Does interdisciplinary breadth shape research data sharing and citation impact? Evidence from six research fields

Research data sharing has become an important component of open science, yet sharing benefits vary across research contexts. Existing studies have examined factors influencing data sharing and the citation advantage associated with open data, but less is known about how interdisciplinary breadth relates to observable sharing practices and whether the citation advantage of sharing differs across disciplines. This study investigates the relationships among interdisciplinary breadth, observable sharing practices, and citation impact.

The study uses 566,188 journal articles indexed in OpenAlex from 2020 to 2025. Interdisciplinary breadth was operationalized as the number of Level-0 disciplinary concepts associated with each article. Observable sharing practices were identified through repository links, supplementary-material links, and textual sharing statements. Regression models were applied to examine associations among interdisciplinary breadth, data sharing, and citation impact across six research fields, and BERTopic-based topic modeling was also applied in history and digital humanities to explore topic-level variations.

The results show that interdisciplinary breadth had a predominantly negative but non-linear association with observable sharing, with sharing probability generally declining as breadth increased, although the decline weakened at higher levels. Articles with observable sharing signals were associated with higher citation counts overall, but the magnitude of this association varied substantially across fields. History exhibited the largest positive citation advantage, whereas digital humanities showed a negative but statistically uncertain association. Topic-level analysis further revealed that observable sharing was concentrated in specific research topics with distinct methodological and infrastructural characteristics.

These findings highlight that openness practices and their scholarly returns are influenced by disciplinary contexts. Understanding research data sharing therefore requires consideration of both researchers' willingness to share and the infrastructures and evaluation systems embedded in different research disciplines.