Pinpoint target groups
The digitalisation of the energy sector is happening. A human-centred design approach is therefore needed to ensure that it takes place with and for the benefit of the people. However, the development and implementation of interoperability and machine-actionability of data require substantial resources.
Targeted user groups
Making data FAIR in reality requires a careful consideration of who will be the users of the data. User groups may range from different academic disciplines to the general public. It is important that your FAIRification strategy addresses main target group(s) as directly as possible. The EERAdata project has identified the following target groups of low carbon research:
- Researchers — Energy domain experts require context information with high granularity and rich provenance to assess data quality and relevance, precise information on usage rights, and intellectual property rights (IPR) requirements. Interdisciplinary scientists need information on the context of data and provenance (mostly on aggregated levels) and precise information on usage rights and IPR.
- Science funders — Need to monitor, adjust and plan funding policies and principles to better direct R&D investments and to ensure the impact of policy measures. Typically, this application is at a high level of aggregation, and information should be easy to disseminate. A pivotal interest is that the funding agency be acknowledged in the metadata.
- Planners and decision-makers — They may reuse data, analyze some data, and publish aggregated data and decisions. This involves a middle to high level of aggregation, information on data context and provenance.
- Energy sector and other industries — Technical and operational planners and decision-makers, energy market operators draw on expert knowledge and re-use and analyze some data on all aggregation levels. They require information on context, provenance, aggregated data, as well as IPR.
- General public — The group informs itself to adjust behavior and practices. A high level of aggregation is needed to make data easy to understand and navigate.
- Data scientists — Data engineers, software and algorithm developers code, test, and validate software with existing data. They need concise metadata to integrate data sources within software tools, machine-actionable open file formats, agreed standards, terminologies, and interoperability protocols.
- Publishers, librarians, and data curators — publish, store, and archive research data. They may re-use data to link them to metrics such as access statistics and to cross-reference.
Running a gender-inclusive project
The IT and low carbon energy research domains are fields, where female researchers are notoriously underrepresented (UNESCO 2019, Women in Science). Despite this fact, many female researchers are pioneering opening and FAIRification activities in the energy and other domains.
Background: In English, there is a difference between “grammatical gender”, “gender as a social construct” (which refers to the roles, behaviors, activities and attributes that a given society at a certain time considers appropriate for men or women) and “sex” as a biological characteristic of living beings. English has very few gender markers: the pronouns and possessives (he, she, her and his); and some nouns and forms of address. Most English nouns do not have grammatical gender forms (teacher, president), whereas a few nouns are specifically masculine or feminine (actor/actress, waiter/waitress). Some nouns that once ended in -man now have neutral equivalents that are used to include both genders (police officer for policeman/policewoman, spokesperson for spokesman, chair/chairperson for chairman). A challenge for gender-inclusive communication in English is the use of the masculine form by default. For example, “Every Permanent Representative must submit his credentials to Protocol.” Some tips for the use of non-discriminatory English are collected on the EERAdata wiki.
Avoiding research bias
Check out the peer-reviewed publication below on how to avoid research bias, but key insights are:
- Selective knowledge representation leads to a bias
- To avoid selection bias, a systematic review of the ontologies by experts and all relevant stakeholders should be undertaken
- Rich metadata is the way forward to reveal and track potential data quality issues
- Open metadata is essential
Wierling A, Schwanitz VJ, Altinci S, Bałazińska M, Barber MJ, Biresselioglu ME, Burger-Scheidlin C, Celino M, Demir MH, Dennis R, Dintzner N, el Gammal A, Fernández-Peruchena CM, Gilcrease W, Gładysz P, Hoyer-Klick C, Joshi K, Kruczek M, Lacroix D, Markowska M, Mayo-García R, Morrison R, Paier M, Peronato G, Ramakrishnan M, Reid J, Sciullo A, Solak B, Suna D, Süß W, Unger A, Fernandez Vanoni ML, Vasiljevic N. FAIR Metadata Standards for Low Carbon Energy Research—A Review of Practices and How to Advance. Energies. 2021; 14(20):6692. https://doi.org/10.3390/en14206692