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

more »

... ols 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.