EAGER-Profiles: Using researcher profiles to demonstrate the impact of investments in science

EAGER-Profiles：使用研究人员资料来展示科学投资的影响

• 批准号: 1238469
• 负责人: Griffin Weber
• 金额: $ 29.94万
• 依托单位: Harvard University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-07-01 至 2014-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1238469&HistoricalAwards=false
• 关键词: EAGER; Profiles; Using; researcher; profiles

Evaluating the return on investment in science involves accurately associating research inputs (e.g., grants and contracts) with research outputs (e.g., publications and patents). Because many of today's significant discoveries are produced by large multi-institution, cross-disciplinary teams -- often supported by different sponsors, with potential impact spread across different fields -- accurately linking outputs to inputs can be challenging. This research investigates whether and to what extent an online profiling system that aggregates research data around the individual researcher facilitates the processes of linking research inputs to outputs and provides benefits to scientists, institutions, publishers, and agencies. It compares the value of using different data sources, such as federal systems, institutional repositories, commercial databases, manual data entry by librarians and administrators, and data entry by the scientists themselves, to populate a prototype website that profiles computer scientists from multiple institutions. Intellectual Merit: The research breaks new ground in a variety of ways. It determines the cost and effort required to obtain each data source, including the resources needed to disambiguate names in order to link data on scientific contributions to the correct people responsible for them; it calculates the potential reduction in administrative burden each data source provides by measuring the amount of time it takes for a scientist, without the help of a researcher profiling system, to manually locate the data and enter it into an online form; and it evaluates which data sources contain the most information about high-impact cross-institutional or multi-disciplinary research, using data mining techniques to generate collaboration and topic-cluster maps.This study is appropriate for the EAGER program because it is both high-risk -- in that the outcome depends on the coordination of multiple software products, institutions, agencies and data sources in a rapid timeframe -- and high-reward, in that a successful prototype would accelerate the implementation of a national researcher profiling system that benefits multiple stakeholders.The field of Computer Science exemplifies all the challenges and potential rewards of a nation-wide researcher profiling system. Computer scientists are funded by many different agencies (NSF, NIH, DOE, DOD, NASA, etc.); their research outputs take many forms (publications, conference presentations, software, databases, algorithms, patents, etc.); and they collaborate across many disciplines (such as medicine, economics, engineering, physics, and social science). Broader impacts: A publicly-accessible national researcher-profiling system based on linked open data promises numerous benefits beyond enabling more accurate measures of return on federal investment in science. These benefits include the potential to streamline the grant application and reporting process for researchers, identify reviewers without conflicts of interest, help researchers find collaborators, match trainees and junior investigators with mentors and jobs, and enable scientists to showcase their work.

评估科学投资回报涉及将研究投入（例如拨款和合同）与研究产出（例如出版物和专利）准确地联系起来。由于当今许多重大发现都是由大型多机构、跨学科团队产生的——通常得到不同赞助商的支持，潜在影响分布在不同领域——准确地将产出与投入联系起来可能具有挑战性。这项研究调查了一个在线分析系统是否以及在多大程度上聚合了个体研究人员的研究数据，促进了将研究输入与输出联系起来的过程，并为科学家、机构、出版商和代理机构带来了好处。它比较了使用不同数据源（例如联邦系统、机构存储库、商业数据库、图书馆员和管理员的手动数据输入以及科学家自己的数据输入）来填充描述来自多个机构的计算机科学家的原型网站的价值。智力优势：这项研究在多个方面开辟了新天地。 它确定了获取每个数据源所需的成本和工作量，包括消除名称歧义所需的资源，以便将科学贡献的数据与正确的负责人联系起来；它通过测量科学家在没有研究人员分析系统帮助的情况下手动查找数据并将其输入在线表格所需的时间来计算每个数据源提供的管理负担的潜在减少；它评估哪些数据源包含关于高影响力的跨机构或多学科研究的最多信息，使用数据挖掘技术生成协作和主题集群图。这项研究适合 EAGER 计划，因为它既具有高风险（因为结果取决于多个软件产品、机构、机构和数据源在快速时间范围内的协调），又具有高回报（因为成功的原型） 将加速实施国家研究人员分析系统，使多个利益相关者受益。计算机科学领域体现了全国研究人员分析系统的所有挑战和潜在回报。计算机科学家受到许多不同机构的资助（NSF、NIH、DOE、DOD、NASA 等）；他们的研究成果有多种形式（出版物、会议演示、软件、数据库、算法、专利等）；他们跨多个学科（如医学、经济学、工程学、物理学和社会科学）进行合作。更广泛的影响：基于链接的开放数据的可公开访问的国家研究人员分析系统除了能够更准确地衡量联邦科学投资的回报之外，还带来了许多好处。这些好处包括有可能简化研究人员的资助申请和报告流程，识别没有利益冲突的审稿人，帮助研究人员找到合作者，将受训者和初级研究人员与导师和工作相匹配，并使科学家能够展示他们的工作。