The compliance to FAIR principles of shared data in addiction research

The aim of this study is to assess the scientific data sharing in the field of addictions by applying FAIR principles. These principles play an important role, as they guarantee a minimum of findability, accessibility, interoperability and reusability of the shared data. They are one of the main measures to improve the integrity and quality of research data. For this study, three automated tools were used: the Data Citation Index (DCI) to capture datasets on addictions; Bibliometricos, proprietary software for data retrieval; and the F-UJI tool for the FAIR evaluation of datasets. The datasets on the most common addiction topics, such as alcohol, cannabis, tobacco, cocaine, opioids and stimulants, were downloaded by the DCI (5967 DOIs) and parsed into a database for subsequent analysis. In terms of datasets characteristics, alcohol, tobacco and opioids were the most productive. After assessment by F-UJI, none of the addictions analyzed reached an average of 30% FAIR compliance since all of them were between 20% and 29%. When analyzing each principle, Findable was the best scored principle (in a range of 40%–59%), followed by Accessible, Interoperable and Reusable. The results of our study show, first, an increasing number of shared datasets over the years, especially from basic studies. In terms of quality, there are issues that remain to be resolved, especially in relation to interoperability and reusability principles. This emphasizes the important role of adequate data sharing procedures in ensuring that datasets are FAIR compliant and usable in addiction research.