Measuring R&D knowledge diffusion through large databases

测量 R

• 批准号: 1735756
• 负责人: Christina Freyman
• 金额: $ 29.83万
• 依托单位: SRI International
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-07-01 至 2019-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1735756&HistoricalAwards=false
• 关键词: Measuring; R& knowledge; diffusion; through

This research increases understanding of the impact of federal research funding on economic outputs by developing a new data source that enables new analyses of science, engineering, and innovation. The project will improve the measurement of R&D knowledge diffusion by collecting and linking three types of public data: federal government grants and contracts, U.S. patents, and licensing agreements. This project assists the federal government in communicating the impact of public R&D investments to policy makers, stakeholders and citizens. By understanding the factors that lead to commercial licensing of federal government research, the federal government can invest its funds more strategically and increase the productivity of R&D investment, accelerate innovation and increase the United States? economic competitiveness. In addition, this project directly addresses the goal to create a federal government that is responsive and accountable to its citizens. The project builds a data platform on an extensible, common data schema shared by all data types, including patents, publications, and awards. Collecting and combining these data will create a unique, novel, big data set. The government interest section of a U.S. patent will be used to link patents and federal government investments, which can be found in grant databases and the federal procurement data system. Patent licensing is opaque and not generally available; however, reports of technology licensing agreements are filed with the U.S. Securities and Exchange Commission, providing a rich public dataset on technology development. This project will use machine learning to extract these mentions, building a licensing agreement database. Specifically, this database will then be combined with the limited information available in the USPTO's licensing database, and linked to types of federal investment. This approach represents a new way to measure R&D impact by tracing knowledge flows through multiple sources and points in time. The project will demonstrate the viability of collecting licensing data on a large scale and linking the data back to the supporting federal government investments. The resulting data set will be made publicly available.

这项研究通过开发一种新的数据源，使科学，工程和创新的新分析，增加了对联邦研究资金对经济产出的影响的理解。该项目将通过收集和连接三种类型的公共数据来改进对研发知识扩散的衡量：联邦政府赠款和合同、美国专利和许可协议。 该项目协助联邦政府向决策者、利益相关者和公民宣传公共研发投资的影响。通过了解导致联邦政府研究商业许可的因素，联邦政府可以更有战略性地投资其资金，并提高研发投资的生产率，加速创新，增加美国？经济竞争力。 此外，该项目直接涉及建立一个对公民负责的联邦政府的目标。该项目在所有数据类型（包括专利、出版物和奖项）共享的可扩展公共数据模式上构建数据平台。收集和组合这些数据将创建一个独特的，新颖的大数据集。美国专利的政府利益部分将用于将专利和联邦政府投资联系起来，这些投资可以在赠款数据库和联邦采购数据系统中找到。专利许可是不透明的，通常无法获得;然而，技术许可协议的报告提交给美国证券交易委员会，提供了丰富的技术开发公共数据集。该项目将使用机器学习来提取这些提及，构建许可协议数据库。具体来说，该数据库将与USPTO许可数据库中可用的有限信息相结合，并与联邦投资类型相关联。这种方法是一种新的方法来衡量研发的影响，通过跟踪知识流动通过多个来源和时间点。该项目将证明大规模收集许可数据并将数据与支持性的联邦政府投资联系起来的可行性。由此产生的数据集将向公众提供。