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Using the Alluvial diagram to display variable characteristics for COVID-19 patients and research achievements on the topic of COVID-19, epidemiology, pathogenesis, and vaccine (CEPV): Bibliometric analysis
An Alluvial diagram illustrates the flow of values from one set to another. Edges (or links/connections) are the connections between nodes (or actors/ vertices). There has been an increase in the use of Alluvial deposits in medical research in recent years. However, there was no illustration of such...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Lippincott Williams & Wilkins
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10289785/ https://www.ncbi.nlm.nih.gov/pubmed/37352056 http://dx.doi.org/10.1097/MD.0000000000033873 |
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author | Yen, Po-Tsung Chien, Tsair-Wei Chou, Willy Tsai, Kang-Ting |
author_facet | Yen, Po-Tsung Chien, Tsair-Wei Chou, Willy Tsai, Kang-Ting |
author_sort | Yen, Po-Tsung |
collection | PubMed |
description | An Alluvial diagram illustrates the flow of values from one set to another. Edges (or links/connections) are the connections between nodes (or actors/ vertices). There has been an increase in the use of Alluvial deposits in medical research in recent years. However, there was no illustration of such research on the way to draw the Alluvial for the readers. Our objective was to demonstrate how to draw the Alluvial in Microsoft Excel by using 2 examples, including variable characteristics for COVID-19 patients and research achievements (RAs) on the topic of COVID-19, epidemiology, pathogenesis, and vaccine (CEPV), and provide an easy and friendly method of drawing the Alluvial in MS Excel. METHODS: Blood samples were collected and analyzed from 485 infected individuals in Wuhan, China. An operational decision tree and 2 Alluvial diagrams were shown to be capable of identifying variable characteristics in COVID-19 patients. A second example is the 100 top-cited articles downloaded from the Web of Science core collection (WoSCC) on the CEPV topic. On the Alluvial diagram, the mean citations (=citations/publications) and x-index were used to identify the top 5 members with the highest RAs in each entity (country, institute, journal, and research area). Two examples (i.e., blood samples taken from 485 infected individuals in Wuhan, China, and 100 top-cited articles on the CEPV topic) were illustrated and compared with traditional visualizations without flow relationships between nodes. RESULTS: The top members in entities with the x-index are U Arab Emirates (242), Jama-J. Am. Med. Assoc. (27.18), Lancet (58.34), San Francisco Va Med (178), and Chaolin Huang (189) in countries, institutes, departments, and authors, respectively. The most cited article with 1315 citations was written by Huang and his colleagues and published by Lancet in 2021. CONCLUSION: This study generates several Alluvial diagrams as demonstrations. The tutorial material and MP4 video provided in the Excel module allow readers to draw the Alluvial on their own in an easy and friendly manner. |
format | Online Article Text |
id | pubmed-10289785 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Lippincott Williams & Wilkins |
record_format | MEDLINE/PubMed |
spelling | pubmed-102897852023-06-24 Using the Alluvial diagram to display variable characteristics for COVID-19 patients and research achievements on the topic of COVID-19, epidemiology, pathogenesis, and vaccine (CEPV): Bibliometric analysis Yen, Po-Tsung Chien, Tsair-Wei Chou, Willy Tsai, Kang-Ting Medicine (Baltimore) 4400 An Alluvial diagram illustrates the flow of values from one set to another. Edges (or links/connections) are the connections between nodes (or actors/ vertices). There has been an increase in the use of Alluvial deposits in medical research in recent years. However, there was no illustration of such research on the way to draw the Alluvial for the readers. Our objective was to demonstrate how to draw the Alluvial in Microsoft Excel by using 2 examples, including variable characteristics for COVID-19 patients and research achievements (RAs) on the topic of COVID-19, epidemiology, pathogenesis, and vaccine (CEPV), and provide an easy and friendly method of drawing the Alluvial in MS Excel. METHODS: Blood samples were collected and analyzed from 485 infected individuals in Wuhan, China. An operational decision tree and 2 Alluvial diagrams were shown to be capable of identifying variable characteristics in COVID-19 patients. A second example is the 100 top-cited articles downloaded from the Web of Science core collection (WoSCC) on the CEPV topic. On the Alluvial diagram, the mean citations (=citations/publications) and x-index were used to identify the top 5 members with the highest RAs in each entity (country, institute, journal, and research area). Two examples (i.e., blood samples taken from 485 infected individuals in Wuhan, China, and 100 top-cited articles on the CEPV topic) were illustrated and compared with traditional visualizations without flow relationships between nodes. RESULTS: The top members in entities with the x-index are U Arab Emirates (242), Jama-J. Am. Med. Assoc. (27.18), Lancet (58.34), San Francisco Va Med (178), and Chaolin Huang (189) in countries, institutes, departments, and authors, respectively. The most cited article with 1315 citations was written by Huang and his colleagues and published by Lancet in 2021. CONCLUSION: This study generates several Alluvial diagrams as demonstrations. The tutorial material and MP4 video provided in the Excel module allow readers to draw the Alluvial on their own in an easy and friendly manner. Lippincott Williams & Wilkins 2023-06-23 /pmc/articles/PMC10289785/ /pubmed/37352056 http://dx.doi.org/10.1097/MD.0000000000033873 Text en Copyright © 2023 the Author(s). Published by Wolters Kluwer Health, Inc. https://creativecommons.org/licenses/by-nc/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC) (https://creativecommons.org/licenses/by-nc/4.0/) , where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal. |
spellingShingle | 4400 Yen, Po-Tsung Chien, Tsair-Wei Chou, Willy Tsai, Kang-Ting Using the Alluvial diagram to display variable characteristics for COVID-19 patients and research achievements on the topic of COVID-19, epidemiology, pathogenesis, and vaccine (CEPV): Bibliometric analysis |
title | Using the Alluvial diagram to display variable characteristics for COVID-19 patients and research achievements on the topic of COVID-19, epidemiology, pathogenesis, and vaccine (CEPV): Bibliometric analysis |
title_full | Using the Alluvial diagram to display variable characteristics for COVID-19 patients and research achievements on the topic of COVID-19, epidemiology, pathogenesis, and vaccine (CEPV): Bibliometric analysis |
title_fullStr | Using the Alluvial diagram to display variable characteristics for COVID-19 patients and research achievements on the topic of COVID-19, epidemiology, pathogenesis, and vaccine (CEPV): Bibliometric analysis |
title_full_unstemmed | Using the Alluvial diagram to display variable characteristics for COVID-19 patients and research achievements on the topic of COVID-19, epidemiology, pathogenesis, and vaccine (CEPV): Bibliometric analysis |
title_short | Using the Alluvial diagram to display variable characteristics for COVID-19 patients and research achievements on the topic of COVID-19, epidemiology, pathogenesis, and vaccine (CEPV): Bibliometric analysis |
title_sort | using the alluvial diagram to display variable characteristics for covid-19 patients and research achievements on the topic of covid-19, epidemiology, pathogenesis, and vaccine (cepv): bibliometric analysis |
topic | 4400 |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10289785/ https://www.ncbi.nlm.nih.gov/pubmed/37352056 http://dx.doi.org/10.1097/MD.0000000000033873 |
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