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bioRxiv · 10.1101/649202

Evaluating FAIR Maturity Through a Scalable, Automated, Community-Governed Framework

Wilkinson, M. D.·Dumontier, M.·Sansone, S.-A.·da Silva Santos, L. O. B.·Prieto Godoy, M.·Batista, D.·McQuilton, P.·Kuhn, T.·Rocca-Serra, P.·Crosas, M.·Schultes, E.
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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.

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Wilkinson, M. D., Dumontier, M., Sansone, S.-A., da Silva Santos, L. O. B., Prieto Godoy, M., Batista, D., McQuilton, P., Kuhn, T., Rocca-Serra, P., Crosas, M., Schultes, E.. 2019-05-28. Evaluating FAIR Maturity Through a Scalable, Automated, Community-Governed Framework. https://doi.org/10.1101/649202

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