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

合作研究：RUI：HNDS-R：走出平地：复杂网络、拓扑数据分析和科学进步

• 批准号: 2318171
• 负责人: Lori Ziegelmeier
• 金额: $ 14.31万
• 依托单位: Macalester College
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2318171&HistoricalAwards=false
• 关键词: Collaborative; Research; RUI; HNDS; Stepping

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.

科学进步是通过科学家提出的新发现的数量来衡量的。随着科学的发展，研究人员必须掌握以推动科学前进的前进的知识数量急剧增加。因此，科学的突破很难实现。该项目使用创新的数学工具来研究知识的变化规模和范围以及科学的社会组织如何减慢科学发现的步伐。能够确定什么鼓励（相反，可以扼杀）科学进步对于对未来的科学政策，确定资金优先级以及弄清楚如何最好地组织科学工作的良好决定很重要。由于科学进步所需的越来越多的“知识负担”使个人科学家更加困难地进行新发现，因此建立了专门解决复杂科学问题的更大，更多样化的专家研究团队已经变得很普遍。这种“分歧和征服”跨学科的方法非常有效，但是关于知识成长如何减慢发现率以及科学社会组织变化的改善或加剧影响，还有什么要了解。该项目通过应用拓扑数据分析绘制的数学工具来分析大规模文献计量数据和UMETRICS项目的赠款付款数据，从而建立在NSF支持的校正项目的基础上。这些工具允许研究捕获“科学知道的知识”，“它想要知道的”和“科学家目前有承诺知道的概念网络的拓扑结构和动态”。该项目将有助于确定影响新发现率和特征的因素，发现率是否受研究资金的变化或科学工作组织的影响以及这些影响如何在整个询问的时间和领域之间变化。该奖项反映了NSF的法定任务，并通过使用该基金会的知识分子优点和广泛影响来评估NSF的法定任务，并被认为是珍贵的支持。