INSPIRE: Evaluating the Effect of Cyberinfrastructure on Universities' Production Process

INSPIRE：评估网络基础设施对大学生产流程的影响

• 批准号: 1243436
• 负责人: Paul Wilson
• 金额: $ 60万
• 依托单位: Clemson 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: 2012-08-15 至 2016-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1243436&HistoricalAwards=false
• 关键词: INSPIRE; Evaluating; Effect; Cyberinfrastructure; Universities

This INSPIRE award is partially funded by the Science of Science and Innovation Policy Program in the Division of SBE Multidisciplinary Activities in the Directorate for Social, Behavioral, and Economic Sciences and the NSF Office of Cyberinfrastructure. This project undertakes an interdisciplinary and novel approach to the problem of measuring the effects of investment in cyberinfrastructure to universities' production processes of research outputs and vital educational services. A decision to support funding of the infrastructure that supports research, or a decision to support funding of focused research activities, is an increasingly critical decision with far-reaching impacts not only to the institutions receiving those funds, but also to national competitiveness. While it is generally agreed that cyberinfrastructure is essential to scholarly inquiry in some science fields, the scope of cyberinfrastructure's broad effects on the growth of knowledge, to the academic enterprise, and to areas of science has not been explicitly quantified. This work extends the state of knowledge of frontier efficiency analysis (FEA) techniques, a rigorous statistically-grounded approach, and uses FEA in a novel application to examine the returns to cyberinfrastructure investments in research institutions. Such work is strongly interdisciplinary: experts in econometrics will be working collaboratively with experts in computing and cyberinfrastructure to apply and extend the state-of-the-art in data management, extend our developed Unified Data Framework, and prepare and curate a significant new body of data of great interest to decision-makers in cyberinfrastructure investment and science policy. This research also makes a contribution to statistical estimation theory, developing new central limit theorem results applicable to means of nonparametric frontier efficiency estimators, permitting testing of hypotheses regarding returns to scale and other aspects of universities' productivity. To overcome the problem that nonparametric frontier efficiency estimators are biased in finite samples and have slow convergence rates that depend on dimensionality of the particular problem, this project uses subsampling ideas to construct new statistics for hypothesis testing in nonparametric frontier efficiency estimators for which central limit theorem results can be obtained.Finally, the project makes a dual contribution to science and innovation policy and to cyberinfrastructure investment decisions, using frontier efficiency analysis (FEA) techniques to evaluate the productivity (i.e., efficiency in transforming inputs into outputs) of research institutions who have received various amount of cyberinfrastructure investments. Project personnel will collect and curate a large body of data on educational and research institutions' productivity and performance as a part of this analysis. The analysis of this data will allow examination of important science-policy questions on investment in cyberinfrastructure, such as "Does cyberinfrastructure enhance universities' research and educational output? If so, how, where, and by how much? Are returns to scale in cyberinfrastructure investment increasing, constant, or decreasing? What might be a method of allocating future scarce cyberinfrastructure resources efficiently across universities?" The project will support 50% of a postdoctoral associate and a full-time Ph.D. student in interdisciplinary econometric and computer-science research. The techniques and tools utilized in this research are to be incorporated into a newly-developed course in Data-Enabled Science, which, over the course of the project, is expected to reach over 40 students in a variety of science and engineering disciplines.The overall project is potentially transformative to the science of science policy: the project's development and demonstration of the use of formal measures for capturing the effectiveness of investment has the potential to significantly change the processes used to construct portfolios of funded scientific-research projects, and to add significant quantitative support to the policies by which these decisions are made. The specific case study (the effect of cyberinfrastructure investments on research and educational outcomes) can immediately inform institutions, states, and governmental funding bodies about opportunities for high-priority future investments in this area, particularly by identifying overlooked opportunities where modest investments will yield significant returns.

该INSPIRE奖是由科学和创新政策计划的部分资助，该计划在社会，行为和经济科学局以及NSF Cyber​​infrasture办公室的SBE多学科活动部门中。 该项目采用了跨学科和新颖的方法来衡量网络基础设施对大学的研究成果和重要教育服务的生产过程的影响的问题。 支持支持研究或支持重点研究活动资金的基础设施资金的决定，这是一个日益关键的决定，不仅对获得这些资金的机构，而且对国家的竞争力也有深远的影响。 尽管人们普遍认为，网络基础设施对于某些科学领域的学术询问至关重要，但网络基础设施对知识的增长，对学术企业的增长，对科学领域的广泛影响，尚未明确量化。 这项工作扩展了前沿效率分析（FEA）技术的知识状态，这是一种严格的统计基础方法，并在新颖的应用中使用FEA来检查研究机构中网络基础设施投资的回报。 这样的工作是强烈的跨学科：计量经济学专家将与计算和网络基础设施专家合作，以应用和扩展数据管理中的最新技术，扩展我们开发的统一数据框架，并准备和策划一个重要的新机构在网络基础设施投资和科学政策中，决策者兴趣的数据。 这项研究还为统计估计理论做出了贡献，开发了适用于非参数边界效率估计器的新中心限制定理结果，允许对有关规模的回报和大学生产力的其他方面进行假设的测试。为了克服以下问题：非参数边界效率估计器在有限样本中有偏见，并且具有较慢的收敛速率，取决于特定问题的维度，该项目使用子采样想法来构建非参数边界效率估算器中的假设测试的新统计数据最终，该项目对科学和创新政策做出了双重贡献，并使用前沿效率分析（FEA）技术来评估生产力（即将投入转换为产出变为产出的效率），对这些研究机构的生产率（即效率）为他们提供了双重贡献。已经收到了各种网络基础设施投资。 项目人员将收集并策划大量有关教育和研究机构的生产力和绩效的数据，作为该分析的一部分。对这些数据的分析将允许研究有关网络基础设施投资的重要科学政策问题，例如“网络基础设施是否可以增强大学的研究和教育成果？投资增加，恒定或减少？ 该项目将支持博士后副学士的50％和全职博士学位。跨学科计量经济学和计算机科学研究的学生。这项研究中使用的技术和工具将纳入一个新开发的数据支持科学课程中，该课程在项目的过程中预计将吸引40多名科学和工程学科的学生。总体项目可能对科学政策的科学政策有可能变革：该项目的开发和证明使用正式措施来捕获投资有效性，有可能显着改变用于构建资助的科学研究项目组合的过程，以及在做出这些决策的政策中增加大量的定量支持。具体的案例研究（网络基础设施投资对研究和教育成果的影响）可以立即为机构，州和政府资助机构提供有关在这一领域进行高优先级投资的机会的信息返回。