Evaluating FAIR maturity through a scalable, automated, community-governed framework

Citation:

Wilkinson MD, Dumontier M, Sansone S-A, Olavo L, Prieto M, Batista D, McQuilton P, Kuhn T, Rocca-Serra P, Crosas M, et al. Evaluating FAIR maturity through a scalable, automated, community-governed framework. Nature-Springer Scientific Data [Internet]. 2019;6 (174).
Evaluating FAIR maturity through a scalable, automated, community-governed framework

Abstract:

Transparent evaluations of FAIRness are increasingly required by a wide range of stakeholders, from scientists to publishers, funding agencies and policy makers. We propose a scalable, automatable framework to evaluate digital resources that encompasses measurable indicators, open source tools, and participation guidelines, which come together to accommodate domain relevant community-defined FAIR assessments. The components of the framework are: (1) Maturity Indicators – community-authored specifications that delimit a specific automatically-measurable FAIR behavior; (2) Compliance Tests – small Web apps that test digital resources against individual Maturity Indicators; and (3) the Evaluator, a Web application that registers, assembles, and applies community-relevant sets of Compliance Tests against a digital resource, and provides a detailed report about what a machine “sees” when it visits that resource. We discuss the technical and social considerations of FAIR assessments, and how this translates to our community-driven infrastructure. We then illustrate how the output of the Evaluator tool can serve as a roadmap to assist data stewards to incrementally and realistically improve the FAIRness of their resources.

Publisher's Version

See also: Data Science
Last updated on 11/23/2020