Implementing metrics for automated FAIR digital objects assessment in a disciplinary context for F-UJI tool

Q2 2023
  • Implementation challenges
    • FAIR metrics & certification
  • Result description

    FAIR-IMPACT will extend and adapt the FAIRsFAIR data object assessment metrics and F-UJI tool to be more disciplinary-context aware and to include more discipline-specific tests (social domain). F-UJI and the FAIR-Aware tool will be further developed to improve the user interface, efficiency and to identify and mitigate bias. Discipline-aware metrics and tests will be developed with use case partners, domain data repositories, research infrastructures and e-infrastructures. A reference collection of test datasets is provided for verification and benchmarking of FAIR assessment tools’ results. Pilots will test FAIR assessment tools including additional disciplinary-extended tests.

    Problem addressed

    FAIR-IMPACT aims to realise a FAIR EOSC using proven solutions, tools and methods developed during the FAIRsFAIR and other initiatives. One of the goals of the project is to enable the ‘FAIRification’ of different research objects such as datasets, software and semantic artefacts originating from a large range of scientific disciplines. This includes the provision and extension of FAIR assessment metrics and associated tools and their adoption to the needs and requirements of a variety of research communities. In particular FAIRsFAIR data object assessment metrics as well as the F-UJI tool are intended to become more disciplinary-context aware and to include more discipline-specific tests in cooperation with FAIR-IMPACT use case partners, domain data repositories, research infrastructures and e-infrastructures.

    Problem addressed: This deliverable provides the first set of discipline specific tests and metrics developed in cooperation with FAIR-IMPACT Social Sciences and Humanities (SSH) use case partners. We present an analysis of SSH community FAIR-aligned habits and practices carried out using available literature and whitepapers, data collected using standard interfaces provided by the community, as well as FAIR Implementation Profiles (FIPs) from a number of SSH data repositories. Based on this analysis we identified an appropriate SSH sub-community, the social sciences, for which we defined a set of discipline specific metrics and tests derived from the FAIRsFAIR data assessment metrics which are also presented in this deliverable.

    Who can use the result

    Everyone

    How to use the result

    Robert Huber, Maaike Verburg, Mike Priddy, Hervé L'Hours, Joy Davidson, & Hannah Mihai. (2023). D5.1 Implementing metrics for automated FAIR digital objects assessment in a disciplinary context (V1.0). Zenodo.

    Type of result