Collaborative Research: Academic hiring networks and scientific productivity across disciplines

科学的进步来自于成千上万在学科内和跨学科工作的研究人员的集体和相互联系的努力。该项目创建了美国科学劳动力的组成、动态和网络结构的严格模型，这些模型跨越具有不同优势和重点的异构独立机构。研究了这些机构和个人特征对跨学科科学进步的系统影响。该项目的结果将对科学劳动力的组成和跨领域的科学生产力产生新的见解。此外，该项目还将培养新的研究生和本科生掌握尖端的计算和统计研究技术，并将开发和传播关于美国科学劳动力构成的新的大规模开放数据集，并提供从开放的非结构化来源自动收集结构化数据的新软件。该项目利用网络科学、机器学习和社会建模等领域最先进的计算和统计技术，创建了一个新的技术平台，用于自动、系统地收集有关科研人员组成、动态和产出的高质量结构化数据。这些数据将与个别研究人员的社会调查结果相结合，并与严格的网络方法相结合，以模拟科学领域内和科学领域之间个人和机构层面上的劳动力构成、生产率和可观察到的差异之间的关系。为了评估某些类型的干预和政策的可能结果，开发了劳动力短期和长期演变的数学模型。

Advances in science come from the collective and linked efforts of thousands of researchers working within and across disciplines. This project creates rigorous models of the composition, dynamics, and network structure of the United States' scientific workforce across heterogeneous independent institutions with different strengths and emphases. The systematic influence of these institutional and individual characteristics on scientific advances across disciplines is investigated. The results of this project will generate new insights into the composition of the scientific workforce and scientific productivity across fields. In addition, this project trains new graduate and undergraduate students in cutting-edge computational and statistical research techniques, and will develop and disseminate new large-scale open data sets on the composition of the United States' scientific workforce and provide new software for collecting structured data automatically from open unstructured sources.This project uses state-of-the-art computational and statistical techniques from network science, machine learning, and social modeling to create a new technology platform for automatically and systematically collecting high-quality structured data on the composition, dynamics, and output of the scientific workforce. These data will be combined with social survey results of individual researchers and with rigorous network methods to model the relationship between workforce composition, productivity, and observable differences at the individual and institutional levels within and between scientific fields. Mathematical models of the short- and long-term evolution of workforce in order to evaluate the likely outcomes of certain types of interventions and policies are developed.