Extensive studies have documented the detrimental effects of COVID-19 on scientists in various aspects that might dampen scientists’ capacities to innovate. However, some believe that crises could be drivers of innovation due to the urgency for addressing the unprecedented challenges and the need for fast solutions to new problems.

How did scientific novelty evolve during COVID-19?

The evolution of scientific novelty during COVID-19 might be accompanied by changes in its influential factors, especially collaboration-related factors, due to the dominance of teams in the production of knowledge. Scientists could expand the scope of resources (e.g., knowledge, data, and expertise) that they could access for producing novel ideas through two channels: that is, first-time collaboration and international collaboration.

First-time collaboration indicates collaboration between two authors who have never collaborated with each other in the past so that scientists established collaboration outside their existing collaborative networks. First-time collaboration increases team freshness, facilitates scientists with wide reach, and helps acquire more complementary academic resources.

International collaboration allows access to skills, knowledge, and other resources used for research across national borders. International collaboration influences scientific novelty in two opposite directions. On the one hand, the reach of an international network expands the “search space” of teams and thus leads to access to more novel ideas, which facilitates scientific novelty. Furthermore, variety and cross-cultural differences caused by international collaboration could contribute to greater creativity and high impacts. However, international collaboration can also impede novelty due to higher transaction costs, communication barriers, and audience effect.

Although researchers pointed out various barriers which impede international collaboration and first-time collaboration, we expect that these two types of collaboration might increase during the pandemic due to resource constraints and the urgent need for novel solutions to the disease. To investigate the possible mechanisms of changes in scientific novelty from the perspective of resource searching, we raise a question:

How did first-time collaboration and international collaboration evolve during COVID-19?

The aforementioned discussion suggests a potential association between first-time collaboration/international collaboration and scientific novelty, while whether their relationships were disrupted during COVID-19 remains unclear. Thus, we propose the third question as follows.

Is the relationship between first-time collaboration or international collaboration and scientific novelty during COVID-19 different from that in the normal period?

We focus on the coronavirus-related domain as scientists in this field were most affected by COVID-19, which allows us to capture the immediate impact of COVID-19. We follow the long-standing tradition of combinatorial novelty and measure novelty based on unusual combinations of preceding knowledge components. Bio-entities, such as genes, diseases, and proteins, constitute the basic units of knowledge in the biomedical domain, and thus we use bio-entities to represent knowledge elements in coronavirus-related papers. We apply a cutting-edge word embedding technique, namely BioBERT.

To address the research questions, based on 98,981 coronavirus papers, we treat the outbreak of COVID-19 as a natural experiment and use a difference in differences (DID) approach to explore how scientific novelty, first-time collaboration, and international collaboration evolved from January 2018 to December 2020.

Our results show that in the initial period following a coronavirus outbreak, scientific novelty dramatically increased, which suggests scientists’ efforts to try novel re-combinations of existing knowledge to combat this global pandemic. The fraction of first-time collaboration (that is, collaboration between team members without prior collaboration) in scientific teams engaged in coronavirus research grew, and the proportion of internationally collaborative papers sharply decreased. In the pre-COVID-19 period, first-time collaboration is significantly negatively associated with a paper’s novelty score, while this relationship turns significantly positively related to a paper’s novelty during the pandemic. We find that there is insignificant difference in novelty scores between internationally collaborative papers and their counterparts during the pandemic.

With rapidly developing globalization and the increasing complexity of economic, societal, political, and environmental issues, the traditional perception of normal science, with the assumption that the research system operates with institutional stability, is no longer sufficient to address issues or problems in the scientific community. Local and even global research systems could be immediately influenced by exogenous and unexpected events. This study provides evidence on how science progresses differently during a pandemic from a normal science period.

The original article on which this essay is based is: Liu, M., Bu, Y., Chen, C., Xu, J., Li, D., Leng, Y., … & Ding, Y. (2021). Pandemics are catalysts of scientific novelty: Evidence from COVID-19. Journal of the Association for Information Science and Technology, 1– 14. https://doi.org/10.1002/asi.24612

Meijun Liu is an assistant professor at Institute for Global Public Policy, Fudan University. Before joining Fudan University, she was a faculty associate at Department of Economics, Harvard University and a research fellow at the Center for Science of Science and Innovation, Northwestern Institute on Complex Systems, and the Kellogg School of Management, Northwestern University. She studies science and innovation, especially from the perspective of team science and S&T policy. Her recent research focuses on team assembly and its effect on innovation and team performance. Her research builds on big data analytics and advanced statistical and data mining methods. She currently serves as an associate editor for an international journal, Quantity and Quality. She is a winner of the Shanghai Pujiang Talent program award. She holds a Ph.D. degree in information science from the University of Hong Kong. She has been leading projects supported by the National Natural Science Foundation and Shanghai Scientific and Technological Committee.

Yi Bu is an Assistant Professor in Data Science at the Department of Information Management, Peking University, China. Before joining Peking University, he was a research fellow at the Center for Science of Science and Innovation, Northwestern Institute on Complex Systems, and the Kellogg School of Management, Northwestern University, Illinois. Yi aims to understand the social dimensions of the global scientific ecosystem by leveraging massive datasets, computational techniques, and social theories. Yi is particularly focusing on scholarly data mining—specifically, his research endeavors to elucidate the process of knowledge diffusion (e.g., differences between knowledge diffusion of interdisciplinary and unidisciplinary publications), the analysis of scholarly networks and their variants (e.g., co-citation, bibliographic coupling, and some hybrid networks), and bibliometric indicators for research assessment (e.g., citation-based impact indicators). He has been leading projects supported by the National Natural Science Foundation, the Ministry of Education of China, and a variety of enterprises. He is serving as an editorial board member of the Journal of Information Science. He has an undergraduate degree in information management and system from Peking University, an M.Sc. in data science, and a Ph.D. in informatics from Indiana University.

Ying Ding is Bill & Lewis Suit Professor at School of Information, University of Texas at Austin. She has been involved in various NIH, NSF and European-Union funded projects. She has published 240+ papers in journals, conferences, and workshops, and served as the program committee member for 200+ international conferences. She is the co-editor of book series called Semantic Web Synthesis by Morgan & Claypool publisher, the co-editor-in-chief for Data Intelligence published by MIT Press and Chinese Academy of Sciences, and serves as the editorial board member for several top journals in Information Science and Semantic Web. She is the co-founder of Data2Discovery company advancing cutting edge AI technologies in drug discovery and healthcare. Her current research interests include data-driven science of science, AI in healthcare, Semantic Web, knowledge graph, data science, scholarly communication, and the application of Web technologies.