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Informal data citation for data sharing and reuse is more common than formal data citation in biomedical fields

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  • Hyoungjoo Park
  • Sukjin You
  • Dietmar Wolfram

Abstract

Data citation, where products of research such as data sets, software, and tissue cultures are shared and acknowledged, is becoming more common in the era of Open Science. Currently, the practice of formal data citation—where data references are included alongside bibliographic references in the reference section of a publication—is uncommon. We examine the prevalence of data citation, documenting data sharing and reuse, in a sample of full text articles from the biological/biomedical sciences, the fields with the most public data sets available documented by the Data Citation Index (DCI). We develop a method that combines automated text extraction with human assessment for revealing candidate occurrences of data sharing and reuse by using terms that are most likely to indicate their occurrence. The analysis reveals that informal data citation in the main text of articles is far more common than formal data citations in the references of articles. As a result, data sharers do not receive documented credit for their data contributions in a similar way as authors do for their research articles because informal data citations are not recorded in sources such as the DCI. Ongoing challenges for the study of data citation are also outlined.

Suggested Citation

  • Hyoungjoo Park & Sukjin You & Dietmar Wolfram, 2018. "Informal data citation for data sharing and reuse is more common than formal data citation in biomedical fields," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 69(11), pages 1346-1354, November.
  • Handle: RePEc:bla:jinfst:v:69:y:2018:i:11:p:1346-1354
    DOI: 10.1002/asi.24049
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    Cited by:

    1. Zeng, Tong & Wu, Longfeng & Bratt, Sarah & Acuna, Daniel E., 2020. "Assigning credit to scientific datasets using article citation networks," Journal of Informetrics, Elsevier, vol. 14(2).
    2. Yongmei Bai & Jian Du, 2022. "Measuring the impact of health research data in terms of data citations by scientific publications," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(12), pages 6881-6893, December.
    3. Libby Hemphill & Amy Pienta & Sara Lafia & Dharma Akmon & David A. Bleckley, 2022. "How do properties of data, their curation, and their funding relate to reuse?," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(10), pages 1432-1444, October.
    4. Li, Kai & Chen, Pei-Ying & Yan, Erjia, 2019. "Challenges of measuring software impact through citations: An examination of the lme4 R package," Journal of Informetrics, Elsevier, vol. 13(1), pages 449-461.
    5. 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.
    6. Barbara McGillivray & Paola Marongiu & Nilo Pedrazzini & Marton Ribary & Mandy Wigdorowitz & Eleonora Zordan, 2022. "Deep Impact: A Study on the Impact of Data Papers and Datasets in the Humanities and Social Sciences," Publications, MDPI, vol. 10(4), pages 1-40, October.

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