Metadados e os princípios FAIR: um estudo dos repositórios de dados de pesquisa em São Paulo

Abstract

In the context of e-Science, there is a large production of data by researchers. Research data demand a high degree of contextualization and need proper management in repositories, in order to maximize its benefits, such as increased visibility for the researcher and for an institution. But to do so, it is not enough to make them available on the web, it is necessary to pay attention to international good practices that aim to increase the integrity and value of data, as is the case with the FAIR principles. Through an exploratory and descriptive research with a qualiquantitative approach, this study aims to assess the level of compliance of research data deposited in institutional repositories regards to the FAIR principles. The sample consists of repositories of the State of São Paulo mapped in the metasearch engine of the Fundação de Amparo à Pesquisa do Estado de São Paulo. Data were collected with the help of the F-UJI tool, then compiled into Google Sheets for analysis. It was possible to verify that the interoperable and reusable facets were the most difficult to adhere to, with most datasets obtaining a score of 0 or 1 out of a maximum of 4, and a score of 1 or 2 out of a maximum of 10, respectively. The findable facet was the one with the greatest adherence by the datasets, which reached scores such as 6, 4 and 3.5 out of a maximum of 7. The best evaluated datasets were deposited in repositories using Dataverse (Unicamp e UFABC), and all six repositories in the sample used the Dublin Core. Overall, the scores received by the sample datasets were low, with the highest adherence equal to 50% and the lowest 14%. Feedback was given to all six institutions, which could be guided to implement the FAIR principles, aiming for more findable, accessible, interoperable and reusable datasets. From the results, it was possible to suggest a minimum set of 15 descriptive metadata elements for the representation of research data.