Accelerating the implementation of FAIR assessment in practice

FAIRsFAIR Adopts the RDA FAIR Data Maturity Model Specification and Guidelines

The overall goal of the FAIRsFAIR project is to develop practical solutions to facilitate the application of the FAIR principles throughout the research data life cycle. One of the objectives of FAIRsFAIR is to pilot the assessment of digital objects (e.g., research data) in FAIR-enabling Trustworthy Digital Repositories (TDRs). To reach this goal the project developed a set of systematic metrics that let us assess FAIR digital objects. A number of groups and communities that are interested in evaluating FAIRness have proposed their own criteria/measures. This leads to a range of sometimes ambivalent or incomplete interpretations, thereby raising the need to define systematic measurements of data FAIRness through an assessment framework that provides consensus based on existing approaches.

The Recommendation ‘FAIR Data Maturity Model (Specification and Guidelines)’ includes the definition of the indicators and their assessment details which are important for implementing FAIR data assessment. FAIRsFAIR has participated and provided feedback on the indicators during two of the online workshops organised by the Working Group, and will disseminate and share the results of pilot testing through project reports. More generally, FAIRsFAIR aims to increase the production and use of FAIR data and will draw upon emerging specifications and guidelines as recommendations, guidance and training for various stakeholders (research communities, data stewards, policymakers) are being developed. Anusuriya Devaraju, Hervé L’Hours, Ilona von Stein, Mustapha Mokrane, Ingrid Dillo, Patricia Herterich, Joy Davidson (FAIRsFAIR)

The RDA outputs adopted:

FAIRsFAIR used the RDA FAIR Data Maturity Model Specification and Guidelines Recommendation (assessment indicators version 3) of the FAIR Data Maturity Model Working Group as a basis to develop a set of minimum metrics for assessing the FAIRness of research data objects and tools implementing those metrics to address two main use cases (researchers and data repositories). To support assessments based on the metrics, FAIRsFAIR is currently implementing a tool set (Fair-Aware – a manual self-assessment tool to be used by researchers, and F-UJI – an automated assessment service for implementation by repositories). The metrics and tools will be iteratively improved through pilot testing with researchers and selected data repositories.

Read and watch the full story

Read the graphically designed full story here.

Accelerating the implementation of FAIR assessment in practice

During the RDA Global Adoption Week 2020, Patricia Herterich (DCC) told the FAIRsFAIR adoption story of FAIR data maturity model specification and guidelines. Find the slides here and watch the recordings:

u003cstrongu003eStakeholder:u003c/strongu003e Researchers & Scientists

u003cstrongu003eRegion:u003c/strongu003e Global

Related Group: FAIR Data Maturity Model WG

RDA Output Adopted: FAIR Data Maturity Model: specification and guidelines