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Published January 20, 2023 | Version v1
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Tracing data: A survey investigating disciplinary differences in data citation

  • 1. University of Ottawa
  • 2. Université de Montréal
  • 3. ZBW Leibniz Information Center for Economics


Data citations, or citations in reference lists to data, are increasingly seen as an important means to trace data reuse and incentivise data sharing. Although disciplinary differences in data citation practices have been well documented via scientometric approaches, we do not yet know how representative these practices are within disciplines. Now do we yet have insight into researchers’ motivations for citing - or not citing - data in their academic work. Here, we present the results of the largest known survey (n=2,492) to explicitly investigate data citation practices, preferences, and motivations, using a representative sample of academic authors by discipline, as represented in the Web of Science (WoS). We present findings about researchers’ current practices and motivations for reusing and citing data and also examine their preferences for how they would like their own data to be cited. We conclude by discussing disciplinary patterns in two broad clusters, focusing on patterns in the social sciences and humanities, and consider the implications of our results for tracing and rewarding data sharing and reuse.


This preprint has been submitted to the journal Quantitative Science Studies.


Gregory-et-al-2023_Tracing data_Preprint-v1.pdf

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Dataset: 10.5281/zenodo.7555363 (DOI)