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Tracking and Mining the COVID-19 Research Literature
The unprecedented, explosive growth of the COVID-19 domain presents challenges to researchers to keep up with research knowledge within the domain. This article profiles this research to help make that knowledge more accessible via overviews and novel categorizations. We provide websites offering me...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8025982/ https://www.ncbi.nlm.nih.gov/pubmed/33870056 http://dx.doi.org/10.3389/frma.2020.594060 |
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author | Porter, Alan L. Zhang, Yi Huang, Ying Wu, Mengjia |
author_facet | Porter, Alan L. Zhang, Yi Huang, Ying Wu, Mengjia |
author_sort | Porter, Alan L. |
collection | PubMed |
description | The unprecedented, explosive growth of the COVID-19 domain presents challenges to researchers to keep up with research knowledge within the domain. This article profiles this research to help make that knowledge more accessible via overviews and novel categorizations. We provide websites offering means for researchers to probe more deeply to address specific questions. We further probe and reassemble COVID-19 topical content to address research issues concerning topical evolution and emphases on tactical vs. strategic approaches to mitigate this pandemic and reduce future viral threats. Data suggest that heightened attention to strategic, immunological factors is warranted. Connecting with and transferring in research knowledge from outside the COVID-19 domain demand a viable COVID-19 knowledge model. This study provides complementary topical categorizations to facilitate such modeling to inform future Literature-Based Discovery endeavors. |
format | Online Article Text |
id | pubmed-8025982 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-80259822021-04-15 Tracking and Mining the COVID-19 Research Literature Porter, Alan L. Zhang, Yi Huang, Ying Wu, Mengjia Front Res Metr Anal Research Metrics and Analytics The unprecedented, explosive growth of the COVID-19 domain presents challenges to researchers to keep up with research knowledge within the domain. This article profiles this research to help make that knowledge more accessible via overviews and novel categorizations. We provide websites offering means for researchers to probe more deeply to address specific questions. We further probe and reassemble COVID-19 topical content to address research issues concerning topical evolution and emphases on tactical vs. strategic approaches to mitigate this pandemic and reduce future viral threats. Data suggest that heightened attention to strategic, immunological factors is warranted. Connecting with and transferring in research knowledge from outside the COVID-19 domain demand a viable COVID-19 knowledge model. This study provides complementary topical categorizations to facilitate such modeling to inform future Literature-Based Discovery endeavors. Frontiers Media S.A. 2020-11-06 /pmc/articles/PMC8025982/ /pubmed/33870056 http://dx.doi.org/10.3389/frma.2020.594060 Text en Copyright © 2020 Porter, Zhang, Huang and Wu. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Research Metrics and Analytics Porter, Alan L. Zhang, Yi Huang, Ying Wu, Mengjia Tracking and Mining the COVID-19 Research Literature |
title | Tracking and Mining the COVID-19 Research Literature |
title_full | Tracking and Mining the COVID-19 Research Literature |
title_fullStr | Tracking and Mining the COVID-19 Research Literature |
title_full_unstemmed | Tracking and Mining the COVID-19 Research Literature |
title_short | Tracking and Mining the COVID-19 Research Literature |
title_sort | tracking and mining the covid-19 research literature |
topic | Research Metrics and Analytics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8025982/ https://www.ncbi.nlm.nih.gov/pubmed/33870056 http://dx.doi.org/10.3389/frma.2020.594060 |
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