Modeling the FAIR Rubrics Landscape

Marijane White, Lily Winfree, Payal Mehndiratta, Kimberly Robasky, Robin Champieux

Keywords: data sharing, FAIR, rubrics, data modeling

TL;DR: An exploration into whether various available FAIR rubrics fully map to the FAIR principles and requirements.

Abstract:

Researchers and organizations that support science increasingly consider the FAIR Data Principles to be the gold standard for the management and sharing of data and research resources. There are many parallel efforts to identify recommended practices and metrics to improve and measure the FAIRness of data. We sought to delineate the rubrics and frameworks that describe practices and criteria for interpreting and measuring FAIR data as well as the relationships between these rubrics, their criteria, and the original FAIR principles.

This poster describes our work to create a semantic linked data model based on the schema.org vocabulary that describes the characteristics of the FAIR metrics landscape. We integrated the data we collected about the various rubrics with this model to create a small knowledge base that we interrogated with SPARQL queries in order to identify which components of the FAIR principles have been sufficiently addressed with metrics and the nature of those metrics, which components have yet to be sufficiently addressed, where the community agrees and disagrees in their interpretations of FAIRness, and where the community has extended and reinterpreted the FAIR principles.

Our goals are to provide an evidence-based identification of existing metrics that reflect community agreement on the measurability of FAIR components, the components that have been neglected by these metrics, and the characteristics of data and research resources that the community agree are important to reuse but have not been adequately addressed in the original FAIR principles.

ORCID: https://orcid.org/0000-0001-5059-4132

Submission Type: poster