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 Key facts