Community-driven governance of FAIRness assessment: an open issue, an open discussion

Although FAIR Research Data Principles are targeted at and implemented by different communities, research disciplines, stakeholders (data stewards, curators, etc.), there is no conclusive way to determine the level of FAIRness intended or required make artefacts (including, but not limited to, data) Findable, Accessible, Interoperable, Reusable. The cover all types digital objects, metadata, infrastructures. However, they focus their narrative on data features that support reusability. defines principles, standards, therefore do propose a mechanism achieve behaviours describe in an attempt be technology/implementation neutral. A range assessment metrics tools have been designed measure FAIRness. Unfortunately, same objects assessed often exhibit widely outcomes because these independent interpretations FAIR. This results confusion among publishers, funders, users objects. Moreover, absence standard transparent definition what constitutes behaviours, temptation define existing approaches as being FAIR-compliant rather than having expected behaviours. whitepaper identifies three high-level stakeholder categories -FAIR decision policymakers, custodians, practitioners - provides examples outlining specific stakeholders' (hypothetical anticipated) needs. It also examines possible models for governance based peer efforts, standardisation bodies, other ways acknowledge specifications potential benefits. can serve starting point foster open discussion around mechanism(s) could used implement it, trusted, broadly representative, appropriately scoped, sustainable. We invite engagement this conversation Google Group [email protected]