D2.4 FAIR Cookbook - Public Version

FAIRplus seeks to establish ‘FAIRification’ processes that can be used at scale to ensure FAIRness of IMI data. The FAIR Cookbook aims to collate protocols for making data FAIR: Findable, Accessible, Interoperable, Reusable. This includes how to FAIRify a number of exemplar datasets, putting the FAIR principles in practice; basic knowledge of what makes data more or less FAIR; understand levels and indicators of FAIRness, and the corresponding maturity model; how to apply the technologies and tools available to assess and improve FAIRness; learn about user roles and their different perspectives on data, and on the challenges when making data FAIR enough for a specific purpose. Continued efforts by consortium partners, organised in “squads” have significantly improved the breadth and depth of the FAIR cookbook coverage.