Collaborative Research: RUI: HNDS-R: Stepping out of flatland: Complex networks, topological data analysis, and the progress of science

科学进步是用科学家新发现的数量来衡量的。随着科学的进步，研究人员为了推动科学前沿而必须掌握的先验知识数量急剧增加。因此，科学突破更难实现。该项目使用创新的数学工具来研究不断变化的知识规模和范围以及科学的社会组织可能如何减缓科学发现的步伐。能够确定什么鼓励（或者相反，什么阻碍）科学进步对于制定关于未来科学政策的正确决策、确定资助优先事项以及弄清楚如何最好地组织科学工作是很重要的。由于科学进步所需要的日益增加的“知识负担”使得单个科学家更难做出新的发现，因此，建立更大、更多样化的专家研究团队来致力于解决复杂的科学问题已经变得很普遍。这种“分而治之”的跨学科方法非常有效，但关于知识增长如何减缓发现速度，以及科学社会组织变化的改善或加剧影响是什么，还有很多东西需要学习。本项目建立在美国国家科学基金会支持的ExHACT项目的基础上，通过应用从拓扑数据分析中得出的数学工具来分析大规模的文献计量数据和来自UMETRICS项目的拨款支付数据。这些工具允许对概念网络的拓扑结构和动态进行检查，以捕获“科学知道什么”、“科学想知道什么”和“科学家目前倾向于知道什么”。该项目将有助于确定影响新发现速度和特征的因素，发现速度是否受到研究经费或科学工作组织变化的影响，以及这些影响如何随时间和研究领域而变化。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Scientific progress is measured by the number of new discoveries that scientists make. As science advances, the amount of prior knowledge that researchers must master to push the frontiers of science forward has increased dramatically. Consequently, scientific breakthroughs are harder to achieve. This project uses innovative mathematical tools to study how the changing scale and scope of knowledge and the social organization of science might be slowing the pace of scientific discovery. Being able to identify what encourages (and, conversely, stifles) scientific advances is important for making good decisions about future science policy, determining funding priorities, and figuring out how scientific work can best be organized. Because the increasing “burden of knowledge” required for scientific progress has made it more difficult for individual scientists to make new discoveries, the creation of larger and more diverse research teams of experts devoted to solving complex scientific problems has become common. This “divide and conquer” interdisciplinary approach has been very effective, but there is still much to learn about how knowledge growth slows discovery rates and what the ameliorating or exacerbating effects of changes in the social organization of science are. This project builds on the NSF-supported ExHACT project by applying mathematical tools drawn from topological data analysis to analyze large scale bibliometric data and data on grant payments from the UMETRICS project. These tools permit the examination of the topological structure and dynamics of concept networks that capture "what science knows," "what it wants to know," and "what scientists currently have the propensity to know." The project will help identify the factors that affect the rate and character of new discoveries, whether the discovery rate is affected by changes in research funding or the organization of scientific work, and how these effects vary across time and fields of inquiry.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.