Research Article: “Evaluating FAIR-Compliance Through an Objective, Automated, Community-Governed Framework” (Preprint)

The following research article (preprint) was posted today on bioRxiv.
Title
Evaluating FAIR-Compliance Through an Objective, Automated, Community-Governed Framework
Authors
Mark D. Wilkinson
Center for Plant Biotechnology and Genomics UPM-INIA, Madrid, Spain
Michel Dumontier
Institute of Data Science, Maastricht University, Maastricht, The Netherlands
Susanna-Assunta Sansone
Oxford e-Research Centre, Department of Engineering Science, University of Oxford, Oxford, UK
Luiz Olavo Bonino da Silva Santos
GO FAIR International Support and Coordination Office, Leiden, The Netherlands; Leiden University Medical Center, Leiden, The Netherlands
Mario Prieto
Center for Plant Biotechnology and Genomics UPM-INIA, Madrid, Spain
Peter McQuilton
Oxford e-Research Centre, Department of Engineering Science, University of Oxford, Oxford, UK
Julian Gautier
Institute for Quantitative Social Science, Harvard University, Cambridge, USA
Derek Murphy
Institute for Quantitative Social Science, Harvard University, Cambridge, USA
Mercѐ Crosas
Institute for Quantitative Social Science, Harvard University, Cambridge, USA
Erik Schultes
GO FAIR International Support and Coordination Office, Leiden, The Netherlands; Leiden University Medical Center, Leiden, The Netherlands
Source
via arXiv
September 16, 2018
doi: 10.1101/418376
Abstract

With the increased adoption of the FAIR Principles, a wide range of stakeholders, from scientists to publishers, funding agencies and policy makers, are seeking ways to transparently evaluate resource FAIRness. We describe the FAIR valuator, a software infrastructure to register and execute tests of compliance with the recently published FAIR Metrics. The Evaluator enables digital resources to be assessed objectively and transparently. We illustrate its application to three widely used generalist repositories – Dataverse, Dryad, and Zenodo – and report their feedback. Evaluations allow communities to select relevant Metric subsets to deliver FAIRness measurements in diverse and specialized applications. Evaluations are executed in a semi-automated manner through Web Forms filled-in by a user, or through a JSON-based API. A comparison of manual vs automated evaluation reveals that automated evaluations are generally stricter, resulting in lower, though more accurate, FAIRness scores. Finally, we highlight the need for enhanced infrastructure such as standards registries, like FAIRsharing, as well as additional community involvement in domain-specific data infrastructure creation.

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