EAGER: Data Infrastructure to Enhance Research on the Scientific Workforce

EAGER：加强科学劳动力研究的数据基础设施

• 批准号: 1647187
• 负责人: Donna Ginther
• 金额: $ 15万
• 依托单位: University of Kansas Center for Research Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2019-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1647187&HistoricalAwards=false
• 关键词: EAGER; Data; Infrastructure; Enhance; Research

The National Center for Science and Engineering Statistics (NCSES) at the National Science Foundation collects high-quality data on the scientific workforce, but these data are not as widely utilized by the larger Science of Science and Innovation Policy (SciSIP) community because of changes in survey instruments over time and the data format restrictions. The NSF only supports SAS for NCSES data whereas the majority of researchers use STATA. This proposed research will break down the barriers to using SESTAT and SDR data for the SciSIP community that in the long-run, could yield new insights about science policy. This project will create data infrastructure and enhancements for NCSES data that will be posted to the National Bureau of Economic Research (NBER) website for use by the broader research community. The proposed data tools and data sets are public goods for the Science of Science and Innovation Policy research community. In the process of developing these tools one graduate student will be trained. This project creates the 1993 ? 2013 Harmonized Survey of Doctorate Recipients (SDR) and Harmonized Science and Engineering Data System (SESTAT) data. In both the SDR and SESTAT variable definitions have changed, major fields have been added, and answers to questions have also changed. This proposal creates SAS and STATA code for use with the restricted-use and public-use versions of the SDR and SESTAT micro data that harmonizes variable definitions based on the 2013 variable definitions where possible. This source code will be accompanied by a working paper and posted to the NBER website. Second, it will create a data set of patents assigned to US campuses. The United States Patent and Trademark Office has matched patents to university assignees. While some of the university assignees are single campuses, several are large university systems (e.g. the Regents of the University of California). This project will use several sources to assign patents to individual campuses. In addition, the PI will identify whether these patents acknowledge federal research funding and the source of that funding. The patent data will be linked to campuses using IPEDS codes and can be merged onto the SDR or linked to other patent data. Third, it will create a data set of NSF and NIH funding ranks to be used as quality measures. Previous research has found that NIH funding rank was a better measure of institution quality than NRC ranking or Carnegie ranking. This project proposes to create data on the NIH and NSF funding rank of an institution by major research field and year matched to institutional IPEDS codes. These data can then be merged onto SDR data by IPEDS code in order to have improved measures of institutional quality.

国家科学基金会的国家科学与工程统计中心（NCSES）收集了有关科学劳动力的高质量数据，但是由于随着时间的推移和数据格式限制的调查工具的变化，这些数据并未被较大的科学和创新政策科学科学（SCISIP）社区广泛使用。 NSF仅支持NCSES数据的SAS，而大多数研究人员使用Stata。 这项拟议的研究将打破使用SESTAT和SDR数据为SCISIP社区使用的障碍，从长远来看，这可能会产生有关科学政策的新见解。 该项目将为NCSES数据创建数据基础架构和增强功能，并将发布到国家经济研究局（NBER）网站，以供广泛的研究社区使用。 提出的数据工具和数据集是科学与创新政策研究界的公共物品。在开发这些工具的过程中，将对一名研究生进行培训。 这个项目创建了1993年？ 2013年对博士学位获得者（SDR）和协调的科学与工程数据系统（SESTAT）数据的协调调查。 在SDR和SESTAT变量定义都发生了变化中，已经添加了主要领域，并且问题的答案也发生了变化。该提案创建了SAS和Stata代码，供SDR和SESTAT Micro Data的限制使用和公共用途版本使用，该版本在可能的情况下根据2013年可变定义统一了可变定义。 该源代码将附有工作文件，并发布到NBER网站。 其次，它将创建分配给美国校园的专利数据集。 美国专利商标局已将专利与大学受让人相匹配。 虽然一些大学受让人是单个校园，但有几个是大型大学系统（例如，加利福尼亚大学的摄政）。 该项目将使用多种来源将专利分配给各个校园。此外，PI还将确定这些专利是否确认联邦研究资金和资金的来源。专利数据将使用IPEDS代码链接到校园，并可以合并到SDR上或链接到其他专利数据。 第三，它将创建NSF和NIH资金等级的数据集，以用作质量措施。 先前的研究发现，NIH资金排名比NRC排名或卡内基排名更好地衡量了机构质量。该项目建议通过主要的研究领域和年份与机构IPEDS代码相匹配的机构NIH和NSF资金等级的数据。然后可以将这些数据通过IPEDS代码合并到SDR数据中，以提高机构质量的衡量标准。