课题基金 / 基金详情

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

Collaborative Research: EAGER: Automating HERD Reporting Using Machine Learning and Administrative Data
合作研究:EAGER:使用机器学习和管理数据自动化 HERD 报告
批准号:
1547513
负责人:
Joshua Rosenbloom
金额:
$3.74万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2015
资助国家:
美国
项目状态:
已结题
起止时间:
2015-09-01 至 2018-08-31

项目摘要

项目成果

Joshua Rosenbloom的其他基金

相似基金

相关文献

中文摘要
翻译
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。
英文摘要
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.
期刊论文(0)
专著(0)
科研奖励(0)
会议论文
Collaborative Research: The Effect of State Disinvestment in Higher Education on Research Quality and Returns to Scale in Science Funding
  • 批准号:
    1854850
  • 项目类别:
    Standard Grant
  • 资助金额:
    $31.36万
  • 财政年份:
    2019
  • 负责人:
    Joshua Rosenbloom
  • 依托单位:
EAGER: Implementing Effective Sharing of Research Data
  • 批准号:
    1823496
  • 项目类别:
    Standard Grant
  • 资助金额:
    $22.93万
  • 财政年份:
    2018
  • 负责人:
    Joshua Rosenbloom
  • 依托单位:
SciSIP - NIH Workshop Promoting Research, Collaboration and Data Sharing
EESE: University of Kansas Initiative on Ethics Education in Science and Engineering (KUI-EESE)
国内基金
海外基金
Research on Quantum Field Theory without a Lagrangian Description
  • 批准号:
    24ZR1403900
  • 项目类别:
    省市级项目
  • 资助金额:
    --
  • 批准年份:
    2024
  • 负责人:
    SATOSHI NAWATA
  • 依托单位:
Cell Research
Cell Research
Cell Research (细胞研究)