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Evaluating FAIR-Compliance Through an Objective, Automated, Community-Governed Framework

View ORCID ProfileMark D Wilkinson, View ORCID ProfileMichel Dumontier, View ORCID ProfileSusanna-Assunta Sansone, View ORCID ProfileLuiz Olavo Bonino da Silva Santos, View ORCID ProfileMario Prieto, View ORCID ProfilePeter McQuilton, View ORCID ProfileJulian Gautier, View ORCID ProfileDerek Murphy, View ORCID ProfileMercѐ Crosas, View ORCID ProfileErik Schultes
doi: https://doi.org/10.1101/418376
Mark D Wilkinson
1Center for Plant Biotechnology and Genomics UPM-INIA, Madrid, Spain.
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Michel Dumontier
2Institute of Data Science, Maastricht University, Maastricht, The Netherlands
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Susanna-Assunta Sansone
3Oxford e-Research Centre, Department of Engineering Science, University of Oxford, Oxford, UK
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Luiz Olavo Bonino da Silva Santos
4GO FAIR International Support and Coordination Office, Leiden, The Netherlands
5Leiden University Medical Center, Leiden, The Netherlands
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Mario Prieto
1Center for Plant Biotechnology and Genomics UPM-INIA, Madrid, Spain.
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Peter McQuilton
3Oxford e-Research Centre, Department of Engineering Science, University of Oxford, Oxford, UK
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Julian Gautier
6Institute for Quantitative Social Science, Harvard University, Cambridge, USA
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Derek Murphy
6Institute for Quantitative Social Science, Harvard University, Cambridge, USA
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Mercѐ Crosas
6Institute for Quantitative Social Science, Harvard University, Cambridge, USA
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Erik Schultes
4GO FAIR International Support and Coordination Office, Leiden, The Netherlands
5Leiden University Medical Center, Leiden, The Netherlands
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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 Evaluator, 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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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted September 25, 2018.
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Evaluating FAIR-Compliance Through an Objective, Automated, Community-Governed Framework
Mark D Wilkinson, Michel Dumontier, Susanna-Assunta Sansone, Luiz Olavo Bonino da Silva Santos, Mario Prieto, Peter McQuilton, Julian Gautier, Derek Murphy, Mercѐ Crosas, Erik Schultes
bioRxiv 418376; doi: https://doi.org/10.1101/418376
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Evaluating FAIR-Compliance Through an Objective, Automated, Community-Governed Framework
Mark D Wilkinson, Michel Dumontier, Susanna-Assunta Sansone, Luiz Olavo Bonino da Silva Santos, Mario Prieto, Peter McQuilton, Julian Gautier, Derek Murphy, Mercѐ Crosas, Erik Schultes
bioRxiv 418376; doi: https://doi.org/10.1101/418376

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