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

分类： 计算机科学 >> 计算机科学的集成理论 提交时间： 2022-11-16 合作期刊: 《数据智能（英文）》

Sustkova, Hana Pergl Hettne, Kristina Maria Wittenburg, Peter Jacobsen, Annika Kuhn, Tobias Pergl, Robert Slifka, Jan McQuilton, Peter Magagna, Barbara Sansone, Susanna-Assunta Stocker, Markus Imming, Melanie Lannom, Larry Musen, Mark Schultes, Erik

摘要： 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.

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