Report on FAIR Signposting and its Uptake by the Community

Implementation challenges arrow_forward FAIR metrics & certification


This report by the FAIR Metrics subgroup addresses critical issues surrounding the adoption and impact of FAIRness metrics, essential for enhancing data quality and interoperability within the research landscape. By scrutinising FAIRness evaluations, the report contributes significantly to advancing the understanding and implementation of the FAIR principles, which are pivotal for modern research practices.


This deliverable presents recent progress by the FAIR Metrics subgroup, focusing on outcomes from workshops and hackathons. It highlights the development of the FAIR Signposting design pattern for standardised metadata publication. Targeting researchers, data scientists, repository managers, and tool developers, it aims to enhance data quality and interoperability within the research community.

Main highlights

The report emphasises the development of the FAIR Signposting design pattern, enhancing metadata publication for improved data discovery in the EOSC. It highlights the establishment of reference environments and governance mechanisms to ensure consistency in FAIR assessment tests. Advocating for endorsement, the report aims to promote standardised practices and collaboration, facilitating seamless data interoperability and advancing open science principles across disciplines.

Key recommendations

  1. Endorse FAIR Signposting as a FAIR-enabling design pattern for EOSC resources, ensuring clarity and uniformity in FAIR assessment.
  2. Encourage the use of Signposting alongside other metadata publishing options to improve data discovery.
  3. Discourage deviations from the Signposting standard to maintain consistency and interpretability of metadata.
  4. Advocate for the registration of community standards in standards registries to promote their visibility and facilitate validation by FAIR assessment tools.