From Conceptualization to Implementation: FAIR Assessment of Research Data Objects

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.

Towards FAIR Data for Low Carbon Energy - Current State and Call for Action

“…Naturally, this can be best tested with an algorithmic framework. Indeed, the plethora of FAIRness claims and assessment tools led to state the FAIR principle more precisely on the one hand 11 and the development of automated tools on the other hand 6,16 .…”

“…We select the ARDC DAIR data selfassessment tool 1 for manual assessment and the FAIR indicator maturity test 16 as one of the two available machine-actionable test. The other one is the rapidly evolving F-UJI test 6 . The rationale for our choice of the ARDC FAIR data assessment tool 5/7 is that it aligns with the FAIR principles, has a good balance between technical and non-technical questions (22 questions).…”

An automated solution for measuring the progress toward FAIR research data

“…We have improved the metrics based on the feedback from FAIR stakeholders through various means; e.g., a focus group study, open consultation, and pilot repositories. For more details on the methodology, see Devaraju et al 24 The metrics (v0.4) specified by this paper are detailed in the specification. 36 Hierarchical model FAIR principles are high-level guidelines.…”

“…Figure 2 illustrates the iterative development. During the preliminary study, we explored several FAIR assessment scenarios 24 and reviewed existing FAIR assessment frameworks. One of the outcomes of the study was draft metrics 6 of FAIR data assessment.…”

“…For more details on the methodology, see Devaraju et al. 24 The metrics (v0.4) specified by this paper are detailed in the specification. 36…”