Analyzing the vast coronavirus literature with CoronaCentral

  1. Jake Lever
  2. Russ B. Altman

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Abstract

The SARS-CoV-2 pandemic has caused a surge in research exploring all aspects of the virus and its effects on human health. The overwhelming publication rate means that researchers are unable to keep abreast of the literature. To ameliorate this, we present the CoronaCentral resource that uses machine learning to process the research literature on SARS-CoV-2 together with SARS-CoV and MERS-CoV. We categorize the literature into useful topics and article types and enable analysis of the contents, pace, and emphasis of research during the crisis with integration of Altmetric data. These topics include therapeutics, disease forecasting, as well as growing areas such as “long COVID” and studies of inequality. This resource, available at https://coronacentral.ai , is updated daily.

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  1. Version published to 10.1073/pnas.2100766118
  2. ScreenIT

    SciScore for 10.1101/2020.12.21.423860: (What is this?)

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

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Data Collection: The CORD-19 dataset [9] and PubMed articles containing relevant coronavirus keywords are downloaded daily.
    PubMed
    suggested: (PubMed, RRID:SCR_004846)
    Multi-label classifiers were implemented using ktrain [10] and HuggingFace models for BERT models and scikit-learn for others [11].
    BERT
    suggested: (BERT, RRID:SCR_018008)
    scikit-learn
    suggested: (scikit-learn, RRID:SCR_002577)
    This used the BioText project (https://github.com/jakelever/biotext).
    BioText
    suggested: None
    Other Analyses: All other analyses were implemented in Python and visualized using R and ggplot2.
    Python
    suggested: (IPython, RRID:SCR_001658)
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)
    Data Availability: The data is hosted on Zenodo and available at https://doi.org/10.5281/zenodo.4383289.
    Zenodo
    suggested: (ZENODO, RRID:SCR_004129)

    Results from OddPub: Thank you for sharing your code and 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:
    • 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.

    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.

  3. Version published to 10.1101/2020.12.21.423860v1 on bioRxiv