Towards FAIRness literacy – new publication

We are pleased to announce that a new paper entitled ‘FAIRness Literacy: The Achilles’ Heel of Applying FAIR Principles‘ written as part of the RDA SHARC Interest Group’s ‘FAIR criteria task’ was published in the CoData Data Science Journal on the 12th August 2020.

This work recommends that researchers should be supported by data management professionals (not only data stewards), organised in networks and embedded in institutions. In order to enhance treatment of data according to the FAIR principles, we suggest that organisations should be assessed on the basis of how well they support their researchers in becoming FAIR advocates.

FAIRification can be schematized as a wheel describing iterative quality steps that need to be approved by the community throughout the process (as per the figure above). This schema displays the ‘preparing’ and ‘training’ phases as conditions of pre-FAIRification. The pre-FAIRification processes must be community-approved at each iteration. The FAIRification steps ‘check’ and ‘adjust’ implementation must be approved by the community before a new iteration.

This practice paper was partly supported by the Data Strand of the PARSEC project and we encourage you to read and cite it as follows:

David, R., Mabile, L., Specht, A., Stryeck, S., Thomsen, M., Yahia, M., Jonquet, C., Dollé, L., Jacob, D., Bailo, D., Bravo, E., Gachet, S., Gunderman, H., Hollebecq, J.-E., Ioannidis, V., Le Bras, Y., Lerigoleur, E., Cambon-Thomsen, A. and the Research Data Alliance–SHAring Reward and Credit (SHARC) Interest Group (2020). FAIRness Literacy: The Achilles’ Heel of Applying FAIR Principles. Data Science Journal, 19(1), p.32. DOI: