Maryam Okhovati, Homa Arshadi,
Volume 35, Issue 1 (1-2021)
Abstract
Background: Coronavirus primarily targets the human respiratory system, COVID-19 (Coronavirus disease 2019) triggered in China in the late 2019. In March 2020, WHO announced the COVID-19 pandemic. This study aims to analyze and visualize the scientific structure of the COVID-19 publications using co-citation and co-authorship.
Methods: This is a scientometric study. Web of Science Core Collection (WoSCC) was searched for all documents regarding COVID-19, MERS-Cov, and SARS-Cov from the beginning to 2020. An Excel spreadsheet was applied to gather and analyze the data and the CiteSpace was used to visualize and analyze the data.
Results: A total of 5159 records were retrieved in WoSCC. The structure of the network indicated that the network mean silhouette was low (0.1444), implying that the network clusters’ identity is not identifiable with high confidence. The network modularity was 0.7309. The cluster analysis of the co-citation network on documents from 2003 to 2020 provided 188 clusters. The largest cluster entitled, “the Middle East respiratory syndrome coronavirus” had 255 nodes. The coauthorship network illustrated that the most prolific countries, USA, China, and Saudi Arabia, have focused on a specific field and have formed separate clusters.
Conclusion: The present study identified the important topics of research in the field of COVID-19 based on co-citation networks as well as the analysis of clusters of countries' collaborations. Despite the similarities in the production behavior in prolific countries, their thematic focus varies so that a country like China plays a role in “Quantitative Detection” cluster, while USA is the leading country in the “Biological Evaluation” cluster.
Nahid Hashemi-Madani, Zahra Emami, Mohammad E. Khamseh,
Volume 35, Issue 1 (1-2021)
Abstract
Background: Social network analysis (SNA) evaluates the connections and behavior of individuals in social groups. The scientific collaboration network is a kind of SNAs. A social network could be defined as a collection of nodes (social existence) and links (connections) associated with the nodes. The aim of this study was to evaluate the scientific outputs and collaboration networks of the countries and authors using indicators of SNA in the field of pituitary disorders between 2000 and 2020.
Methods: This is a practical study performed by applying a scientometric approach and SNA. We retrieved 31257 papers in the field of pituitary disorders between 2000 and 2020. Data were analyzed using scientific software, namely, VOSviewer, UciNet, and Netdarw.
Results: Based on degree centrality, Colao and Pivonello in the world, Shimon and Kadioghlu in the Middle-East (ME), and Khamseh, Ghorbani in Iran achieved the top ranking. Based on the betweenness centrality, Pivonello, Colao, and Chanson in the world, Laws, and Kadioghlu in the Middle-East, and Larijani, Mohseni, and Khamseh in Iran were known as the top authors. According to closeness centrality, Pivonello, Colao, and Chanson in the world, Kadioghlu and Kelestimur in the Middle-East, and Mohseni, Khamseh, and Larijani in Iran were the top authors. The map of the authors’ collaboration in the field of pituitary disorders consists of 92 nodes. A total number of 77313 authors had global collaboration. The global collaboration network was comprised of 129 nodes (country) and 2694 links (country’s collaboration). The Middle-East collaboration network revealed 69 nodes and 1708 links. The collaboration network of the Middle-East countries consists of 13 nodes and 50 links.
Conclusion: Authors with a higher degree, betweenness and closeness centrality have greater efficiency (the number of articles) and effectiveness (the number of received citations). Moreover, the authors and countries that published more scientific products received more citations. In addition, in the Middle-East countries, the interdisciplinary scientific collaboration between the researchers in the fields of endocrinology, neurosurgery, pathology, and radiology has a significant impact on improving scientific outputs.
