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Open data and data sharing in articles about COVID-19 published in preprint servers medRxiv and bioRxiv

Author

Listed:
  • Josip Strcic

    (Catholic University of Croatia)

  • Antonia Civljak

    (Specialist Family Medicine Clinic Dr. Ljiljana Lipovac-Francuz)

  • Terezija Glozinic

    (Catholic University of Croatia)

  • Rafael Leite Pacheco

    (Hospital Sírio-Libanês, Universidade Federal de São Paulo (Unifesp) and Centro Universitário São Camilo (CUSC))

  • Tonci Brkovic

    (University Hospital Split)

  • Livia Puljak

    (Catholic University of Croatia)

Abstract

This study aimed to analyze the content of data availability statements (DAS) and the actual sharing of raw data in preprint articles about COVID-19. The study combined a bibliometric analysis and a cross-sectional survey. We analyzed preprint articles on COVID-19 published on medRxiv and bioRxiv from January 1, 2020 to March 30, 2020. We extracted data sharing statements, tried to locate raw data when authors indicated they were available, and surveyed authors. The authors were surveyed in 2020–2021. We surveyed authors whose articles did not include DAS, who indicated that data are available on request, or their manuscript reported that raw data are available in the manuscript, but raw data were not found. Raw data collected in this study are published on Open Science Framework (https://osf.io/6ztec/). We analyzed 897 preprint articles. There were 699 (78%) articles with Data/Code field present on the website of a preprint server. In 234 (26%) preprints, data/code sharing statement was reported within the manuscript. For 283 preprints that reported that data were accessible, we found raw data/code for 133 (47%) of those 283 preprints (15% of all analyzed preprint articles). Most commonly, authors indicated that data were available on GitHub or another clearly specified web location, on (reasonable) request, in the manuscript or its supplementary files. In conclusion, preprint servers should require authors to provide data sharing statements that will be included both on the website and in the manuscript. Education of researchers about the meaning of data sharing is needed.

Suggested Citation

  • Josip Strcic & Antonia Civljak & Terezija Glozinic & Rafael Leite Pacheco & Tonci Brkovic & Livia Puljak, 2022. "Open data and data sharing in articles about COVID-19 published in preprint servers medRxiv and bioRxiv," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(5), pages 2791-2802, May.
    • Handle: RePEc:spr:scient:v:127:y:2022:i:5:d:10.1007_s11192-022-04346-1
    DOI: 10.1007/s11192-022-04346-1
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    References listed on IDEAS

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    1. Dominique G Roche & Loeske E. B Kruuk, 2015. "Public Data Archiving in Ecology and Evolution: How Well are We Doing?," Working Papers id:7811, eSocialSciences.
    2. Lisa M Federer & Christopher W Belter & Douglas J Joubert & Alicia Livinski & Ya-Ling Lu & Lissa N Snyders & Holly Thompson, 2018. "Data sharing in PLOS ONE: An analysis of Data Availability Statements," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-12, May.
    3. Dominique G Roche & Loeske E B Kruuk & Robert Lanfear & Sandra A Binning, 2015. "Public Data Archiving in Ecology and Evolution: How Well Are We Doing?," PLOS Biology, Public Library of Science, vol. 13(11), pages 1-12, November.
    4. 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.
    5. Rut Lucas-Dominguez & Adolfo Alonso-Arroyo & Antonio Vidal-Infer & Rafael Aleixandre-Benavent, 2021. "The sharing of research data facing the COVID-19 pandemic," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(6), pages 4975-4990, June.
    6. J. Homolak & I. Kodvanj & D. Virag, 2020. "Preliminary analysis of COVID-19 academic information patterns: a call for open science in the times of closed borders," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(3), pages 2687-2701, September.
    7. Liwei Zhang & Liang Ma, 2021. "Does open data boost journal impact: evidence from Chinese economics," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(4), pages 3393-3419, April.
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    Cited by:

    1. Liwei Zhang & Liang Ma, 2023. "Is open science a double-edged sword?: data sharing and the changing citation pattern of Chinese economics articles," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(5), pages 2803-2818, May.
    2. Federica Cugnata & Chiara Brombin & Chiara Maria Poli & Roberto Buccione & Clelia Serio, 2024. "Modelling perception and resilience factors to data sharing in clinical and basic research: an observational study," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(6), pages 3169-3192, June.

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