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As part of the data-driven paradigm and open science movement, the data paperis becoming a popular way for researchers to publish their research data, basedon academic norms that cross knowledge domains. Data journals have also beencreated to host this new academic genre. The growing number of data papers andjournals has made them an important large-scale data source for understandinghow research data is published and reused in our research system. One barrierto this research agenda is a lack of knowledge as to how data journals andtheir publications are indexed in the scholarly databases used for quantitativeanalysis. To address this gap, this study examines how a list of 18 exclusivelydata journals (i.e., journals that primarily accept data papers) are indexed infour popular scholarly databases: the Web of Science, Scopus, Dimensions, andOpenAlex. We investigate how comprehensively these databases cover the selecteddata journals and, in particular, how they present the document typeinformation of data papers. We find that the coverage of data papers, as wellas their document type information, is highly inconsistent across databases,which creates major challenges for future efforts to study them quantitatively.As a result, we argue that efforts should be made by data journals anddatabases to improve the quality of metadata for this emerging genre.

How are exclusively data journals indexed in major scholarly databases? An examination of the Web of Science, Scopus, Dimensions, and OpenAlex