Volume 7, Issue 2 (9-2020)
jhbmi 2020, 7(2): 171-180 |
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Khazaneha M, Osareh F, Shafiee K. Visualizing Multiple System Atrophy Studies Based on Collaboration Network and Centrality Indices in Web of Science Database. jhbmi 2020; 7 (2) :171-180
URL: http://jhbmi.ir/article-1-442-en.html
URL: http://jhbmi.ir/article-1-442-en.html
Ph.D. in Information Science and Knowledge Information, Professor, Information Science and knowledge Information Dept., Shahid Chamran University of Ahvaz, Ahvaz, Iran
Abstract: (2303 Views)
Introduction: Social network analysis is an analytical method based on graph theories that identifies relationships between individuals or factors to analyze the social structures resulted from those relationships. The objective of this study was to analyze co-authorship and co-word networks based on scientometric indicators and centrality measures in the studies on multiple atrophy system disease published in Web of Science database from 1988 to 2018.
Methods: In this descriptive-analytical case study, the articles published in Web of Science database from 1988 to 2018 were collected using medical subject headings (MeSH) and various scientometric techniques including co-word analysis, co-authorship analysis, and network mapping.
Results: In this study, 6767 articles on multiple system atrophy disease were retrieved. These articles were written by 39184 authors from 3884 organizations and 80 countries and were collected from 832 journals. In this study, based on co-occurrence, 8 clusters in the subject area of multiple system atrophy disease were identified the most important of which were multiple system atrophy disease, Lewy body, orthostatic, hypotension, progressive paralysis, positron tomography, and genes.
Conclusion: Scientometric studies on this disease show a thematic map that can be effective in policy-making for studies in this field. Moreover, by examining these indicators, the issues in this field were identified and through this, the cases that are less taken into consideration can be detected and investigated as future research topics.
Methods: In this descriptive-analytical case study, the articles published in Web of Science database from 1988 to 2018 were collected using medical subject headings (MeSH) and various scientometric techniques including co-word analysis, co-authorship analysis, and network mapping.
Results: In this study, 6767 articles on multiple system atrophy disease were retrieved. These articles were written by 39184 authors from 3884 organizations and 80 countries and were collected from 832 journals. In this study, based on co-occurrence, 8 clusters in the subject area of multiple system atrophy disease were identified the most important of which were multiple system atrophy disease, Lewy body, orthostatic, hypotension, progressive paralysis, positron tomography, and genes.
Conclusion: Scientometric studies on this disease show a thematic map that can be effective in policy-making for studies in this field. Moreover, by examining these indicators, the issues in this field were identified and through this, the cases that are less taken into consideration can be detected and investigated as future research topics.
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