Research topic displacement and the lack of interdisciplinarity: lessons from the scientific response to COVID-19

Author:

Seidlmayer EvaORCID,Melnychuk Tetyana,Galke Lukas,Kühnel Lisa,Tochtermann Klaus,Schultz Carsten,Förstner Konrad U.

Abstract

AbstractBased on a large-scale computational analysis of scholarly articles, this study investigates the dynamics of interdisciplinary research in the first year of the COVID-19 pandemic. Thereby, the study also analyses the reorientation effects away from other topics that receive less attention due to the high focus on the COVID-19 pandemic. The study aims to examine what can be learned from the (failing) interdisciplinarity of coronavirus research and its displacing effects for managing potential similar crises at the scientific level. To explore our research questions, we run several analyses by using the COVID-19++ dataset, which contains scholarly publications, preprints from the field of life sciences, and their referenced literature including publications from a broad scientific spectrum. Our results show the high impact and topic-wise adoption of research related to the COVID-19 crisis. Based on the similarity analysis of scientific topics, which is grounded on the concept embedding learning in the graph-structured bibliographic data, we measured the degree of interdisciplinarity of COVID-19 research in 2020. Our findings reveal a low degree of research interdisciplinarity. The publications’ reference analysis indicates the major role of clinical medicine, but also the growing importance of psychiatry and social sciences in COVID-19 research. A social network analysis shows that the authors’ high degree of centrality significantly increases her or his degree of interdisciplinarity.

Funder

Bundesministerium für Bildung und Forschung

Deutsche Zentralbibliothek für Medizin (ZBMED)

Publisher

Springer Science and Business Media LLC

Reference80 articles.

1. Aboelela, S., Merrill, J., Carley, K. M., & Larson, E. (2007). Social network analysis to evaluate an interdisciplinary research center. The Journal of Research Administration, 38, 61–75.

2. Ali, I., & Alharbi, O. M. (2020). COVID-19: Disease, management, treatment, and social impact. Science of The Total Environment, 728, 138861. https://doi.org/10.1016/j.scitotenv.2020.138861

3. Allison, P. D. (1999). Multiple Regression: A Primer. Thousand Oak: Calif. Pine Forge Press.

4. Amey, M.J. & Brown, D. (2006). Breaking out of the box: Interdisciplinary collaboration and faculty work.

5. Aviv-Reuven, S., & Rosenfeld, A. (2021). Publication patterns’ changes due to the COVID-19 pandemic: A longitudinal and short-term scientometric analysis. Scientometrics, 126(8), 6761–6784. https://doi.org/10.1007/s11192-021-04059-x

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3