FAIR4Health: Findable, Accessible, Interoperable and Reusable data to foster Health Research.

Due to the nature of health data, its sharing and reuse for research are limited by ethical, legal and technical barriers. The FAIR4Health project facilitated and promoted the application of FAIR principles in health research data, derived from the publicly funded health research initiatives to make them Findable, Accessible, Interoperable, and Reusable (FAIR). To confirm the feasibility of the FAIR4Health solution, we performed two pathfinder case studies to carry out federated machine learning algorithms on FAIRified datasets from five health research organizations.

Progress toward a comprehensive teaching approach to the FAIR data principles.

We evaluate recent efforts to further the effective teaching of FAIR data principles by examining existing and developing educational frameworks focused upon FAIR, training initiatives that have informed teaching on FAIR skills' topics, and a number of key sources for discovering FAIR training materials and how much those sources provide descriptive information about the materials. FAIR4S, providing a coherent description of skills and competencies, is analyzed by target audience using the description of actors found in a European Open Science Cloud ecosystem report and by comparison of the coverage and extent of description of educational and training materials available from the list of sources for finding such materials. Our analysis elucidates the importance of linking resources to FAIR-related educational frameworks, providing consistent descriptions of them using a community-based metadata scheme, and developing an instructor community of practice where ideas and methods can be shared on how to teach FAIR data skills.

Ten principles for machine-actionable data management plans.

Data management plans (DMPs) are documents accompanying research proposals and project outputs. DMPs are created as free-form text and describe the data and tools employed in scientific investigations. They are often seen as an administrative exercise and not as an integral part of research practice.