Making experimental data tables in the life sciences more FAIR: a pragmatic approach

TL;DR

This paper proposes a pragmatic approach to help life sciences researchers make experimental data tables more FAIR, providing practical tools and a model to improve data management and dissemination.

Contribution

It introduces a practical model and tools to facilitate FAIR data principles application to experimental data tables in life sciences.

Findings

A model for FAIRification of experimental data tables

Tools to assist researchers in improving data management

Enhanced data dissemination aligned with FAIR principles

Abstract

Making data compliant with the FAIR Data principles (Findable, Accessible, Interoperable, Reusable) is still a challenge for many researchers, who are not sure which criteria should be met first and how. Illustrated from experimental data tables associated with a Design of Experiments, we propose an approach that can serve as a model for a research data management that allows researchers to disseminate their data by satisfying the main FAIR criteria without insurmountable efforts. More importantly, this approach aims to facilitate the FAIRification process by providing researchers with tools to improve their data management practices.