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

Clicks: 333
ID: 53213
2019
Article Quality & Performance Metrics
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Combines engagement data with AI-assessed academic quality
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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.
Reference Key
wilkinson2019evaluatingscientific Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Wilkinson, Mark D;Dumontier, Michel;Sansone, Susanna-Assunta;Bonino da Silva Santos, Luiz Olavo;Prieto, Mario;Batista, Dominique;McQuilton, Peter;Kuhn, Tobias;Rocca-Serra, Philippe;Crosas, Mercѐ;Schultes, Erik;
Journal scientific data
Year 2019
DOI
10.1038/s41597-019-0184-5
URL
Keywords
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