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奖部分由社会，行为和经济科学理事会SBE多学科活动部门的科学和创新政策计划科学以及NSF网络基础设施办公室资助。 该项目采取跨学科和新颖的方法来衡量网络基础设施投资对大学研究成果和重要教育服务生产过程的影响。 支持资助支持研究的基础设施的决定，或支持资助重点研究活动的决定，是一个日益重要的决定，不仅对接受这些资金的机构，而且对国家竞争力都有深远的影响。 虽然人们普遍认为网络基础设施对某些科学领域的学术研究至关重要，但网络基础设施对知识增长、学术事业和科学领域的广泛影响的范围尚未明确量化。 这项工作扩展了前沿效率分析（FEA）技术的知识状态，这是一种严格的基于知识的方法，并在一个新的应用程序中使用FEA来检查研究机构网络基础设施投资的回报。 这些工作具有很强的跨学科性：计量经济学专家将与计算和网络基础设施专家合作，应用和扩展最先进的数据管理技术，扩展我们开发的统一数据框架，并准备和管理一个重要的新数据体，这些数据对网络基础设施投资和科学政策的决策者非常感兴趣。 这项研究也作出了贡献，统计估计理论，开发新的中心极限定理的结果适用于非参数前沿效率估计的手段，允许测试的假设，规模回报和其他方面的大学的生产力。为了克服非参数前沿效率估计量在有限样本中有偏，且收敛速度慢（取决于特定问题的维数）的问题，本项目使用二次抽样的思想来构造新的统计量，用于非参数前沿效率估计量的假设检验，从而获得中心极限定理的结果。该项目对科学和创新政策以及网络基础设施投资决策做出了双重贡献，使用前沿效率分析（FEA）技术来评估生产力（即，（c）将投入转化为产出的效率）。 作为分析的一部分，项目人员将收集和整理关于教育和研究机构生产力和业绩的大量数据。对这些数据的分析将有助于研究有关网络基础设施投资的重要科学政策问题，例如“网络基础设施是否能提高大学的研究和教育产出？如果是，如何、在哪里、以及增加多少？ 网络基础设施投资的规模回报是增加的、不变的还是减少的？ 有什么方法可以在大学之间有效地分配未来稀缺的网络基础设施资源？“该项目将支持50%的博士后助理和全职博士。从事跨学科计量经济学和计算机科学研究学生。在这项研究中使用的技术和工具将被纳入一个新开发的课程数据使能科学，这在该项目的过程中，预计将达到40多名学生在各种科学和工程学科。整个项目是潜在的变革科学政策的科学：该项目开发和示范使用正式措施来获取投资的有效性，有可能大大改变用于构建资助的科学研究项目组合的过程，并为这些决策所依据的政策提供重要的量化支持。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）可以立即告知机构，国家和政府资助机构未来在这一领域进行高优先级投资的机会，特别是通过识别被忽视的机会，其中适度的投资将产生显着的回报。