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追踪与挖掘新冠疫情研究文献

Tracking and Mining the COVID-19 Research Literature.

作者信息

Porter Alan L, Zhang Yi, Huang Ying, Wu Mengjia

机构信息

Search Technology, Inc., Norcross, GA, United States.

Science, Technology & Innovation Policy, Georgia Tech, Atlanta, GA, United States.

出版信息

Front Res Metr Anal. 2020 Nov 6;5:594060. doi: 10.3389/frma.2020.594060. eCollection 2020.

Abstract

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.

摘要

COVID-19领域前所未有的爆发式增长给研究人员带来了挑战,要跟上该领域的研究知识。本文对这项研究进行了概述,以通过综述和新颖的分类使这些知识更容易获取。我们提供了一些网站,为研究人员提供更深入探究以解决特定问题的途径。我们进一步探究并重新整合COVID-19的主题内容,以解决有关主题演变以及缓解这一疫情和减少未来病毒威胁的战术与战略方法重点的研究问题。数据表明,有必要更加关注战略、免疫学因素。从COVID-19领域之外联系并转移研究知识需要一个可行的COVID-19知识模型。本研究提供了补充性的主题分类,以促进这种建模,为未来基于文献的发现工作提供信息。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/26d6/8025982/9cb26bafcd9d/frma-05-594060-g0001.jpg

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