World Science against COVID-19: Gender and Geographical Distribution of Research

  1. Julio González-Álvarez

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Abstract

In just a year and a half, an enormous volume of scientific research has been generated throughout the world to study a virus/disease that turned into a pandemic. All the articles on COVID-19 or SARS-CoV-2 included in the SCI-EXPANDED database (Web of Science), signed by more than a third of a million of authorships, were analyzed. Gender could be identified in 92% of the authorships. Women represent 40% of all authors, a similar proportion as first authors, but just 30% as last/senior authors. The pattern of collaboration shows an interesting finding: when a woman signs as a first or last/senior author, the article byline approximates gender parity

According to the corresponding address, the USA shares 22.8% of all world articles, followed by China (14.4%), Italy (7.8%), the UK (5.8%), India (4.2%), Spain (3.8%), Germany (3.6%), France (2.9%), Turkey (2.5%), and Canada (2.4%).

Despite their short lives, the papers received an average of 11 citations. The high impact of papers from China is striking (25.1 citations; the UK, 12.4 citations; the USA, 11.3 citations), presumably because the disease emerged in China, and the first publications (very cited) came from there.

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    SciScore for 10.1101/2021.09.29.21264261: (What is this?)

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

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Procedure: Each variable of interest (author name and surnames, title of article, year of publication, journal, corresponding address, etc.) was extracted using the BibExcel program2 and merged in a master Excel database to perform the bibliometric analyses.
    BibExcel
    suggested: None
    master Excel
    suggested: None
    Statistical analyses were carried out with the SPSS v.
    SPSS
    suggested: (SPSS, RRID:SCR_002865)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    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:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • 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.

    Results from scite Reference Check: We found no unreliable references.

    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 10.1101/2021.09.29.21264261v1 on medRxiv