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Reproducibility of COVID-19 pre-prints

Author

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  • Annie Collins

    (University of Toronto)

  • Rohan Alexander

    (University of Toronto)

Abstract

To examine the reproducibility of COVID-19 research, we create a dataset of pre-prints posted to arXiv, bioRxiv, and medRxiv between 28 January 2020 and 30 June 2021 that are related to COVID-19. We extract the text from these pre-prints and parse them looking for keyword markers signaling the availability of the data and code underpinning the pre-print. For the pre-prints that are in our sample, we are unable to find markers of either open data or open code for 75% of those on arXiv, 67% of those on bioRxiv, and 79% of those on medRxiv.

Suggested Citation

  • Annie Collins & Rohan Alexander, 2022. "Reproducibility of COVID-19 pre-prints," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(8), pages 4655-4673, August.
    • Handle: RePEc:spr:scient:v:127:y:2022:i:8:d:10.1007_s11192-022-04418-2
    DOI: 10.1007/s11192-022-04418-2
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    References listed on IDEAS

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    1. Gary King & Patrick Lam & Margaret E. Roberts, 2017. "Computer‐Assisted Keyword and Document Set Discovery from Unstructured Text," American Journal of Political Science, John Wiley & Sons, vol. 61(4), pages 971-988, October.
    2. Jaime A. Teixeira da Silva & Panagiotis Tsigaris & Mohammadamin Erfanmanesh, 2021. "Publishing volumes in major databases related to Covid-19," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(1), pages 831-842, January.
    3. Heidi Ledford & Richard Van Noorden, 2020. "High-profile coronavirus retractions raise concerns about data oversight," Nature, Nature, vol. 582(7811), pages 160-160, June.
    4. Diana Kwon, 2020. "How swamped preprint servers are blocking bad coronavirus research," Nature, Nature, vol. 581(7807), pages 130-131, May.
    5. Guillaume Cabanac & Theodora Oikonomidi & Isabelle Boutron, 2021. "Day-to-day discovery of preprint–publication links," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(6), pages 5285-5304, June.
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