Is coronavirus-related research becoming more interdisciplinary? A perspective of co-occurrence analysis and diversity measure of scientific articles

https://doi.org/10.1016/j.techfore.2021.121344Get rights and content

Highlights

  • We analyze the degree of interdisciplinarity and patterns of disciplinary co-occurrence in coronavirus-related research.

  • Coronavirus-related research is becoming less interdisciplinary between 1990 and 2019.

  • Immunology and microbiology & medicine is the most stable co-occurrence pattern and has lasted for 31 years.

  • The results are helpful to provide an interdisciplinary picture as complete as possible in coronavirus-related research.

Abstract

The outbreak of coronavirus disease 2019 (COVID-19) has had a significant repercussion on the health, economy, politics and environment, making coronavirus-related issues more complicated and difficult to adequately address by relying on a single field. Interdisciplinary research can provide an effective solution to complex issues in the related field of coronavirus. However, whether coronavirus-related research becomes more interdisciplinary still needs corroboration. In this study, we investigate interdisciplinary status of the coronavirus-related fields via the COVID-19 Open Research Dataset (CORD-19). To this end, we calculate bibliometric indicators of interdisciplinarity and apply a co-occurrence analysis method. The results show that co-occurrence relationships between cited disciplines have evolved dynamically over time. The two types of co-occurrence relationships, Immunology and Microbiology & Medicine and Chemical Engineering & Chemistry, last for a long time in this field during 1990–2020. Moreover, the number of disciplines cited by coronavirus-related research increases, whereas the distribution of disciplines is uneven, and this field tends to focus on several dominant disciplines such as Medicine, Immunology and Microbiology, Biochemistry, Genetics and Molecular Biology. We also measure the disciplinary diversity of COVID-19 related papers published from January to December 2020; the disciplinary variety shows an upward trend, while the degree of disciplinary balance shows a downward trend. Meanwhile, the comprehensive index 2Ds demonstrates that the degree of interdisciplinarity in coronavirus field decreases between 1990 and 2019, but it increases in 2020. The results help to map the interdisciplinarity of coronavirus-related research, gaining insight into the degree and history of interdisciplinary cooperation.

Keywords

Coronavirus related research
Interdisciplinarity
Disciplinary co-occurrence relationship
Diversity measure

Cited by (0)

Yi Zhao is currently a PhD student in the School of Economics and Management, Nanjing University of Science and Technology. He received his master's Degree of Economics from Hohai University, Nanjing, China, in 2019. His-research interests mainly focus on knowledge organization and text mining. He has published four publications including DIM, DI, ASIS&T, ISSI.

Lifan Liu received her bachelor's degree in information management and system program from Nanjing University of Information Science and Technology, Nanjing, China, in 2018. She received her master's degree in information science program from Nanjing University of Science and Technology, Nanjing, China, In 2021. Her research interests include text mining and informetrics. She has published three publications including JOI, ASIS&T.

Chengzhi Zhang is a professor of Department of Information Management, Nanjing University of Science and Technology, China. He received his PhD degree of Information Science from Nanjing University, China. He has published more than 100 publications, including JASIST, Aslib JIM, JOI, OIR, SCIM, ACL, NAACL, etc. His-current research interests include scientific text mining, knowledge entity extraction and evaluation, social media mining. He serves as Editorial Board Member and Managing Guest Editor for 10 international journals (Patterns, OIR, Aslib JIM, SCIM, TEL, IDD, NLE, JDIS, DIM, DI, etc.) and PC members of several international conferences in fields of natural language process and scientometrics. (https://chengzhizhang.github.io/)

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