EAGER: Data Infrastructure to Enhance Research on the Scientific Workforce

美国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，但由于调查工具的变化和数据格式的限制，这些数据并没有被更大的科学与创新政策科学（SciSIP）社区广泛使用。NSF对NCSES数据只支持SAS，而大多数研究人员使用STATA。这项拟议的研究将打破sciip社区使用SESTAT和SDR数据的障碍，从长远来看，可能会产生关于科学政策的新见解。该项目将创建数据基础设施并增强NCSES数据，这些数据将发布到国家经济研究局（NBER）网站上，供更广泛的研究界使用。拟议的数据工具和数据集是科学与创新政策研究界的公共产品。在开发这些工具的过程中，将培训一名研究生。这个项目创造了1993 ？2013年博士学位获得者协调调查（SDR）和协调科学与工程数据系统（SESTAT）数据。SDR和SESTAT变量的定义都发生了变化，增加了主要字段，问题的答案也发生了变化。本提案创建SAS和STATA代码，用于SDR和SESTAT微数据的限制使用和公共使用版本，尽可能在2013年变量定义的基础上协调变量定义。该源代码将与工作论文一起发布到NBER网站上。其次，它将创建一个分配给美国大学的专利数据集。美国专利和商标局已经将专利与大学的受让人进行了匹配。虽然一些被委派的大学是单一的校园，但也有一些是大型大学系统（例如加州大学的校务委员会）。该项目将使用多个资源将专利分配给各个校园。此外，PI将确定这些专利是否承认联邦研究资助及其资金来源。专利数据将使用IPEDS代码链接到校园，并可以合并到SDR或链接到其他专利数据。第三，它将创建一个NSF和NIH资助排名的数据集，作为质量衡量标准。先前的研究发现，NIH资助排名比NRC排名或卡内基排名更能衡量机构质量。本项目拟创建与机构IPEDS代码匹配的按主要研究领域和年份划分的机构NIH和NSF资助排名数据。然后，这些数据可以通过IPEDS代码合并到SDR数据中，以便更好地衡量机构质量。

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.