A Practical Approach of Actions for FAIRification Workflows

TL;DR

This paper proposes a detailed, practical workflow for implementing FAIR principles in data management, demonstrated through a COVID-19 research pilot, with potential applicability across various scientific domains.

Contribution

It introduces a structured workflow for FAIRification, including evaluation of automation tools, tailored for COVID-19 data but adaptable to other fields.

Findings

Workflow clarifies each step in FAIR data generation

Evaluation of tools for semi-automating data treatment

Workflow applicable beyond COVID-19 research

Abstract

Since their proposal in 2016, the FAIR principles have been largely discussed by different communities and initiatives involved in the development of infrastructures to enhance support for data findability, accessibility, interoperability, and reuse. One of the challenges in implementing these principles lies in defining a well-delimited process with organized and detailed actions. This paper presents a workflow of actions that is being adopted in the VODAN BR pilot for generating FAIR (meta)data for COVID-19 research. It provides the understanding of each step of the process, establishing their contribution. In this work, we also evaluate potential tools to (semi)automatize (meta)data treatment whenever possible. Although defined for a particular use case, it is expected that this workflow can be applied for other epidemical research and in other domains, benefiting the entire scientific community.