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FAIR - more than a happy feeling!

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"


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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