Reference ontology and database annotation of the COVID-19 Open Research Dataset (CORD-19)

  1. Oliver Giles
  2. Rachael Huntley
  3. Anneli Karlsson
  4. Jane Lomax
  5. James Malone

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

The COVID-19 Open Research Dataset (CORD-19) was released in March 2020 to allow the machine learning and wider research community to develop techniques to answer scientific questions on COVID-19. The dataset consists of a large collection of scientific literature, including over 100,000 full text papers. Annotating training data to normalise variability in biological entities can improve the performance of downstream analysis and interpretation. To facilitate and enhance the use of the CORD-19 data in these applications, in late March 2020 we performed a comprehensive annotation process using named entity recognition tool, TERMite, along with a number of large reference ontologies and vocabularies including domains of genes, proteins, drugs and virus strains. The additional annotation has identified and tagged over 45 million entities within the corpus made up of 62,746 unique biomedical entities. The latest updated version of the annotated data, as well as older versions, is made openly available under GPL-2.0 License for the community to use at: https://github.com/SciBiteLabs/CORD19

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.10.04.325266: (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

    No key resources detected.

    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.

  2. Version published to 10.1101/2020.10.04.325266 on bioRxiv