FAIR - more than a happy feeling!

The FAIR Principles for publishing scientific data say that data should be Findable, Accessible, Interoperable, and Reusable. Adherence to the FAIR Principles allows machines to correctly identify and reuse data without human intervention. But, how do you know if your data is FAIR? In this publication, we provide the first community-driven, automated evaluations of FAIRness, where a computer will explore your data and provide you a "FAIRness Profile" that helps you to learn what aspects of FAIR are successfully found in your data.

 

Although the word "FAIR" is associated with positivity and openness, FAIR data experts often point out that "FAIR is more than just a warm, fuzzy feeling!"

The FAIR Principles aim to provide high-level guidelines that guide a data provider toward publication practices that are increasingly intended to be accessed by machines, rather than humans. Since machine readability is the goal, it makes sense to build machines that are capable of testing if this goal has been achieved.

In this paper, we provide a software framework and a straightforward website that allows anyone to apply FAIR "Maturity Indicators" to a data source in order to evaluate how well it complies with each of the FAIR Principles. In addition, we provide 22 "starter-pack" tests that evaluate basic aspects of FAIRness. We intend these 22 tests to be extended by individual communities and other stakeholders who have more detailed requirements for FAIRness.

Together, these resources provide an objective and automated way to test your "FAIRness"

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Original Paper:

Wilkinson, M.D., Dumontier, M., Sansone, S.-A., Santos, L.O.B. da S., Prieto, M., Batista, D., McQuilton, P., Kuhn, T., Rocca-Serra, P., Crosas, M., Schultes, E. 2019. Evaluating FAIR maturity through a scalable, automated, community-governed framework. Scientific Data 6, 1–12. DOI: 10.1038/s41597-019-0184-5

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