Demystifying COVID-19 publications: institutions, journals, concepts, and topics.
Objective: We analyzed the COVID-19 Open Research Dataset (CORD-19) to understand leading research institutions, collaborations among institutions, major publication venues, key research concepts, and topics covered by pandemic- related research. Methods: We conducted a descriptive analysis of autho...
| Publicado en: | Journal of the Medical Library Association Vol. 109; no. 3; pp. 395 - 406 |
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| Autores principales: | , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
University of Pittsburgh, University Library System
Jul2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=152939183&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 152939183 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15365050 PI8 jtl: Journal of the Medical Library Association issn: 15365050 maglogo: N pubinfo: dt: Jul2021 vid: 109 iid: 3 pid: 60406 pub: University of Pittsburgh, University Library System place: Pittsburgh, Pennsylvania artinfo: ui: 152939183 152939183 152939183 10.5195/jmla.2021.1141 152939183 ppf: 395 ppct: 11 formats: fmt: @attributes: type: P tig: atl: Demystifying COVID-19 publications: institutions, journals, concepts, and topics. aug: au: Haihua Chen Jiangping Chen Huyen Nguyen affil: PhD Candidate, Department of Information Science, University of North Texas, Denton, TX sug: subj: COVID-19 Pandemic Psychosocial Factors Publishing Serial Publications Collaboration Human Descriptive Research Abstracts Research, Medical Social Distancing Health Services Mortality Risk Factors Data Analytics Minimum Data Set Algorithms Content Analysis Data Analysis Software Descriptive Statistics ab: Objective: We analyzed the COVID-19 Open Research Dataset (CORD-19) to understand leading research institutions, collaborations among institutions, major publication venues, key research concepts, and topics covered by pandemic- related research. Methods: We conducted a descriptive analysis of authors' institutions and relationships, automatic content extraction of key words and phrases from titles and abstracts, and topic modeling and evolution. Data visualization techniques were applied to present the results of the analysis. Results: We found that leading research institutions on COVID-19 included the Chinese Academy of Sciences, the US National Institutes of Health, and the University of California. Research studies mostly involved collaboration among different institutions at national and international levels. In addition to bioRxiv, major publication venues included journals such as The BMJ, PLOS One, Journal of Virology, and The Lancet. Key research concepts included the coronavirus, acute respiratory impairments, health care, and social distancing. The ten most popular topics were identified through topic modeling and included human metapneumovirus and livestock, clinical outcomes of severe patients, and risk factors for higher mortality rate. Conclusion: Data analytics is a powerful approach for quickly processing and understanding large-scale datasets like CORD-19. This approach could help medical librarians, researchers, and the public understand important characteristics of COVID-19 research and could be applied to the analysis of other large datasets. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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