@article {1534998,
	title = {Evaluating FAIR maturity through a scalable, automated, community-governed framework},
	journal = {Scientific Data},
	volume = {6},
	number = {174},
	year = {2019},
	note = {See also: Data Science},
	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 {\textendash} community-authored specifications that delimit a specific automatically-measurable FAIR behavior; (2) Compliance Tests {\textendash} 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 {\textquotedblleft}sees{\textquotedblright} 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.},
	url = {https://www.nature.com/articles/s41597-019-0184-5},
	author = {Mark D. Wilkinson and Michel Dumontier and Susanna-Assunta Sansone and Luiz Olavo Bonino da Silva Santos and Mario Prieto and Dominique Batista and McQuilton, Peter and Tobias Kuhn and Philippe Rocca-Serra and Mercѐ Crosas and Erik Schultes}
}
