Identification of research communities of environmental engineering and their evolution using coauthor network analysis
Networks of coauthorship for scientific publication well represent the collaboration dynamics among members of a scientific community. Here, we employed a coauthor network analysis to identify research communities and their evolutionary patterns as the first attempt to characterize the dynamics of c...
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| Published in | Environmental modelling & software : with environment data news Vol. 149; p. 105320 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Oxford
Elsevier Ltd
01.03.2022
Elsevier Science Ltd |
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| Abstract | Networks of coauthorship for scientific publication well represent the collaboration dynamics among members of a scientific community. Here, we employed a coauthor network analysis to identify research communities and their evolutionary patterns as the first attempt to characterize the dynamics of collaboration in environmental engineering. Using bibliometric data from 2007 to 2018 in environmental engineering, 20 research communities were identified and two major drivers of scientific collaboration were revealed: research interest and country of one's affiliation. We developed a novel methodology that enables tracking cluster's evolutionary patterns. This methodology allowed systematic stereotyping of each cluster as “mature” or “emerging”. The two stereotypes did not show a significant difference in the annual growth rate of the volume of publications, implying that comparable attention has been paid to advancing the knowledge of formerly-established subjects and exploring innovative subjects of environmental engineering.
•A total of 68,881 environmental engineering papers published 2007–2018 were collected to construct the coauthor network.•Twenty clusters identified from the coauthor network represent research communities in environmental engineering developed from spontaneous coauthoring.•Major drivers of scientific collaboration in environmental engineering were research interest and country of author's affiliation.•Novel methodology developed herein categorized evolutionary behaviors of research communities into “mature” or “emerging”. |
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| AbstractList | Networks of coauthorship for scientific publication well represent the collaboration dynamics among members of a scientific community. Here, we employed a coauthor network analysis to identify research communities and their evolutionary patterns as the first attempt to characterize the dynamics of collaboration in environmental engineering. Using bibliometric data from 2007 to 2018 in environmental engineering, 20 research communities were identified and two major drivers of scientific collaboration were revealed: research interest and country of one's affiliation. We developed a novel methodology that enables tracking cluster's evolutionary patterns. This methodology allowed systematic stereotyping of each cluster as “mature” or “emerging”. The two stereotypes did not show a significant difference in the annual growth rate of the volume of publications, implying that comparable attention has been paid to advancing the knowledge of formerly-established subjects and exploring innovative subjects of environmental engineering.
•A total of 68,881 environmental engineering papers published 2007–2018 were collected to construct the coauthor network.•Twenty clusters identified from the coauthor network represent research communities in environmental engineering developed from spontaneous coauthoring.•Major drivers of scientific collaboration in environmental engineering were research interest and country of author's affiliation.•Novel methodology developed herein categorized evolutionary behaviors of research communities into “mature” or “emerging”. Networks of coauthorship for scientific publication well represent the collaboration dynamics among members of a scientific community. Here, we employed a coauthor network analysis to identify research communities and their evolutionary patterns as the first attempt to characterize the dynamics of collaboration in environmental engineering. Using bibliometric data from 2007 to 2018 in environmental engineering, 20 research communities were identified and two major drivers of scientific collaboration were revealed: research interest and country of one's affiliation. We developed a novel methodology that enables tracking cluster's evolutionary patterns. This methodology allowed systematic stereotyping of each cluster as “mature” or “emerging”. The two stereotypes did not show a significant difference in the annual growth rate of the volume of publications, implying that comparable attention has been paid to advancing the knowledge of formerly-established subjects and exploring innovative subjects of environmental engineering. |
| ArticleNumber | 105320 |
| Author | Park, Jaebeom Park, Jeryang Choi, Yongju |
| Author_xml | – sequence: 1 givenname: Jaebeom surname: Park fullname: Park, Jaebeom organization: Department of Civil and Environmental Engineering, Seoul National University, Seoul, 08826, Republic of Korea – sequence: 2 givenname: Jeryang orcidid: 0000-0002-9682-5669 surname: Park fullname: Park, Jeryang organization: Department of Civil and Environmental Engineering, Hongik University, Seoul, 04066, Republic of Korea – sequence: 3 givenname: Yongju surname: Choi fullname: Choi, Yongju email: ychoi81@snu.ac.kr organization: Department of Civil and Environmental Engineering, Seoul National University, Seoul, 08826, Republic of Korea |
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| Keywords | Community evolution Bibliometric analysis Research pattern Coauthor network Trend analysis Environmental engineering |
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