FAIR evaluation of ten widely used chemical datasets: Lessons learned and recommendations

TL;DR

This study evaluates the FAIRness of ten major chemical datasets using manual and automated methods, revealing gaps in metadata and standardization that hinder data reusability and interoperability.

Contribution

It provides a comprehensive comparison of manual and automated FAIRness assessment approaches applied to chemical datasets, highlighting common issues and offering recommendations.

Findings

Automated tools scored up to 54/100 on FAIRness.

Manual analysis identified FAIR compliance issues not detected automatically.

Metadata and standard formats are lacking in several datasets.

Abstract

This document focuses on databases disseminating data on (hazardous) substances found on the North American and the European (EU) market. The goal is to analyse the FAIRness (Findability, Accessibility, Interoperability and Reusability) of published open data on these substances and to qualitatively evaluate to what extend the selected databases already fulfil the criteria set out in the commission draft regulation on a common data chemicals platform. We implemented two complementary approaches: Manual, and Automatic. The manual approach is based on online questionnaires. These questionnaires provide a structured approach to evaluating FAIRness by guiding users through a series of questions related to the FAIR principles. They are particularly useful for initiating discussions on FAIR implementation within research teams and for identifying areas that require further attention. Automated tools for FAIRness assessment, such as F-UJI and FAIR Checker, are gaining prominence and are continuously under development. Unlike manual tools, automated tools perform a series of tests automatically starting from a dereferenceable URL to the data resource to be evaluated. We analysed ten widely adopted datasets managed in Europe and North America. The highest score from automatic analysis was 54/100. The manual analysis shows that several FAIR metrics were satisfied, but not detectable by automatic tools because there is no metadata, or the format of the information was not a standard one. Thus, it was not interpretable by the tool. We present the details of the analysis and tables summarizing the outcomes, the issues, and the suggestions to address these issues.