FAIR Principles


The FAIR Principles are findability (F), accessibility (A), interoperability (I), and reusability (R) and delineate requirements that allow for data sharing such that data reuse is possible, as put forth in a paper in Scientific Data in 2016 by members of the organization Force11. Findability requires rich published metadata that is both human and machine-readable and for the metadata to include a persistent unique identifier for the data. Accessibility requires that there is a clear protocol for accessing the data. This does not mean that all data must be freely downloadable, only that the process for gaining access to it is transparent. Interoperability requires that the data is represented in a format that is formally defined and able to be integrated with other data. The data should also be in a format that can be accessed and modified or analyzed by common analysis, storage, and processing tools. The ultimate goal of FAIR is to optimize the reuse of data. Reusability requires that the data are in a domain-relevant data standard, that the conditions for usage are clear, and that the metadata provides sufficient attributes for meaningful reuse.  In addition, data should be well-described so that they can be replicated and/or combined in different settings.

Building on the FAIR Principles are the CARE Principles for Indigenous Data Governance.

Further Resources

Original 2016 paper that defined the FAIR principles:

Wilkinson MD, Dumontier M, Aalbersberg IJ, et al. The FAIR Guiding Principles for scientific data management and stewardship [published correction appears in Sci Data. 2019 Mar 19;6(1):6]. Sci Data. 2016;3:160018. Published 2016 Mar 15. https://doi.org/10.1038/sdata.2016.18.

Carroll, S.R., Herczog, E., Hudson, M. et al. Operationalizing the CARE and FAIR Principles for Indigenous data futures. Sci Data 8, 108 (2021). https://doi.org/10.1038/s41597-021-00892-0

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