Anusuriya Devaraju , Mustapha Mokrane, Linas Cepinskas, Robert Huber, Patricia Herterich, Jerry de Vries, Vesa Akerman, Hervé L’Hours, Joy Davidson, Michael Diepenbroek
Data Science Journal (Special Collection Research Data Alliance Results)
Funders and policy makers have strongly recommended the uptake of the FAIR principles in scientific data management. Several initiatives are working on the implementation of the principles and standardized applications to systematically evaluate data FAIRness. This paper presents practical solutions, namely metrics and tools, developed by the FAIRsFAIR project to pilot the FAIR assessment of research data objects in trustworthy data repositories. The metrics are mainly built on the indicators developed by the RDA FAIR Data Maturity Model Working Group. The tools’ design and evaluation followed an iterative process. We present two applications of the metrics: an awareness-raising self-assessment tool and an automated FAIR data assessment tool. Initial results of testing the tools with researchers and data repositories are discussed, and future improvements suggested including the next steps to enable FAIR data assessment in the broader research data ecosystem.
How to Cite: Devaraju, A., Mokrane, M., Cepinskas, L., Huber, R., Herterich, P., de Vries, J., Akerman, V., L’Hours, H., Davidson, J. and Diepenbroek, M., 2021. From Conceptualization to Implementation: FAIR Assessment of Research Data Objects. Data Science Journal, 20(1), p.4. DOI: http://doi.org/10.5334/dsj-2021-004