在合作产生科学影响的演变过程中解开配对协同作用的谜团

IF 3 2区 计算机科学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS EPJ Data Science Pub Date : 2023-12-19 DOI:10.1140/epjds/s13688-023-00439-w
Gangmin Son, Jinhyuk Yun, Hawoong Jeong
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引用次数: 0

摘要

协同作用或团队化学是一个难以捉摸的概念,它解释了合作如何能够产生超出预期的结果。在这里,我们通过重建 560,689 位科学家和 1,026,196 对科学家的发表史,揭示了成对科学合作中协同作用的存在及其内在机制。我们通过提取合作对科学影响的非加成效应来量化配对协同作用,这种效应不受先前合作经验或运气的影响。我们采用随机块模型的网络推断方法来研究配对协同作用的机制及其与个人属性的联系。仅从观察到的协同类型推断出的块结构,可以预测从未合作过的两位科学家之间未确定的协同类型。这表明,协同作用产生于某些尚未确定的个体特征的适当组合。此外,与配对协同作用最相关的是研究兴趣,尽管其多样性并不会导致所有学科的互补性。我们的研究结果为了解科学界合作成功的动力以及通过科学家之间的牵线搭桥发掘合作的隐藏潜力铺平了道路。
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Untangling pair synergy in the evolution of collaborative scientific impact

Synergy, or team chemistry, is an elusive concept that explains how collaboration is able to yield outcomes beyond expectations. Here, we reveal its presence and underlying mechanisms in pairwise scientific collaboration by reconstructing the publication histories of 560,689 individual scientists and 1,026,196 pairs of scientists. We quantify pair synergy by extracting the non-additive effects of collaboration on scientific impact, which are not confounded by prior collaboration experience or luck. We employ a network inference methodology with the stochastic block model to investigate the mechanism of pair synergy and its connection to individual attributes. The inferred block structure, derived solely from the observed types of synergy, can anticipate an undetermined type of synergy between two scientists who have never collaborated. This suggests that synergy arises from a suitable combination of certain, yet unidentified, individual characteristics. Furthermore, the most relevant to pair synergy is research interest, although its diversity does not lead to complementarity across all disciplines. Our results pave the way for understanding the dynamics of collaborative success in science and unlocking the hidden potential of collaboration by matchmaking between scientists.

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来源期刊
EPJ Data Science
EPJ Data Science MATHEMATICS, INTERDISCIPLINARY APPLICATIONS -
CiteScore
6.10
自引率
5.60%
发文量
53
审稿时长
13 weeks
期刊介绍: EPJ Data Science covers a broad range of research areas and applications and particularly encourages contributions from techno-socio-economic systems, where it comprises those research lines that now regard the digital “tracks” of human beings as first-order objects for scientific investigation. Topics include, but are not limited to, human behavior, social interaction (including animal societies), economic and financial systems, management and business networks, socio-technical infrastructure, health and environmental systems, the science of science, as well as general risk and crisis scenario forecasting up to and including policy advice.
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