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Data set mentions and citations: A content analysis of full†text publications

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  • Mengnan Zhao
  • Erjia Yan
  • Kai Li

Abstract

This study provides evidence of data set mentions and citations in multiple disciplines based on a content analysis of 600 publications in PLoS One. We find that data set mentions and citations varied greatly among disciplines in terms of how data sets were collected, referenced, and curated. While a majority of articles provided free access to data, formal ways of data attribution such as DOIs and data citations were used in a limited number of articles. In addition, data reuse took place in less than 30% of the publications that used data, suggesting that researchers are still inclined to create and use their own data sets, rather than reusing previously curated data. This paper provides a comprehensive understanding of how data sets are used in science and helps institutions and publishers make useful data policies.

Suggested Citation

  • Mengnan Zhao & Erjia Yan & Kai Li, 2018. "Data set mentions and citations: A content analysis of full†text publications," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 69(1), pages 32-46, January.
    • Handle: RePEc:bla:jinfst:v:69:y:2018:i:1:p:32-46
    DOI: 10.1002/asi.23919
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    Cited by:

    1. Mike Thelwall & Marcus Munafò & Amalia Mas-Bleda & Emma Stuart & Meiko Makita & Verena Weigert & Chris Keene & Nushrat Khan & Katie Drax & Kayvan Kousha, 2020. "Is useful research data usually shared? An investigation of genome-wide association study summary statistics," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-11, February.
    2. Bettina Suhr & Johanna Dungl & Alexander Stocker, 2020. "Search, reuse and sharing of research data in materials science and engineering—A qualitative interview study," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-26, September.
    3. Wang, Yuzhuo & Zhang, Chengzhi, 2020. "Using the full-text content of academic articles to identify and evaluate algorithm entities in the domain of natural language processing," Journal of Informetrics, Elsevier, vol. 14(4).
    4. Jialiang Lin & Yao Yu & Jiaxin Song & Xiaodong Shi, 2022. "Detecting and analyzing missing citations to published scientific entities," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(5), pages 2395-2412, May.

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