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Collaborative Research: The Impact of Research Costs on the Rate and Direction of Scientific Discovery

协作研究：研究成本对科学发现的速度和方向的影响

基本信息

批准号：
1564368
负责人：
Jeffrey Furman
金额：
$ 32.12万
依托单位：
National Bureau of Economic Research Inc
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：
2016-04-01 至 2022-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1564368&HistoricalAwards=false
关键词：
Collaborative Research Impact Costs Rate

项目摘要

Understanding scientific and technical progress requires measures of both the rate and direction of resources directed to innovative efforts and the outputs produced. This project leverages advances in computational power to map the evolution of research fields based on researchers? project portfolios in idea spaces. The research takes advantages of shocks, such as changes in policies and research costs, to use empirical research techniques to understand the factors that affect the directions into which science and technology evolve. This work has important implications for understanding the rate and direction of technological change. Specifically, the project uses techniques based on machine learning and natural language processing that measure the incidence and configuration of keywords in published research to quantify the similarity of groups of such articles to define idea space and to subsequently measure the ways in which idea space evolve in response to shocks in research costs and public policies. The research applies these techniques to three contexts: (a) how changes in the costs of research materials affect research trajectories in motion-sensing technology, (b) how researchers in quantum computing change their project portfolios in response to a controversial approach that differs from an established research paradigm; and (c) how pharmaceutical firm research trajectories change in response to news about rivals' drug discovery projects.

要了解科学和技术进步，就必须衡量用于创新努力的资源的速度和方向以及所产生的产出。该项目利用计算能力的进步，根据研究人员？创意空间中的项目组合。本研究利用政策和研究费用变化等冲击，使用实证研究技术来了解影响科学和技术发展方向的因素。这项工作对理解技术变革的速度和方向具有重要意义。具体而言，该项目使用基于机器学习和自然语言处理的技术，测量已发表研究中关键词的发生率和配置，以量化此类文章组的相似性，从而定义想法空间，并随后测量想法空间在应对研究成本和公共政策冲击时的演变方式。该研究将这些技术应用于三种情况：（a）研究材料成本的变化如何影响运动传感技术的研究轨迹，（B）量子计算研究人员如何改变他们的项目组合，以应对与既定研究范式不同的有争议的方法;以及（c）制药公司的研究轨迹如何改变竞争对手的药物发现项目的新闻。