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Research software citation in the Data Citation Index: Current practices and implications for research software sharing and reuse

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  • Park, Hyoungjoo
  • Wolfram, Dietmar

Abstract

The aim of this study is to explore the phenomenon of research software citation and, in particular, to draw attention to the increasing importance of this form of citation in scholarly communication. This research sheds light on the current status of formal software citation that is captured by citation databases. Data for the study were gathered from more than 67,000 research software records available in public repositories indexed by Clarivate Analytics’ Data Citation Index (DCI). The metadata characteristics of the indexed records and citation data were then analyzed. Research software was rarely cited in the DCI, suggesting that the documented reuse of research software rarely occurs or is not well documented. Institutional repositories attracted few citations and had low rate of citation. It proved impossible, however, using the available data to isolate specific identifiers that can promote formal software citation. The findings presented here offer insights into research software citation that will be of interest to funding agencies, publishers, researchers, and research organizations.

Suggested Citation

  • Park, Hyoungjoo & Wolfram, Dietmar, 2019. "Research software citation in the Data Citation Index: Current practices and implications for research software sharing and reuse," Journal of Informetrics, Elsevier, vol. 13(2), pages 574-582.
    • Handle: RePEc:eee:infome:v:13:y:2019:i:2:p:574-582
    DOI: 10.1016/j.joi.2019.03.005
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    Citations

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    Cited by:

    1. Yuzhuo Wang & Kai Li, 2024. "How do official software citation formats evolve over time? A longitudinal analysis of R programming language packages," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(7), pages 3997-4019, July.
    2. Caifan Du & Johanna Cohoon & Patrice Lopez & James Howison, 2021. "Softcite dataset: A dataset of software mentions in biomedical and economic research publications," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(7), pages 870-884, July.
    3. Lu Jiang & Xinyu Kang & Shan Huang & Bo Yang, 2022. "A refinement strategy for identification of scientific software from bioinformatics publications," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(6), pages 3293-3316, June.
    4. Avick Kumar Dey & Pijush Kanti Dutta Pramanik & Prasenjit Choudhury & Goutam Bandopadhyay, 2021. "Distinctive author ranking using DEA indexing," Quality & Quantity: International Journal of Methodology, Springer, vol. 55(2), pages 601-620, April.
    5. 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.
    6. Enrique Orduña-Malea & Rodrigo Costas, 2021. "Link-based approach to study scientific software usage: the case of VOSviewer," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(9), pages 8153-8186, September.
    7. Alsudais, Abdulkareem, 2021. "In-code citation practices in open research software libraries," Journal of Informetrics, Elsevier, vol. 15(2).

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