One of the grand challenges of data-intensive science is to facilitate knowledge discovery by assisting humans and machines in their discovery of, access to, integration and analysis of, task-appropriate scientific data and their associated algorithms and workflows. Findable, accessible, interoperable and re-usable/ re-producible (FAIR) data is an integral part in the process of opening up science and research. By improving the FAIR-ness of research data it will unlock the potential for both scientific research and society to draw from the benefits of this data, and also enable significant contribution to economic growth.
The FAIRsFAIR project addresses, in a 36 months timeplan, the development and concrete realisation of an overall knowledge infrastructure on academic quality data management, procedures, standards, metrics and related matters, based on the FAIR principles.
FAIRsFAIR aims to supply practical solutions for the use of the FAIR data principles throughout the research data life cycle. Emphasis is on fostering FAIR data culture and the uptake of good practices in making data FAIR. FAIRsFAIR will play a key role in the development of global standards for FAIR certification of repositories and the data within them contributing to those policies and practices that will turn the EOSC programme into a functioning infrastructure. In the end, FAIRsFAIR will provide a platform for using and implementing the FAIR principles in the day to day work of European research data providers and repositories. FAIRsFAIR will also deliver essential FAIR dimensions of the Rules of Participation (RoP) and regulatory compliance for participation in the EOSC. The EOSC governance structure will use these FAIR aligned RoPs to establish whether components of the infrastructure function in a FAIR manner.
FAIRsFAIR Key facts