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Most notable 100 articles of COVID-19: an Altmetric study based on bibliometric analysis

OBJECTIVE: The purpose of this study is to guide researchers in the COVID-19 pandemic by evaluating the 100 most cited articles of COVID-19 in terms of bibliometric analysis, Altmetric scores, and dimension badges. METHODS: “COVID-19” was entered as the search term in Thomson Reuter’s Web of Science...

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Detalles Bibliográficos
Autores principales: Borku Uysal, Betul, Islamoglu, Mehmet Sami, Koc, Suna, Karadag, Mehmet, Dokur, Mehmet
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7811952/
https://www.ncbi.nlm.nih.gov/pubmed/33459942
http://dx.doi.org/10.1007/s11845-020-02460-8
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author Borku Uysal, Betul
Islamoglu, Mehmet Sami
Koc, Suna
Karadag, Mehmet
Dokur, Mehmet
author_facet Borku Uysal, Betul
Islamoglu, Mehmet Sami
Koc, Suna
Karadag, Mehmet
Dokur, Mehmet
author_sort Borku Uysal, Betul
collection PubMed
description OBJECTIVE: The purpose of this study is to guide researchers in the COVID-19 pandemic by evaluating the 100 most cited articles of COVID-19 in terms of bibliometric analysis, Altmetric scores, and dimension badges. METHODS: “COVID-19” was entered as the search term in Thomson Reuter’s Web of Science database. The 100 most cited articles (T100) were analyzed bibliometrically. Altmetric attention scores (AASs) and dimension badge scores of the articles were evaluated. RESULTS: T100 articles were published from January to September 2020. The average citation of the top 100 articles on COVID-19 was 320 ± 344.3 (143–2676). The language of all articles was English. The average Altmetric value of T100 is 3246 ± 3795 (85–16,548) and the mean dimension badge value was 670 ± 541.6 (176–4232). Epidemiological features (n = 22) and treatment (n = 21) were at the top of the main topics of T100 articles. CONCLUSION: The more citations an article is made, the more it indicates the contribution of that article to science. However, the number of citations is not always the only indicator of article quality. The existence of methods that measure the impact of the article outside the academia to measure the value of the article arises more in an issue that affects the whole world, such as the COVID-19 pandemic.
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spelling pubmed-78119522021-01-18 Most notable 100 articles of COVID-19: an Altmetric study based on bibliometric analysis Borku Uysal, Betul Islamoglu, Mehmet Sami Koc, Suna Karadag, Mehmet Dokur, Mehmet Ir J Med Sci Original Article OBJECTIVE: The purpose of this study is to guide researchers in the COVID-19 pandemic by evaluating the 100 most cited articles of COVID-19 in terms of bibliometric analysis, Altmetric scores, and dimension badges. METHODS: “COVID-19” was entered as the search term in Thomson Reuter’s Web of Science database. The 100 most cited articles (T100) were analyzed bibliometrically. Altmetric attention scores (AASs) and dimension badge scores of the articles were evaluated. RESULTS: T100 articles were published from January to September 2020. The average citation of the top 100 articles on COVID-19 was 320 ± 344.3 (143–2676). The language of all articles was English. The average Altmetric value of T100 is 3246 ± 3795 (85–16,548) and the mean dimension badge value was 670 ± 541.6 (176–4232). Epidemiological features (n = 22) and treatment (n = 21) were at the top of the main topics of T100 articles. CONCLUSION: The more citations an article is made, the more it indicates the contribution of that article to science. However, the number of citations is not always the only indicator of article quality. The existence of methods that measure the impact of the article outside the academia to measure the value of the article arises more in an issue that affects the whole world, such as the COVID-19 pandemic. Springer International Publishing 2021-01-18 2021 /pmc/articles/PMC7811952/ /pubmed/33459942 http://dx.doi.org/10.1007/s11845-020-02460-8 Text en © Royal Academy of Medicine in Ireland 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Original Article
Borku Uysal, Betul
Islamoglu, Mehmet Sami
Koc, Suna
Karadag, Mehmet
Dokur, Mehmet
Most notable 100 articles of COVID-19: an Altmetric study based on bibliometric analysis
title Most notable 100 articles of COVID-19: an Altmetric study based on bibliometric analysis
title_full Most notable 100 articles of COVID-19: an Altmetric study based on bibliometric analysis
title_fullStr Most notable 100 articles of COVID-19: an Altmetric study based on bibliometric analysis
title_full_unstemmed Most notable 100 articles of COVID-19: an Altmetric study based on bibliometric analysis
title_short Most notable 100 articles of COVID-19: an Altmetric study based on bibliometric analysis
title_sort most notable 100 articles of covid-19: an altmetric study based on bibliometric analysis
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7811952/
https://www.ncbi.nlm.nih.gov/pubmed/33459942
http://dx.doi.org/10.1007/s11845-020-02460-8
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