Revisão por Pares de Data Papers e Dados de Pesquisa

Abstract

The objective was to investigate how traditional journals and data journals handle the peer review of data papers and associated datasets, identifying review typologies and evaluation criteria. The methodology involved a bibliographic and documentary study: 634 data papers published by authors affiliated with Brazilian institutions were retrieved from the Scopus and Web of Science databases, supplemented by metadata extracted from OpenAlex. Subsequently, the editorial policies of 38 journals, classified as either mixed or data journals, were analyzed. The results show that 28 journals are mixed and 10 are data journals. All claim to conduct peer review, yet only nine specify whether this process covers data papers and/or datasets. Most of the analyzed journals adopt conventional review models, with limited application of open review. Only seven journals, all of which are data journals, detail evaluation criteria, highlighting metadata, methodology, and data contextualization. Peer review for data papers is considered to be in its early stages, with little standardization. The absence of explicit policies compromises transparency and reliability, underscoring the need for clearer, more open practices to ensure data reusability and credibility.