Visualization of knowledge flow in interpersonal scientific collaboration network endocrinology and metabolism research institute

Abstract

Purpose Research collaborations can help to increase scientific productivity. The purpose of the present study was to draw up the knowledge flow network of the Endocrinology and Metabolism Research Institute (EMRI) affiliated to Tehran University of Medical Sciences. Methods The present study is a descriptive cross-sectional study on the publications of the EMRI. Web of Science Core collection databases were searched for the EMRI publications between 2002 to November 2019. Besides, publications were classified and visualized based on authorships (institutes and country of affiliation), and keywords (cooccurrence and trend). Scientometric methods including VOSviewer and HistCite were used for descriptive statistics and data analysis. Results Total citations to the records were 47,528 and papers were published in 916 journals. The annual growth rate of publications and the citation was 14.2% and 18.9%, respectively. A total of 9466 authors from 136 countries collaborated in the publications. The co-authorship patterns showed that the average co-authorship and collaboration coefficient was 3.3 and 0.19. Conclusion Knowledge flow between EMRI researchers with international collaborations, engagement with leading countries, and interdisciplinary collaborations have an increasing trend. To develop a full picture of co-authorship, using social network analysis indicators are suggested for future studies.

Assessing the impact of history of medicine research: A scientometric and altmetric analysis

“… 9 , 13 The mean Collaboration Index shows the number of contributing authors in History of Medicine research, which is less than the average number of co‐authors of all medical articles indexed in PubMed. 2 , 14 , 15 Less than 10% of the research was conducted by a single author, indicating the interdisciplinary nature of History of Medicine research areas and increased scientific productivity with the scientific division of labor. 1 , 15 , 16 Our results align with previous research that has investigated the connections between coauthorship and other factors, such as academic rank, departmental affiliation, citation count, and experience in the field.…”

A Bibliometric Approach to Scientific Production on Familial Hypophosphatemic Rickets in Scopus (2000-2022)

“…International collaboration networks are crucial in medical research. Research collaboration can help increase scientific productivity (25). The U.S leads in terms of its broad collaboration network, followed by Japan, which is the second most productive country.…”

“…Globally, in correspondence with other research areas (16)(17)(18)20,23), the U.S is the most productive country in terms of familial hypophosphatemic rickets, contributing to more than a quarter of the world's scientific production, and 60% of its articles are International collaboration networks are crucial in medical research. Research collaboration can help increase scientific productivity (25). The U.S leads in terms of its broad collaboration network, followed by Japan, which is the second most productive country.…”

A comparative study of scientific collaborations in medical informatics, health information management, medical librarianship, and information sciences among Iranian research communities: A bibliometric study

“…For the fourth phase, using scientific measurement methods and with the help of analytical techniques, the occurrence of scientific collaborations between researchers with VOSviewer ‡ version 1.6.17 was illustrated 9,10 . During the illustration process, researchers utilized data normalization and clustering algorithms to establish a network of relationships between them.…”

“…For the fourth phase, using scientific measurement methods and with the help of analytical techniques, the occurrence of scientific collaborations between researchers with VOSviewer ‡ version 1.6.17 was illustrated. 9 , 10 During the illustration process, researchers utilized data normalization and clustering algorithms to establish a network of relationships between them. The matrix was then prepared for principal component analysis using VOSviewer software, with varying scales of similarity employed in different studies and data conversions used to normalize the data.…”