Analyzing the vast coronavirus literature with CoronaCentral
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Abstract
The global SARS-CoV-2 pandemic has caused a surge in research exploring all aspects of the virus and its effects on human health. The overwhelming rate of publications means that human researchers are unable to keep abreast of the research.
To ameliorate this, we present the CoronaCentral resource which uses machine learning to process the research literature on SARS-CoV-2 along with articles on SARS-CoV and MERS-CoV. We break the literature down into useful categories and enable analysis of the contents, pace, and emphasis of research during the crisis. These categories cover therapeutics, forecasting as well as growing areas such as “Long Covid” and studies of inequality and misinformation. Using this data, we compare topics that appear in original research articles compared to commentaries and other article types. Finally, using Altmetric data, we identify the topics that have gained the most media attention.
This resource, available at https://coronacentral.ai , is updated multiple times per day and provides an easy-to-navigate system to find papers in different categories, focussing on different aspects of the virus along with currently trending articles.
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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 Sentences Resources Data Collection: The CORD-19 dataset [9] and PubMed articles containing relevant coronavirus keywords are downloaded daily. PubMedsuggested: (PubMed, RRID:SCR_004846)Multi-label classifiers were implemented using ktrain [10] and HuggingFace models for BERT models and scikit-learn for others [11]. BERTsuggested: (BERT, RRID:SCR_018008)scikit-learnsuggested: (scikit-learn, RRID:SCR_002577)This used the BioText project (https://github.com/jakelever/biotext). BioTextsuggested: NoneOther Analyses: All other analyses were implemented in Python and visualized using R and ggplot2. Pythonsuggested: …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 Sentences Resources Data Collection: The CORD-19 dataset [9] and PubMed articles containing relevant coronavirus keywords are downloaded daily. PubMedsuggested: (PubMed, RRID:SCR_004846)Multi-label classifiers were implemented using ktrain [10] and HuggingFace models for BERT models and scikit-learn for others [11]. BERTsuggested: (BERT, RRID:SCR_018008)scikit-learnsuggested: (scikit-learn, RRID:SCR_002577)This used the BioText project (https://github.com/jakelever/biotext). BioTextsuggested: NoneOther Analyses: All other analyses were implemented in Python and visualized using R and ggplot2. Pythonsuggested: (IPython, RRID:SCR_001658)ggplot2suggested: (ggplot2, RRID:SCR_014601)Data Availability: The data is hosted on Zenodo and available at https://doi.org/10.5281/zenodo.4383289. Zenodosuggested: (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.
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