Year: 2019
Type: article
Abstract: Despite persistent efforts in understanding the creativity of scientists over different career stages, little is known about the underlying dynamics of research topic switching that drives innovation. Here, we analyze the publication records of individual scientists, aiming to quantify their topic switching dynamics and its influence. We find that the co-citing network of papers of a scientist exhibits a clear community structure where each major community represents a research topic. Our analysis suggests that scientists have a narrow distribution of number of topics. However, researchers nowadays switch more frequently between topics than those in the early days. We also find that high switching probability in early career is associated with low overall productivity, yet with high overall productivity in latter career. Interestingly, the average citation per paper, however, is in all career stages negatively correlated with the switching probability. We propose a model that can explain the main observed features.
Source: Nature Communications
Authors: An Zeng, Zhesi Shen, Jianlin Zhou, Ying Fan, Zengru Di, Yougui Wang, H. Eugene Stanley, Shlomo Havlin
Language: English
Subfield: Statistics, Probability and Uncertainty
Field: Decision Sciences
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $5,200
Funders: National Science Foundation, National Natural Science Foundation of China, China Postdoctoral Science Foundation, Israel Science Foundation, Defense Threat Reduction Agency, Office of Naval Research, Division of Civil, Mechanical and Manufacturing Innovation, Division of Chemistry, Army Research Office
Awards: G1736866159, G1890829241, G2619044695, G2654352436, Collaborative Research: Unraveling Cerebral Connectivity with Diffusion MRI, Microscopy and Statistical Physics, 自组织视角下的社会集群行为涌现机制和演化规律研究, Collaborative Reseach: Ordering Process in Water, Aqueous Solutions, and Water-Biomolecule Systems, CDI-Type II: Collaborative Research: Dynamical processes in interdependent techno-social networks, G4371387823, G4715269412, G5379740167, G5739864355, G635955982, G6395419459, G6541015203, G702394665, G7520120651
OpenAlex ID: W2905469878
