reuse?
|| Partially recorded
|| Partially recorded
|-
| R1.3. (meta)data meet domain-relevant community standards
|
|
|
|-
| colspan="6" style="background: #ABE" |
|-
! Total across F.A.I.R
|
! Low: < 40%; Medium: 50-60%; High: > 60%
|| 45 %
| 85 %
|-
| colspan="6" style="background: #ABE" |
|-
! Remarks about database
||
||
||
|| The data is provided by the open-source platform Zenodo, not the project website itself. A very positive aspect is that information on different versions of the data as well as the number of views and downloads is available.
|-
! Identified gaps
||
||
||
* No DOI, only URL
* Extremely long URL link spanning several lines with 585 characters, that can easily become corrupted
* No links to other metadata
* No license information given
* Provenance not fully recorded

||
* Dataset identifiers should be included in the files describing the data
* Provenance details are only partially recorded with a note with contact name for further information
|}

= Dublin Core Evaluation =

== Dublin Core Elements ==
{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ The DCMI Metadata Terms lists the current set of the Dublin Core vocabulary. Source: [https://dublincore.org/specifications/dublin-core/dcmi-terms/ Dublin Core Metadata Initiative]
| 1 || abstract || 11 || contributor || 21 || extent || 31 || isReplacedBy || 41 || publisher || 51 || tableOfContents
|-
| 2 || accessRights || 12 || coverage || 22 || format || 32 || isRequiredBy || 42 || references || 52 || temporal
|-
| 3 || accrualMethod || 13 || created || 23 || hasFormat || 33 || issued || 43 || relation || 53 || title
|-
| 4 || accrualPeriodicity || 14 || creator || 24 || hasPart || 34 || isVersionOf || 44 || replaces || 54 || type
|-
| 5 || accrualPolicy || 15 || date || 25 || hasVersion || 35 || language || 45 || requires || 55 || valid
|-
| 6 || alternative || 16 || dateAccepted || 26 || identifier || 36 || license || 46 || rights || ||
|-
| 7 || audience || 17 || dateCopyrighted || 27 || instructionalMethod || 37 || mediator || 47 || rightsHolder || ||
|-
| 8 || available || 18 || dateSubmitted || 28 || isFormatOf || 38 || medium || 48 || source || ||
|-
| 9 || bibliographicCitation || 19 || description || 29 || isPartOf || 39 || modified || 49 || spatial || ||
|-
| 10 || conformsTo || 20 || educationLevel || 30 || isReferencedBy || 40 || provenance || 50 || subject || ||
|}

'''Metadata Category Types according to [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO]'''

There are three main types of metadata:
* Descriptive metadata describes a resource for purposes such as discovery and identification. It can include elements such as title, abstract, author, and keywords.
* Structural metadata indicates how compound objects are put together, for example, how pages are ordered to form chapters.
* Administrative metadata provides information to help manage a resource, such as when and how it was created, file type and other technical information, and who can access it. There are several subsets of administrative data; two that sometimes are listed as separate metadata types are:
** Rights management meta-data, which deals with intellectual property rights,and
** Preservation metadata, which contains information needed to archive and preserve a resource.
''

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Simple Dublin Core Elements. 15 DC elements with their (shortened) official definitions and examples.
! DC element name !! DC definition ([https://www.dublincore.org/specifications/dublin-core/dces/ DCMI]) !! Example (Energy Domain) !! Category (determined with the aid of [https://web.archive.org/web/20141107022958/http:/www.niso.org/publications/press/UnderstandingMetadata.pdf NISO])
|-
! Title
| A name given to the resource || Outdoor monitoring of a hybrid micro-CPV solar panel with integrated micro-tracking and diffuse capture || Descriptive
|-
! Subject
| The topic of the resource || concentrator photovoltaics; CPV; rooftop photovoltaics; integrated tracking || Descriptive
|-
! Description
| An account of the resource || Dataset from the outdoor characterization of a B Series module from Insolight at the rooftop of the Instituto de Energía Solar - Universidad Politécnica de Madrid. …. || Descriptive
|-
! Creator
| An entity primarily responsible for making the resource || Stephen Askins; Gaël Nardin || Descriptive
|-
! Publisher
| An entity responsible for making the resource available || Stephen Askins || Descriptive
|-
! Contributor
| An entity responsible for making contributions to the resource || Stephen Askins || Descriptive
|-
! Date
| A point or period of time associated with an event in the lifecycle of the resource || 2019-07-23; Date will be associated with the creation or availability of the resource. || Administrative
|-
! Type
| The nature or genre of the resource || info:eu-repo/semantics/other; dataset || Descriptive
|-
! Format
| The file format, physical medium, or dimensions of the resource || image
|| Structural
|-
! Identifier
| An unambiguous reference to the resource within a given context || https://zenodo.org/record/3346823; 10.5281/zenodo.3346823; oai:zenodo.org:3346823 || Descriptive
|-
! Source
| A related resource from which the described resource is derived || RC607.A26W574 1996 [where "RC607.A26W574 1996" is the call number of the print version of the resource, from which the present version was scanned] || Descriptive
|-
! Language
| A language of the resource || eng || Descriptive
|-
! Relation
| A related resource || info:eu-repo/grantAgreement/EC/H2020/787289/ || Descriptive
|-
! Coverage
| The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant || 1995-1996; Madrid, Spain || Descriptive
|-
! Rights
| Information about rights held in and over the resource || info:eu-repo/semantics/openAccess; https://creativecommons.org/licenses/by/4.0/legalcode || Administrative
|}

