Accurate Linking of Grants and Topics in Science

资助金与科学主题的准确联系

• 批准号: 1548907
• 负责人: Kevin Boyack
• 金额: $ 5.55万
• 依托单位: SciTech Strategies, Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2016-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1548907&HistoricalAwards=false
• 关键词: Accurate; Linking; Grants; Topics; Science

Understanding the outcomes associated with R&D funding requires accurate linking of funding with the papers produced by the funded work. This proposal develops a methodology to accurately link grants with topics, an important challenge for the development of a rigorous, quantitative understanding and analysis of science policy. The research will provide methods to accurately and consistently identify coherent research areas and systematically link those topic areas to research funding and research output, such as scientific publications, using text and other features. The ability to accurately link grants and topics in a consistent way would be an important advance in the usefulness of STAR METRICS data. In addition to developing a methodology, this project also establishes the accuracy of existing NIH grant-to-article linkage data, and develop grant-to-article linkage data for non-NIH grants -- data which are not readily available. More accurate grant-article and grant-topic linkages will facilitate other research and be important to the STAR METRICS platform.

要了解与研发资金相关的成果，需要将资金与受资助工作产生的论文准确联系起来。该提案制定了一种方法，将赠款与主题准确地联系起来，这是对科学政策进行严格的定量理解和分析的一个重要挑战。这项研究将提供方法，以准确和一致地确定连贯的研究领域，并利用文本和其他特征将这些专题领域与研究资金和研究成果（如科学出版物）系统地联系起来。以一致的方式准确地将赠款和主题联系起来的能力将是星星METRICS数据有用性的一个重要进步。除了开发一种方法，该项目还建立了现有的NIH补助金与文章联系数据的准确性，并为非NIH赠款开发补助金与文章联系数据-这些数据并不容易获得。更准确的资助论文和资助主题联系将促进其他研究，对星星METRICS平台很重要。