Databiology Lab CORONAHACK: Collection of Public COVID-19 Data

  1. Juan Caballero Pérez
  2. José Manuel Caballero Contreras
  3. Carlos de Blas Pérez
  4. Felipe Leza Alvarez

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Abstract

COVID-19 has had an unprecedented global impact in health and economy affecting millions of persons world-wide. To support and enable a collaborative response from the global research communities, we created a data collection for different public sources for anonymized patient clinical data, imaging datasets, molecular data as nucleotide and protein sequences for the SARS-CoV-2 virus, reports of count of cases and deaths per city/country, and other economic indicators in Databiology Lab ( https://www.lab.databiology.net/ ) where researchers could access these data assets and use the hundreds of available open source bioinformatic applications to analyze them. These data assets are regularly updated and was used in a successful virtual 3-day hackathon organized by Databiology Ltd and Mindstream-AI where hundreds of attendees to work collaboratively to analyze these data collections.

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  1. ScreenIT

    SciScore for 10.1101/2020.10.22.328864: (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
    Phylogenetic tree: To generate a Bayesian phylogenetic tree, we used MrBayes app (App ID: 214, https://www.lab.databiology.net/dbe/userlab/show-application.html?applicationId=214), with the following parameters: lset nst=1, rates=invgamma, mcmc ngen=10000, samplefreq=100, printfreq=100, diagnfreq=100, burnin=10, relburnin=YES, nchains=4, temp=0.5, stoprule=YES.
    MrBayes
    suggested: (MrBayes, RRID:SCR_012067)

    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.
    • 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.

  2. Version published to 10.1101/2020.10.22.328864 on bioRxiv