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Analyzing the vast coronavirus literature with CoronaCentral

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 machin...

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Detalles Bibliográficos
Autores principales: Lever, Jake, Altman, Russ B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: National Academy of Sciences 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8202008/
https://www.ncbi.nlm.nih.gov/pubmed/34016708
http://dx.doi.org/10.1073/pnas.2100766118
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author Lever, Jake
Altman, Russ B.
author_facet Lever, Jake
Altman, Russ B.
author_sort Lever, Jake
collection PubMed
description 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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spelling pubmed-82020082021-06-24 Analyzing the vast coronavirus literature with CoronaCentral Lever, Jake Altman, Russ B. Proc Natl Acad Sci U S A Biological Sciences 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. National Academy of Sciences 2021-06-08 2021-05-20 /pmc/articles/PMC8202008/ /pubmed/34016708 http://dx.doi.org/10.1073/pnas.2100766118 Text en Copyright © 2021 the Author(s). Published by PNAS. https://creativecommons.org/licenses/by/4.0/This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY) (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Biological Sciences
Lever, Jake
Altman, Russ B.
Analyzing the vast coronavirus literature with CoronaCentral
title Analyzing the vast coronavirus literature with CoronaCentral
title_full Analyzing the vast coronavirus literature with CoronaCentral
title_fullStr Analyzing the vast coronavirus literature with CoronaCentral
title_full_unstemmed Analyzing the vast coronavirus literature with CoronaCentral
title_short Analyzing the vast coronavirus literature with CoronaCentral
title_sort analyzing the vast coronavirus literature with coronacentral
topic Biological Sciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8202008/
https://www.ncbi.nlm.nih.gov/pubmed/34016708
http://dx.doi.org/10.1073/pnas.2100766118
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