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. 2020 Nov 6:5:594060.
doi: 10.3389/frma.2020.594060. eCollection 2020.

Tracking and Mining the COVID-19 Research Literature

Affiliations

Tracking and Mining the COVID-19 Research Literature

Alan L Porter et al. Front Res Metr Anal. .

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.

Keywords: COVID-19; bibliometrics; coronavirus; pandemic; tech mining; text analysis.

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Figures

Figure 1
Figure 1
Growth in COVID-19–related Publications Indexed by PubMed, Monthly, January-June, 2020.
Figure 2
Figure 2
Medical Subject Headings (MeSH term) Distribution Consolidated to 4 Levels.
Figure 3
Figure 3
Screenshot of VantagePoint Software: Exploring Combinations of Who, Where, When, and What?
Figure 4
Figure 4
Topic Evolution Pathways in COVID-19 Research [based on analyses of the 47,607 core PubMed search as of July 1, 2020].
Figure 5
Figure 5
The Extent of Co-authoring among the Top 30 Countries (for COVID-19 papers in 2020). Node size reflects number of papers published, 1949-2020 (to date, July 2); links indicate substantial degree of co-authoring, shown above a threshold.
Figure 6
Figure 6
MeSH Qualifiers Multidisciplinary Network (for 2020 Records). Node size reflects number of papers associated with the given frequent MeSH Qualifer; links indicate substantial degree of co-occurrence, shown above a threshold.

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