FAIR principles: interpretations and implementation considerations

Jacobsen, A.; Miranda Azevedo, R. de; Juty, N.; Batista, D.; Coles, S.; Cornet, R.; ... ; Schultes, E. (2020)

Article / Letter to editor

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.

• FAIR Principles
• FAIR data
• FAIR guiding principles
• FAIR implementation
• FAIR convergence
• FAIR communities
• choices and challenges

• All authors: Jacobsen, A.; Miranda Azevedo, R. de; Juty, N.; Batista, D.; Coles, S.; Cornet, R.; Courtot, M.; Crosas, M.; Dumontier, M.; Evelo, C.T.; Goble, C.; Guizzardi, G.; Kryger Hansen, K.; Hasnain, A.; Hettne, K.; Heringa, J.; Hooft, R.W.W.; Imming, M.; Jeffery, K.G.; Kaliyaperumal, R.; Kersloot, M.G.; Kirkpatrick, C.R.; Kuhn, T.; Labastida, I.; Magagna, B.; McQuilton, P.; Meyers, N.; Montesanti, A.; Reisen, M.E.H. van; Rocca-Serra, P.; Pergl, R.; Sansone, S.; Olavo Bonino da Silva Santos, L.; Schneider, J.; Strawn, G.; Thompson, M.; Waagmeester, A.; Weigel, T.; Wilkinson, M.D.; Willighagen, E.; Wittenburg, P.; Roos, M.; Mons, B.; Schultes, E.
• Date: 2020
• Journal: Data Intelligence
• Volume: 2
• Issue: 1-2
• Pages: 10 - 29
• DOI: doi:10.1162/dint_r_00024
• Link: http://www.data-intelligence-journal.org/p/33/