Retrospective FAIRification by complementation of metadata and interoperability (RetroFAIR)

Longitudinal observational and intervention studies with rich data from various sources are invaluable for biomedical research. Datasets such as questionnaires, physical and molecular measurements and scans often have been collected over a long period of time without FAIR principles in mind. Retrospective FAIRification is therefore essential for future reuse. While FAIR principles can guide this process, the practical implementation can be challenging. In this project, we will develop step-by-step tutorials, tools and access procedures to help biomedical researchers retrospectively FAIRify these rich datasets. This will facilitate and stimulate responsible and efficient reuse of precious biomedical data that cannot be reobtained.