Helping the Consumers and Producers of Standards,Repositories and Policies to Enable FAIR Data

Thousands of community-developed(meta)data guidelines,models,ontologies,schemas and formats have been created and implemented by several thousand data repositories and knowledge-bases,across all disciplines.These resources are necessary to meet government,funder and publisher expectations of greater transparency and access to and preservation of data related to research publications.This obligates researchers to ensure their data is FAIR,share their data using the appropriate standards,store their data in sustainable and community-adopted repositories,and to conform to funder and publisher data policies.FAIR data sharing also plays a key role in enabling researchers to evaluate,re-analyse and reproduce each other’s work.We can map the landscape of relationships between community-adopted standards and repositories,and the journal publisher and funder data policies that recommend their use.In this paper,we show how the work of the GO-FAIR FAIR Standards,Repositories and Policies(StRePo)Implementation Network serves as a central integration and cross-fertilisation point for the reuse of FAIR standards,repositories and data policies in general.Pivotal to this effort,the FAIRsharing,an endorsed flagship resource of the Research Data Alliance that maps the landscape of relationships between community-adopted standards and repositories,and the journal publisher and funder data policies that recommend their use.Lastly,we highlight a number of activities around FAIR tools,services and educational efforts to raise awareness and encourage participation.

FAIR Convergence Matrix:Optimizing the Reuse of Existing FAIR-Related Resources

The FAIR principles articulate the behaviors expected from digital artifacts that are Findable,Accessible,Interoperable and Reusable by machines and by people.Although by now widely accepted,the FAIR Principles by design do not explicitly consider actual implementation choices enabling FAIR behaviors.As different communities have their own,often well-established implementation preferences and priorities for data reuse,coordinating a broadly accepted,widely used FAIR implementation approach remains a global challenge.In an effort to accelerate broad community convergence on FAIR implementation options,the GO FAIR community has launched the development of the FAIR Convergence Matrix.The Matrix is a platform that compiles for any community of practice,an inventory of their self-declared FAIR implementation choices and challenges.The Convergence Matrix is itself a FAIR resource,openly available,and encourages voluntary participation by any self-identified community of practice(not only the GO FAIR Implementation Networks).Based on patterns of use and reuse of existing resources,the Convergence Matrix supports the transparent derivation of strategies that optimally coordinate convergence on standards and technologies in the emerging Internet of FAIR Data and Services.