The new paper “FAIRness Literacy: The Achilles’ Heel of Applying FAIR Principles” has been published in Data Science Journal and co-authored by ERINHA Data-Scientist Romain DAVID.
The paper was developed in the framework of the SHaring Reward and Credit (SHARC) Interest Group of the Research Data Alliance (RDA) which was established to improve research crediting and rewarding mechanisms for scientists who wish to organise their data and material resources for community sharing.
This paper reports on the lessons learned from the RDA SHARC Interest Group on identifying the processes required to prepare FAIR implementation in various communities not specifically data skilled, and on the procedures and training that must be deployed and adapted to each practice and level of understanding.
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 below). This schema displays the “preparing” and “training” phases as conditions of pre-FAIRification. The pre-FAIRification processes must be community-approved at each iteration, while the FAIRification steps “check” and “adjust” implementation must be approved by the community before a new iteration. This approach will be also proposed within ERINHA research infrastructure to increase research data interoperability.