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: 1633791
• 负责人: Aaron Clauset
• 金额: $ 39.25万
• 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: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1633791&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 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.

科学的进步来自于数千名学科内和跨学科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，这些模型涵盖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构成和跨领域的科学生产力产生新的见解。此外，该项目还对新研究生和本科生进行尖端计算和统计研究技术的培训，并将开发和传播有关美国科学劳动力构成的新的大规模开放数据集，并提供用于收集结构化数据的新软件。自动从开放的非结构化来源获取数据。该项目使用来自网络科学、机器学习和社会建模的最先进的计算和统计技术，创建一个新技术平台，用于自动、系统地收集有关成分的高质量结构化数据科学队伍的、动态和产出。 这些数据将与个别研究人员的社会调查结果相结合，并采用严格的网络方法来模拟劳动力构成、生产力以及科学领域内和之间的个人和机构层面的可观察差异之间的关系。开发了劳动力短期和长期演变的数学模型，以评估某些类型的干预措施和政策的可能结果。

项目成果

The misleading narrative of the canonical faculty productivity trajectory

对规范教师生产力轨迹的误导性叙述

• DOI: 10.1073/pnas.1702121114
• 发表时间: 2016-12-25
• 期刊: Proceedings of the National Academy of Sciences
• 作者: Samuel F. Way;Allison C. Morgan;A. Clauset;D. Larremore
• 通讯作者: D. Larremore

Prestige drives epistemic inequality in the diffusion of scientific ideas

声望导致科学思想传播过程中的认知不平等

• DOI: 10.1140/epjds/s13688-018-0166-4
• 发表时间: 2018-05-25
• 期刊: EPJ Data Science
• 影响因子: 3.6
• 作者: Allison C. Morgan;Dimitrios J. Economou;Samuel F. Way;A. Clauset
• 通讯作者: A. Clauset

Productivity, prominence, and the effects of academic environment

生产力、知名度和学术环境的影响

• DOI: 10.1073/pnas.1817431116
• 发表时间: 2019-04-29
• 期刊: Proceedings of the National Academy of Sciences
• 作者: Samuel F. Way;Allison C. Morgan;D. Larremore;A. Clauset
• 通讯作者: A. Clauset

Data-driven predictions in the science of science

科学中数据驱动的预测

• DOI: 10.1126/science.aal4217
• 发表时间: 2017-02-03
• 期刊: Science
• 影响因子: 56.9
• 作者: A. Clauset;D. Larremore;R. Sinatra
• 通讯作者: R. Sinatra

Automatically assembling a full census of an academic field

自动收集学术领域的完整普查

• DOI: 10.1371/journal.pone.0202223
• 发表时间: 2018
• 期刊: PLOS ONE
• 影响因子: 3.7
• 作者: Morgan AC;Way SF;Clauset A
• 通讯作者: Clauset A