BIP4COVID19: Releasing impact measures for articles relevant to COVID-19

  1. Thanasis Vergoulis
  2. Ilias Kanellos
  3. Serafeim Chatzopoulos
  4. Danae Pla Karidi
  5. Theodore Dalamagas

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Abstract

Since the beginning of the 2019-20 coronavirus pandemic, a large number of relevant articles has been published or become available in preprint servers. These articles, along with earlier related literature, compose a valuable knowledge base affecting contemporary research studies, or even government actions to limit the spread of the disease and treatment decisions taken by physicians. However, the number of such articles is increasing at an intense rate making the exploration of the relevant literature and the identification of useful knowledge in it challenging. In this work, we describe BIP4COVID19, an open dataset compiled to facilitate the coronavirus-related literature exploration, by providing various indicators of scientific impact for the relevant articles. Additionally, we provide a publicly accessible Web interface on top of our data, allowing the exploration of the publications based on the computed indicators.

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  1. Version published to 10.1101/2020.04.11.037093v4 on bioRxiv
  2. Version published to 10.1162/qss_a_00169
  3. Version published to 10.1101/2020.04.11.037093v3 on bioRxiv
  4. ScreenIT

    SciScore for 10.1101/2020.04.11.037093: (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
    CORD-19 offers a full-text corpus of more than 467 000 articles on coronavirus and COVID-19, collected based on articles that contain a set of COVID-19 related keywords from PMC, arXiv, biorXiv, and medRxiv and the further addition of a set of publications on the novel coronavirus, maintained by the WHO.
    arXiv
    suggested: (arXiv, RRID:SCR_006500)
    biorXiv
    suggested: (bioRxiv, RRID:SCR_003933)
    To find those tweets which are related to the articles in our database, we rely on the URLs of the articles in doi.org, PubMed, and PMC.
    PubMed
    suggested: (PubMed, RRID:SCR_004846)

    Results from OddPub: Thank you for sharing your data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    4.3 Limitations: The following limitations should be taken into consideration with respect to the data: while we take effort to include as many articles as possible, there are many cases where our source data do not provide any pmids or pmcids. As a consequence, no data for these articles are collected and they are not included in the BIP4COVID19 dataset. Furthermore, with respect to the calculated impact scores, it should be noted that the citation analysis we conduct is applied on the citation graph formed by citations from and to collected publications only, i.e., our analyses are not based on pubmed’s complete citation graph, but on a COVID-19-related subgraph. Consequently, the relative scores of publications may differ from those calculated on the complete PubMed data. Finally, regarding the tweet-based analysis, since our data come from the COVID-19-TweetIDs dataset which only tracks tweets from a predefined set of accounts and which is based on a particular set of COVD-19-related keywords, the measured number of tweets is only based on a subset of the complete COVID-19-related tweets. 4.4 Usage Notes: Our data are available in files following TSV format, allowing easy import to various database management systems and can be conveniently opened and edited by any text editor, or spreadsheet software. We have been regularly updating the BIP4COVID19 data since March 2020, and we plan to continue providing regular updates, incorporating any additions and changes from our s...

    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.
    • No funding statement was detected.
    • 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.

  5. Version published to 10.1101/2020.04.11.037093v2 on bioRxiv
  6. Version published to 10.1101/2020.04.11.037093v1 on bioRxiv