FAIRsFAIR aims to supply practical solutions for the use of the FAIR data principles throughout the research data life cycle with emphasis on fostering FAIR data culture and the uptake of good practices in making data FAIR.

  • A tested Framework for franchising data science schools with model courses and curricula
  • "Train the trainer" data science schools
  • FAIR Competence Adoption booklet
  • Mapping existing FAIR data training offerings across education institutions

  • Registry for FAIR compliant repositories
  • Technical solutions for interoperability requirements
  • Training, support & guidance

  • Toolsets on certified repositories to researchers
  • Core level certified repositories
  • Badges for end-users
  • Capability maturity model towards FAIR certification
  • A Network of 50+ trusted digital repositories

FAIRsFAIR in action

Improve interoperability of FAIR resources

Increase production and use of FAIR data

Develop a capability maturity model towards FAIR certification

Build a network of Trusted Digital Repositories 

Set up a FAIR competence centre for all communities

Embed FAIR data education in university programmes