FAIR Principles: Interpretations and Implementation Considerations

Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2022-11-16 Cooperative journals: 《数据智能（英文）》

Jacobsen, Annika Azevedo, Ricardo De Miranda Juty, Nick Batista, Dominique Coles, Simon Cornet, Ronald Courtot, Melanie Crosas, Merce Dumontier, Michel Evelo, Chris T. Goble, Carole Guizzardi, Giancarlo Hansen, Karsten Kryger Hasnain, Ali Hettne, Kristina Heringa, Jaap Hooft, Rob W. W. Imming, Melanie Jeffery, Keith G. Kaliyaperumal, Rajaram

Abstract：

The FAIR principles have been widely cited, endorsed and adopted by a broad range of stakeholders since their publication in 2016. By intention, the 15 FAIR guiding principles do not dictate specific technological implementations, but provide guidance for improving Findability, Accessibility, Interoperability and Reusability of digital resources. This has likely contributed to the broad adoption of the FAIR principles, because individual stakeholder communities can implement their own FAIR solutions. However, it has also resulted in inconsistent interpretations that carry the risk of leading to incompatible implementations. Thus, while the FAIR principles are formulated on a high level and may be interpreted and implemented in different ways, for true interoperability we need to support convergence in implementation choices that are widely accessible and (re)-usable. We introduce the concept of FAIR implementation considerations to assist accelerated global participation and convergence towards accessible, robust, widespread and consistent FAIR implementations. Any self-identified stakeholder community may either choose to reuse solutions from existing implementations, or when they spot a gap, accept the challenge to create the needed solution, which, ideally, can be used again by other communities in the future. Here, we provide interpretations and implementation considerations (choices and challenges) for each FAIR principle.

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FAIR Convergence Matrix: Optimizing the Reuse of Existing FAIR-Related Resources

Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2022-11-16 Cooperative journals: 《数据智能（英文）》

Sustkova, Hana Pergl Hettne, Kristina Maria Wittenburg, Peter Jacobsen, Annika Kuhn, Tobias Pergl, Robert Slifka, Jan McQuilton, Peter Magagna, Barbara Sansone, Susanna-Assunta Stocker, Markus Imming, Melanie Lannom, Larry Musen, Mark Schultes, Erik

Abstract： The FAIR principles articulate the behaviors expected from digital artifacts that are Findable, Accessible, Interoperable and Reusable by machines and by people. Although by now widely accepted, the FAIR Principles by design do not explicitly consider actual implementation choices enabling FAIR behaviors. As different communities have their own, often well-established implementation preferences and priorities for data reuse, coordinating a broadly accepted, widely used FAIR implementation approach remains a global challenge. In an effort to accelerate broad community convergence on FAIR implementation options, the GO FAIR community has launched the development of the FAIR Convergence Matrix. The Matrix is a platform that compiles for any community of practice, an inventory of their self-declared FAIR implementation choices and challenges. The Convergence Matrix is itself a FAIR resource, openly available, and encourages voluntary participation by any self-identified community of practice (not only the GO FAIR Implementation Networks). Based on patterns of use and reuse of existing resources, the Convergence Matrix supports the transparent derivation of strategies that optimally coordinate convergence on standards and technologies in the emerging Internet of FAIR Data and Services.

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