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Disciplinary differences of software use and impact in scientific literature

Author

Listed:
  • Xuelian Pan

    (Nanjing University
    Jiangsu Key Laboratory of Data Engineering and Knowledge Service)

  • Erjia Yan

    (Drexel University)

  • Weina Hua

    (Nanjing University)

Abstract

Software plays an important role in the advancement of science. Software developers, users, and funding agencies have deep interests in the impact of software on science. This study investigates the use and impact of software by examining how software is mentioned and cited among 9548 articles published in PLOS ONE in 12 defined disciplines. Our results demonstrate that software is widely used in scientific research and a substantial uncitedness of software exists across different disciplines. Findings also show that the practice of software citations varies noticeably at the discipline level and software that is free for academic use is more likely to receive citations than commercial software.

Suggested Citation

  • Xuelian Pan & Erjia Yan & Weina Hua, 2016. "Disciplinary differences of software use and impact in scientific literature," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(3), pages 1593-1610, December.
    • Handle: RePEc:spr:scient:v:109:y:2016:i:3:d:10.1007_s11192-016-2138-4
    DOI: 10.1007/s11192-016-2138-4
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    References listed on IDEAS

    as
    1. Pan, Xuelian & Yan, Erjia & Wang, Qianqian & Hua, Weina, 2015. "Assessing the impact of software on science: A bootstrapped learning of software entities in full-text papers," Journal of Informetrics, Elsevier, vol. 9(4), pages 860-871.
    2. James Howison & Julia Bullard, 2016. "Software in the scientific literature: Problems with seeing, finding, and using software mentioned in the biology literature," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(9), pages 2137-2155, September.
    3. Jeffrey A. Roberts & Il-Horn Hann & Sandra A. Slaughter, 2006. "Understanding the Motivations, Participation, and Performance of Open Source Software Developers: A Longitudinal Study of the Apache Projects," Management Science, INFORMS, vol. 52(7), pages 984-999, July.
    4. Heather Piwowar, 2013. "Value all research products," Nature, Nature, vol. 493(7431), pages 159-159, January.
    5. Leonardo Candela & Donatella Castelli & Paolo Manghi & Alice Tani, 2015. "Data journals: A survey," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(9), pages 1747-1762, September.
    6. Piwowar, Heather A. & Chapman, Wendy W., 2010. "Public sharing of research datasets: A pilot study of associations," Journal of Informetrics, Elsevier, vol. 4(2), pages 148-156.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
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    Cited by:

    1. Robert Tomaszewski, 2023. "Visibility, impact, and applications of bibliometric software tools through citation analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(7), pages 4007-4028, July.
    2. 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.
    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. 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.
    5. Yuzhuo Wang & Chengzhi Zhang & Kai Li, 2022. "A review on method entities in the academic literature: extraction, evaluation, and application," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(5), pages 2479-2520, May.
    6. 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.
    7. Pan, Xuelian & Yan, Erjia & Cui, Ming & Hua, Weina, 2018. "Examining the usage, citation, and diffusion patterns of bibliometric mapping software: A comparative study of three tools," Journal of Informetrics, Elsevier, vol. 12(2), pages 481-493.
    8. 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.
    9. Bikun Chen & Dannan Deng & Zhouyan Zhong & Chengzhi Zhang, 2020. "Exploring linguistic characteristics of highly browsed and downloaded academic articles," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(3), pages 1769-1790, March.
    10. Li, Kai & Yan, Erjia, 2018. "Co-mention network of R packages: Scientific impact and clustering structure," Journal of Informetrics, Elsevier, vol. 12(1), pages 87-100.
    11. Pan, Xuelian & Yan, Erjia & Cui, Ming & Hua, Weina, 2019. "How important is software to library and information science research? A content analysis of full-text publications," Journal of Informetrics, Elsevier, vol. 13(1), pages 397-406.

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