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.
journal_name
Sci Datajournal_title
Scientific dataauthors
Wilkinson MD,Dumontier M,Sansone SA,Bonino da Silva Santos LO,Prieto M,Batista D,McQuilton P,Kuhn T,Rocca-Serra P,Crosas M,Schultes Edoi
10.1038/s41597-019-0184-5subject
Has Abstractpub_date
2019-09-20 00:00:00pages
174issue
1issn
2052-4463pii
10.1038/s41597-019-0184-5journal_volume
6pub_type
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