FAIR guiding principles
These principles are outlined in Box #2 of Wilkinson et al.[1]
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
In order to react to inconsistent interpretations of these principles, Jacobsen et al. [2] specified implications for possible implementations of these principles, starting from the objectives behind the FAIR principles:
Findability: Digital resources should be easy to find for both humans and computers. Extensive machine-actionable metadata are essential for automatic discovery of relevant datasets and services, and are therefore an essential component of the FAIRification process ...
Accessibility: Protocols for retrieving digital resources should be made explicit, for both humans and machines, including well-defined mechanisms to obtain authorization for access to protected data.
Interoperability: When two or more digital resources are related to the same topic or entity, it should be possible for machines to merge the information into a richer, unified view of that entity. Similarly, when a digital entity is capable of being processed by an online service, a machine should be capable of automatically detecting this compliance and facilitating the interaction between the data and that tool. This requires that the meaning (semantics) of each participating resource – be they data and/or services service – is clear.
Reusability: Digital resources are sufficiently well described for both humans and computers, such that a machine is capable of deciding: if a digital resource should be reused (i.e., is it relevant to the task at-hand?); if a digital resource can be reused, and under what conditions (i.e., do I fulfill the conditions of reuse?); and who to credit if it is reused.
In the paper of Jacobsen [2], recommendations are given for implementation of the 15 principles. Here a few quotes:
Notes
Template:Reflist- ↑ Wilkinson et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci. Data 3:160018 doi: 10.1038/sdata.2016.18. Template:Cite journal
- ↑ 2.0 2.1 Jacobsen et al. FAIR Principles: Interpretations and Implementation Considerations. Data Intelligence 2:10-29 (2020) doi: 10.1162/dint_r_00024. Cite error: Invalid
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