FAIR in action - a flexible framework to guide FAIRification

Abstract

The COVID-19 pandemic has highlighted the need for FAIR (Findable, Accessible, Interoperable, and Reusable) data more than any other scientific challenge to date. We developed a flexible, multi-level, domain-agnostic FAIRification framework, providing practical guidance to improve the FAIRness for both existing and future clinical and molecular datasets. We validated the framework in collaboration with several major public-private partnership projects, demonstrating and delivering improvements across all aspects of FAIR and across a variety of datasets and their contexts. We therefore managed to establish the reproducibility and far-reaching applicability of our approach to FAIRification tasks.

Fig. 1

The three components of the FAIRification Framework: the conceptual FAIRification Process, the FAIRification Template covering all aspects of FAIRification and the FAIRification Workplan as a single tailored implementation guide.

Fig. 2

FAIRification Process composed of four distinct phases. This is a reduced version of the process diagram. The full version, with additional explanatory text, is available in Supplementary Fig. 2.

Fig. 3

Dataset maturity levels for 17 projects before and after passing through the FAIRification Process. Maturity levels are broken down into representation-related, content-related and hosting-related maturity. The assessments were performed using the FAIR Dataset Maturity (FAIR-DSM) model indicators developed by FAIRplus. It is important to note that maturity improvements should not be compared between projects as they are highly dependent on the specific characteristics of each dataset and the chosen FAIRification goals.

Fig. 4

The FAIRification template steps. Each step is colour-coded based on whether its implementation applies to data hosting, representation and format or data content. Each step is broken down into one or more sub-steps. More details can be found in Supplementary Table 2.

Fig. 5

FAIR-DSM levels. The 5 levels of the FAIR-DSM as well as the additional “level 0” baseline for single-use data, with a brief description of the data characteristics and capabilities required for each level.