Data Science Journal · 2020 · 81 citations · 2 references
Many tools and methods have been created to assess FAIRness, but differing interpretations of the FAIR principles make comparing their results challenging. The authors intend to refine the FAIR Data Maturity Model by incorporating user experience into its future development. The RDA FAIR Data Maturity Model delivers prioritized indicators and guidelines that serve as a common language for comparing FAIR assessments, enabling researchers, stewards, policymakers, and funders to evaluate and improve data reuse, and is publicly available to promote practical adoption.
In the past years, many methodologies and tools have been developed to assess the FAIRness of research data. These different methodologies and tools have been based on various interpretations of the FAIR principles, which makes comparison of the results of the assessments difficult. The work in the RDA FAIR Data Maturity Model Working Group reported here has delivered a set of indicators with priorities and guidelines that provide a 'lingua franca' that can be used to make the results of the assessment using those methodologies and tools comparable. The model can act as a tool that can be used by various stakeholders, including researchers, data stewards, policy makers and funding agencies, to gain insight into the current FAIRness of data as well as into the aspects that can be improved to increase the potential for reuse of research data. Through increased efficiency and effectiveness, it helps research activities to solve societal challenges and to support evidence-based decisions. The Maturity Model is publicly available and the Working Group is encouraging application of the model in practice. Experience with the model will be taken into account in the further development of the model.
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The FAIR Guiding Principles for scientific data management and stewardship
Mark D. Wilkinson, Michel Dumontier, IJsbrand Jan Aalbersberg et al. · Scientific Data · 2016 · 16.6K citations · Full text