School of Systems Science, Beijing Normal University, 100875, Beijing, China.
National Science Library, Chinese Academy of Sciences, 100190, Beijing, China.
Nat Commun. 2019 Jul 31;10(1):3439. doi: 10.1038/s41467-019-11401-8.
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.
尽管人们一直在努力理解不同职业阶段科学家的创造力，但对于推动创新的研究主题转换的潜在动态却知之甚少。在这里，我们分析了个体科学家的发表记录，旨在量化他们的主题转换动态及其影响。我们发现，科学家论文的共同引用网络呈现出明显的社区结构，每个主要社区代表一个研究主题。我们的分析表明，科学家研究主题的数量分布较窄。然而，与早期相比，现在的研究人员在主题之间的转换更为频繁。我们还发现，早期职业生涯中的高转换概率与整体生产力低有关，但后期职业生涯中的整体生产力高。有趣的是，无论在哪个职业阶段，论文的平均引用量都与转换概率呈负相关。我们提出了一个可以解释主要观察到的特征的模型。
