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Theory and practice of data citation

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  • Gianmaria Silvello

Abstract

Citations are the cornerstone of knowledge propagation and the primary means of assessing the quality of research, as well as directing investments in science. Science is increasingly becoming “data†intensive,†where large volumes of data are collected and analyzed to discover complex patterns through simulations and experiments, and most scientific reference works have been replaced by online curated data sets. Yet, given a data set, there is no quantitative, consistent, and established way of knowing how it has been used over time, who contributed to its curation, what results have been yielded, or what value it has. The development of a theory and practice of data citation is fundamental for considering data as first†class research objects with the same relevance and centrality of traditional scientific products. Many works in recent years have discussed data citation from different viewpoints: illustrating why data citation is needed, defining the principles and outlining recommendations for data citation systems, and providing computational methods for addressing specific issues of data citation. The current panorama is many†faceted and an overall view that brings together diverse aspects of this topic is still missing. Therefore, this paper aims to describe the lay of the land for data citation, both from the theoretical (the why and what) and the practical (the how) angle.

Suggested Citation

  • Gianmaria Silvello, 2018. "Theory and practice of data citation," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 69(1), pages 6-20, January.
    • Handle: RePEc:bla:jinfst:v:69:y:2018:i:1:p:6-20
    DOI: 10.1002/asi.23917
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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. 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.
    3. Wei‐Min Fan & Wei Jeng & Muh‐Chyun Tang, 2023. "Using data citation to define a knowledge domain: A case study of the Add‐Health dataset," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(1), pages 81-98, January.
    4. Mike Thelwall, 2020. "Data in Brief: Can a mega-journal for data be useful?," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(1), pages 697-709, July.
    5. Chris Schubert & Georg Seyerl & Katharina Sack, 2019. "Dynamic Data Citation Service—Subset Tool for Operational Data Management," Data, MDPI, vol. 4(3), pages 1-12, August.
    6. Wesley Mendes-Da-Silva, 2018. "The Promotion of Transparency and the Impact of Research on Business," RAC - Revista de Administração Contemporânea (Journal of Contemporary Administration), ANPAD - Associação Nacional de Pós-Graduação e Pesquisa em Administração, vol. 22(4), pages 639-649.
    7. 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.
    8. Dosso, Dennis & Silvello, Gianmaria, 2020. "Data credit distribution: A new method to estimate databases impact," Journal of Informetrics, Elsevier, vol. 14(4).
    9. Irina Gerasimov & Binita KC & Armin Mehrabian & James Acker & Michael P. McGuire, 2024. "Comparison of datasets citation coverage in Google Scholar, Web of Science, Scopus, Crossref, and DataCite," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(7), pages 3681-3704, July.
    10. Xinyuan Zhang & Qing Xie & Chaemin Song & Min Song, 2022. "Mining the evolutionary process of knowledge through multiple relationships between keywords," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(4), pages 2023-2053, April.
    11. Pablo Dorta-González & Sara M. González-Betancor & María Isabel Dorta-González, 2021. "To what extent is researchers' data-sharing motivated by formal mechanisms of recognition and credit?," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(3), pages 2209-2225, March.
    12. Indra Budi & Yaniasih Yaniasih, 2023. "Understanding the meanings of citations using sentiment, role, and citation function classifications," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(1), pages 735-759, January.
    13. Xie, Qing & Wang, Jiamin & Kim, Giyeong & Lee, Soobin & Song, Min, 2021. "A sensitivity analysis of factors influential to the popularity of shared data in data repositories," Journal of Informetrics, Elsevier, vol. 15(3).
    14. Mike Thelwall, 2021. "Alternative medicines worth researching? Citation analyses of acupuncture, chiropractic, homeopathy, and osteopathy 1996–2017," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(10), pages 8731-8747, October.
    15. Nushrat Khan & Mike Thelwall & Kayvan Kousha, 2021. "Measuring the impact of biodiversity datasets: data reuse, citations and altmetrics," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(4), pages 3621-3639, April.

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