From Conceptualization to Implementation: FAIR Assessment of Research Data Objects

TLDR

Funders and policy makers strongly recommend adopting the FAIR principles, and several initiatives are working on implementing and standardizing their evaluation. The paper presents practical metrics and tools developed by the FAIRsFAIR project to pilot FAIR assessment of research data objects in trustworthy repositories, including an awareness‑raising self‑assessment tool and an automated assessment tool. The metrics are largely based on indicators from the RDA FAIR Data Maturity Model Working Group, and the tools were designed and evaluated through an iterative process. Initial testing with researchers and repositories shows promising results, and the authors propose future improvements and next steps to expand FAIR data assessment across the research ecosystem.

Abstract

Funders and policy makers have strongly recommended the uptake of the FAIR principles in scientific data management. Several initiatives are working on the implementation of the principles and standardized applications to systematically evaluate data FAIRness. This paper presents practical solutions, namely metrics and tools, developed by the FAIRsFAIR project to pilot the FAIR assessment of research data objects in trustworthy data repositories. The metrics are mainly built on the indicators developed by the RDA FAIR Data Maturity Model Working Group. The tools' design and evaluation followed an iterative process. We present two applications of the metrics: an awareness-raising self-assessment tool and an automated FAIR data assessment tool. Initial results of testing the tools with researchers and data repositories are discussed, and future improvements suggested including the next steps to enable FAIR data assessment in the broader research data ecosystem.