Collaborative Research: EAGER: Automating HERD Reporting Using Machine Learning and Administrative Data

国家科学与工程统计中心（NCSES）的高等教育研究与发展调查（HERD）数据是通过每年发送给大约900所大学和学院的调查工具收集的。每个机构都收集数据，以自己的方式对调查作出回应。大多数国家依靠高度劳动密集型的程序，根据工作性质、资金来源和科学领域收集有关支出和研究项目的信息并进行分类。 所采用的临时支出分类方法取决于执行这项任务的个人，因为他们随着时间的推移发展了必要的评价技能。 随着时间的推移，各机构之间可能缺乏一致性。 这项研究开发了必要的工具，利用大学的行政数据自动化的科学领域，目的和赞助商类型所需的响应NCSES羊群调查项目分类的必要和耗时的步骤。 这些结果将有助于更好地理解HERD和星星METRICS®报告的数据的相似性/差异，并就如何改进每个来源的数据收集提供建议。

The National Center for Science and Engineering Statistics (NCSES) Higher Education Research and Development Survey (HERD) data are collected through a survey instrument sent to approximately 900 universities and colleges annually. Each of these institutions collects data to respond to the survey in their own way. Most rely on highly labor-intensive processes to gather and classify information about expenditures and research projects in terms of the character of the work, funding sources and fields of science. The ad-hoc expenditure classification methods employed are dependent on the individuals carrying out the task as they develop the necessary evaluation skills over time. There is potential for a lack of consistency over time and across institutions. This research develop the tools necessary to leverage university administrative data to automate the essential and time-consuming step of classifying projects by science areas, purpose and sponsor type required to respond to the NCSES HERD Survey. The results will provide a better understanding of the similarities/differences in the data reported for HERD and STAR METRICS® and provide suggestions about how data collection for each source might be improved.