RAPID: Archiving and Contextualizing Data around Academic Program Delivery during the Covid-19 Pandemic

RAPID：Covid-19 大流行期间学术项目交付的数据归档和背景化

• 批准号: 2135089
• 负责人: Mary Kurz
• 金额: $ 14.66万
• 依托单位: Clemson University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2135089&HistoricalAwards=false
• 关键词: RAPID; Archiving; Contextualizing; Data; around

Like many other public and private enterprises, academic institutions have struggled to continue operation during the COVID-19 pandemic while protecting students, faculty and staff. Almost all universities faced considerable service disruptions as they made decisions such as transitioning from in-class teaching to remote teaching and keeping dorms open or sending students home. This Grant for Rapid Response Research (RAPID) project will collect, clean, link, de-identify, curate, archive, and contextualize data related to and informing decisions made about academic program delivery during the pandemic at a large public university. Clemson University provides a rich case study in university decision-making and will allow the research community to review the decisions made, availability data on which the decisions relied, and outcomes of those decisions. It is expected that further studies using this dataset will inform the decision making, particularly with respect to data needs, to improve planning for similar large scale disturbances. A novel feature of this project is the pairing of numerical and textual data, allowing researchers to discover the interplay between policy and collected data. Researchers will be able to utilize the collected quantitative and textual data to pose and answer research questions that reflect the rapidly changing environment during the beginning and first 12-18 months of the pandemic. The project will consolidate a variety of data from many sources across the university, including housing and dining hall data, class configuration data, cross-campus testing data, wastewater testing data, vaccine uptake data, and qualitative data from multiple campus decision-makers. All data will be deidentified and appropriately aggregated and the project will not involve human subjects. These data will be preserved in a central location, mapped in a manner consistent with information extraction and queries, and contextualized in terms of decisions made by university administration, and their outcomes. Numerical research data, stored appropriately in spreadsheets or databases will be archived with support of the Clemson Computing and Information Technology staff and facilities. Access to the data will be provided for individual researchers and research groups through Clemson University. Digital data, such as emails, website archiving, meeting minutes, and other textual data, appropriately redacted to hide sensitive information, will be archived for the use of social science researchers and to contextualize the numerical research data. The data files will be deposited into TigerPrints (Digital Commons), which is Clemson University Libraries’ open access institutional repository and publishing/dissemination platform.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

像许多其他公共企业和私营企业一样，学术机构在保护学生，教职员工和教职员工的同时仍在努力继续运作。几乎所有大学都面临着大量的服务中断，例如从课堂教学过渡到远程教学，并保持宿舍开放或送学生回家。这项用于快速响应研究（快速）项目的赠款将收集，清洁，链接，取消识别，策展人，档案和背景化与与大型公立大学大流行期间有关学术计划交付的数据的数据和情境化。克莱姆森大学（Clemson University）在大学决策方面提供了丰富的案例研究，并将允许研究社区审查做出的决策，依赖决策的可用性数据以及这些决策的结果。可以预期，使用此数据集的进一步研究将为决策，尤其是在数据需求方面提供信息，以改善针对类似大规模灾难的计划。该项目的一个新颖功能是数字和文本数据的配对，使研究人员可以发现策略和收集数据之间的相互作用。研究人员将能够利用收集的定量和文本数据来提出和回答研究问题，这些问题反映了大流行的开始和前12-18个月的迅速变化的环境。该项目将巩固大学中许多来源的各种数据，包括住房和餐厅数据，类配置数据，跨校园测试数据，废水测试数据，疫苗吸收数据以及来自多个校园决策者的定性数据。所有数据都将被确定并适当地汇总，并且该项目将不涉及人类受试者。这些数据将保存在中央位置，以与信息提取和查询一致的方式映射，并根据大学行政的决定及其结果而进行背景。数值研究数据，适当存储在电子表格或数据库中的数据将在Clemson计算和信息技术人员和设施的支持下进行存档。将通过克莱姆森大学为个别研究人员和研究小组提供数据访问。数字数据（例如电子邮件，网站归档，会议记录和其他文本数据）将被适当编辑以隐藏敏感信息，以供供社会科学研究人员使用，并将数字研究数据与情况相关化。数据文件将存放到Tigerprints（Digital Commons）中，这是Clemson University图书馆的开放访问机构存储库和出版/传播平台。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的审查标准来通过评估来通过评估来支持的。