Publication practices during the COVID-19 pandemic: Biomedical preprints and peer-reviewed literature

  1. Yulia V. Sevryugina
  2. Andrew J. Dicks

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Abstract

The coronavirus pandemic introduced many changes to our society, and deeply affected the established in biomedical sciences publication practices. In this article, we present a comprehensive study of the changes in scholarly publication landscape for biomedical sciences during the COVID-19 pandemic, with special emphasis on preprints posted on bioRxiv and medRxiv servers. We observe the emergence of a new category of preprint authors working in the fields of immunology, microbiology , infectious diseases , and epidemiology , who extensively used preprint platforms during the pandemic for sharing their immediate findings. The majority of these findings were works-in-progress unfitting for a prompt acceptance by refereed journals. The COVID-19 preprints that became peer-reviewed journal articles were often submitted to journals concurrently with the posting on a preprint server, and the entire publication cycle, from preprint to the online journal article, took on average 63 days. This included an expedited peer-review process of 43 days and journal’s production stage of 15 days, however there was a wide variation in publication delays between journals. Only one third of COVID-19 preprints posted during the first nine months of the pandemic appeared as peer-reviewed journal articles. These journal articles display high Altmetric Attention Scores further emphasizing a significance of COVID-19 research during 2020. This article will be relevant to editors, publishers, open science enthusiasts, and anyone interested in changes that the 2020 crisis transpired to publication practices and a culture of preprints in life sciences.

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  1. ScreenIT

    SciScore for 10.1101/2021.01.21.427563: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Similar enrichment of dates on the first of the year and each month was observed in Crossref [38].
    Crossref
    suggested: (CrossRef, RRID:SCR_003217)
    Terminology: Preprint is defined according to the COPE (Committee of Publication Ethics) definition [
    COPE
    suggested: (COPE, RRID:SCR_009153)
    Metadata for each individual COVID-19 preprint deposited to bioRxiv or medRxiv was gathered by accessing the bioRxiv database of COVID-19 SARS-CoV-2 preprints from medRxiv and bioRxiv, to which we will further refer as BioRxiv API [40].
    bioRxiv
    suggested: (bioRxiv, RRID:SCR_003933)
    Data analysis and visualization was done in Python (pandas, numpy, requests, matplotlib, bokeh, and seaborn) using Jupyter Notebook.
    Python
    suggested: (IPython, RRID:SCR_001658)
    matplotlib
    suggested: (MatPlotLib, RRID:SCR_008624)
    For E-Utilities, data were downloaded via CSV and converted to Microsoft Excel for further analysis and visualization.
    Microsoft Excel
    suggested: (Microsoft Excel, RRID:SCR_016137)
    Statistical analysis: Descriptive analysis of the data, Student’s t-test, and a one-way ANOVA were conducted on the Statistical Package for Social Sciences version 27 (SPSS).
    SPSS
    suggested: (SPSS, RRID:SCR_002865)
    By Sept 26, PubMed indexed 1,048 preprints from medRxiv, bioRxiv, ChemRxiv, arXiv, Research Square, and SSRN, of which 1,043 were on COVID-19, and this constituted only 11.5% of 9,072 medRxiv and bioRxiv COVID-19 related preprints from the BioRxiv API.
    PubMed
    suggested: (PubMed, RRID:SCR_004846)

    Results from OddPub: Thank you for sharing your data.

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • No conflict of interest statement was detected. If there are no conflicts, we encourage authors to explicit state so.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2021.01.21.427563v1 on bioRxiv