{| class="wikitable" style="margin-left: auto; margin-right: auto; border: none;"
|+ Non-DC elements

! Non-DC element name !! Definition !! Example !! Category
|-
! Date of availability
| Since when is the data available
||
|| Impact/Exploitation

|-
! Number of users
| Is there any information on the number of accesses or users?
||
|| Impact/Exploitation

|-
! Number of scientific publications where data is cited/used
| Is there any information on the number of citations/uses?
||
|| Impact/Exploitation

|-
! Curation activities (is the versioning ongoing)
| Update, maintainance
||
|| Impact/Exploitation
|}

Attempting to put a value on Impact/Exploitation:
* '''ENTSO-E''': Found 172 results in Advanced Search (ALL) in Web of Science ([https://apps.webofknowledge.com/ WoSc]) (16/06/2020). Citations of all these search result add up to 714. This however does not mean, that all search results really explicitly used data from ENTSO-E. Some (for sure, for some were found) only mentioned it saying they adoped workflow etc. from the site.
* '''SMARD''': Found 82 results in Advanced Search (ALL=(SMARD)) in Web of Science ([https://apps.webofknowledge.com/ WoSc]) (16/06/2020). However: SMARD is also an acronym for ''Spinal muscular atrophy with respiratory distress (SMARD)''. This somewhat complicates results and needs some further discussion. All 82 search results accumumlated 2359 citations themselves.

Gap analysis

2020-08-27T13:35:26Z

Christopherbs: /* FAIR Self-Assessment tool */

Gap analysis

2020-08-27T11:49:22Z

Christopherbs: /* Results for ShareWind */

Gap analysis

2020-08-27T11:38:06Z

Christopherbs: /* Results for ShareWind */

Gap analysis

2020-08-27T11:33:53Z

Christopherbs: /* Results for ShareWind */

Gap analysis

2020-08-27T11:27:45Z

Christopherbs: /* Results for ShareWind */

Gap analysis

2020-08-27T11:24:41Z

Christopherbs: /* Results for ShareWind */

Gap analysis

2020-08-27T09:38:28Z

Christopherbs: /* Results for ShareWind */

Gap analysis

2020-08-27T09:31:27Z

Christopherbs: /* Results for ShareWind */

Gap analysis

2020-08-27T09:09:48Z

Christopherbs: /* FAIR Analysis */

Gap analysis

2020-08-27T09:09:22Z

Christopherbs: /* Dublin Core */

Gap analysis

2020-08-27T08:51:30Z

Christopherbs: /* FAIR Self-Assessment tool */

Gap analysis

2020-08-26T13:36:51Z

Christopherbs: /* Results for ShareWind */

Gap analysis

2020-08-26T13:33:20Z

Christopherbs: /* Results for ShareWind */

Gap analysis

2020-08-26T13:30:32Z

Christopherbs: /* FAIR Analysis */

Gap analysis

2020-08-20T14:53:06Z

Christopherbs: /* FAIR Self-Assessment tool */

Gap analysis

2020-08-20T14:52:20Z

Christopherbs: /* Dublin Core */

Gap analysis

2020-08-20T14:51:59Z

Christopherbs: /* Dublin Core */

Gap analysis

2020-08-20T14:51:41Z

Christopherbs: /* FAIR Self-Assessment tool */