Quantifying patterns of research-interest evolution
Quantifying patterns of research-interest evolution
复制标题
量化研究兴趣演变的模式
DOI：
10.1038/s41562-017-0078
复制
发表时间：
2017-04-01
影响因子：
29.9
通讯作者：
Szymanski, Boleslaw K.
中科院分区：
文献类型：
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作者：
Jia, Tao;Wang, Dashun;Szymanski, Boleslaw K.
定量地了解科学家如何随着时间的推移选择和转移研究重点非常重要，因为它影响着科学家的培训方式、科学的资助方式、知识的组织和发现方式以及卓越的认可和奖励方式。尽管对影响科学家选择研究主题的各种因素进行了广泛的调查，但对产生表征个体科学家研究兴趣演变的宏观模式的机制的定量评估仍然有限。在这里，我们对发表记录进行了大规模分析，结果表明研究兴趣的变化遵循以指数分布为特征的可重复模式。我们确定了导致观察到的指数分布的三个基本特征，这些特征源于研究兴趣演化中利用和探索之间的微妙相互作用。我们开发了一个基于随机游走的模型，使我们能够准确地重现经验观察结果。这项工作揭示并定量分析了支配研究兴趣变化的宏观模式，从而表明科学研究和个人职业生涯存在高度的规律性。
To understand quantitatively how scientists choose and shift their research focus over time is of high importance, because it affects the ways in which scientists are trained, science is funded, knowledge is organized and discovered, and excellence is recognized and rewarded–. Despite extensive investigation into various factors that influence a scientist’s choice of research topics–, quantitative assessments of mechanisms that give rise to macroscopic patterns characterizing research-interest evolution of individual scientists remain limited. Here we perform a large-scale analysis of publication records, and we show that changes in research interests follow a reproducible pattern characterized by an exponential distribution. We identify three fundamental features responsible for the observed exponential distribution, which arise from a subtle interplay between exploitation and exploration in research-interest evolution,. We developed a random-walk-based model, allowing us to accurately reproduce the empirical observations. This work uncovers and quantitatively analyses macroscopic patterns that govern changes in research interests, thereby showing that there is a high degree of regularity underlying scientific research and individual careers.