Collaborative Research: Academic hiring networks and scientific productivity across disciplines

协作研究：跨学科的学术招聘网络和科学生产力

• 批准号: 1803530
• 负责人: Daniel Larremore
• 金额: $ 12.33万
• 依托单位: University of Colorado at Boulder
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-10-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1803530&HistoricalAwards=false
• 关键词: Collaborative; Research; Academic; hiring; networks

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 on 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.

科学的进步来自于数千名在学科内部和跨学科工作的研究人员的集体和联系的努力。这个项目创建了严格的模型的组成，动态和网络结构的美国？科学工作者来自不同的独立机构，具有不同的优势和重点。 这些机构和个人的特点对跨学科的科学进步的系统影响进行了研究。该项目的结果将产生对科学劳动力组成和跨领域科学生产力的新见解。此外，该项目还培训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人口构成的新的大规模开放数据集。该项目利用网络科学、机器学习和社会建模领域最先进的计算和统计技术，创建一个新的技术平台，用于自动系统地收集有关科学工作者组成、动态和产出的高质量结构化数据。 这些数据将与个人研究人员的社会调查结果和严格的网络方法相结合，以模拟劳动力组成，生产力和科学领域内和科学领域之间的个人和机构层面的可观察差异之间的关系。为了评估某些类型的干预措施和政策的可能结果，劳动力的短期和长期演变的数学模型的开发。

项目成果

Webweb: a Tool for Creating, Displaying, and Sharing Interactive Network Visualizations on the Web

• DOI: 10.21105/joss.01458
• 发表时间: 2019-08
• 期刊: J. Open Source Softw.
• 作者: K. Wapman;D. Larremore

Productivity, prominence, and the effects of academic environment

• DOI: 10.1073/pnas.1817431116
• 发表时间: 2019-05-28
• 期刊: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• 影响因子: 11.1
• 作者: Way, Samuel F.;Morgan, Allison C.;Clauset, Aaron
• 通讯作者: Clauset, Aaron