Interoperability in Brazilian scientific repositories: evaluation of FAIR compliance in datasets from federal universities within the Dataverse network

The study focuses specifically on the Interoperable dimension, as it is one of the most complex to implement. Interoperability requires the use of recognized metadata standards, controlled vocabularies or ontologies, and semantic linkages that enable unambiguous integration across systems. Although Dataverse provides a technological infrastructure aligned with FAIR principles—such as DOI assignment, structured metadata, APIs, and export in interoperable formats—the text highlights that infrastructure alone does not guarantee high FAIRness. Performance depends largely on the descriptive and curatorial practices adopted by researchers and institutional managers. Given the scarcity of comparative studies centered on interoperability in Brazilian university repositories, the research asks: what is the level of FAIR adherence—particularly regarding interoperability—of datasets published in Dataverse installations hosted by Brazilian federal universities? Methodologically, the research is empirical, exploratory-descriptive, and quantitative-comparative. It identifies Dataverse installations linked to federal universities, selects publicly available datasets with active DOIs, and applies the automated assessment tool F-UJI, generating structured JSON reports and extracting metrics (overall FAIR score, Interoperable dimension score, and sub-indicators I1, I2, and I3).