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Computer and information sciences, Health data, FAIRification, FAIR, FAIR-CMMI Wide sharing of knowledge and data drives the progression of science. Shared data allows other researchers to reproduce findings and benchmark quality of experiments. Sharing data so that other researchers can Find, Access and Interoperate i.e. integrate the data with the outcomes of their own experiments - allows Reuse and an opportunity to build the large aggregated cohorts we need to detect rare signals and manage the many confounding factors in translational research.

This project will develop the guidelines and tools needed to make data FAIR. Through worked examples using IMI and EFPIA data and application and extension of existing methods we will improve the level of discovery, accessibility, interoperability and reusability of selected IMI and EFPIA data. In addition, through disseminated guidelines and tailored training for data handlers in academia, SMEs and pharmaceuticals, data management culture will change and be sustained and datasets will be reused by pharmaceutical companies, academia and SMEs. Our FAIR SME & Innovation programme will enable wide data reuse and foster an innovation ecosystem around these data that power future re-use, knowledge generation, and societal benefit. We call this approach FAIRplus.