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

与许多其他公共和私营企业一样，学术机构在COVID-19大流行期间难以继续运营，同时保护学生，教师和员工。几乎所有的大学都面临着相当大的服务中断，因为他们做出了从课堂教学过渡到远程教学，保持宿舍开放或让学生回家的决定。该快速反应研究拨款（RAPID）项目将收集、清理、链接、去识别、管理、存档和情境化与大型公立大学流行病期间学术课程交付相关的数据，并为相关决策提供信息。克莱姆森大学在大学决策方面提供了丰富的案例研究，并将允许研究界审查所做的决定，决策所依赖的可用性数据，以及这些决定的结果。 预计使用该数据集的进一步研究将为决策提供信息，特别是在数据需求方面，以改善类似大规模干扰的规划。 该项目的一个新特点是数字和文本数据的配对，使研究人员能够发现政策和收集的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提出和回答反映大流行开始和最初12-18个月内迅速变化的环境的研究问题。该项目将整合来自大学多个来源的各种数据，包括住房和食堂数据、班级配置数据、跨校园测试数据、废水测试数据、疫苗接种数据以及来自多个校园决策者的定性数据。 所有数据将被去识别化并适当汇总，该项目将不涉及人类受试者。 这些数据将被保存在一个中央位置，以符合信息提取和查询的方式映射，并根据大学管理部门的决策及其结果进行背景化。在克莱姆森计算和信息技术人员和设施的支持下，将适当存储在电子表格或数据库中的数值研究数据存档。将通过克莱姆森大学为个人研究人员和研究小组提供数据访问权限。数字数据，如电子邮件，网站存档，会议记录和其他文本数据，适当编辑以隐藏敏感信息，将被存档，供社会科学研究人员使用，并将数字研究数据置于上下文环境中。这些数据文件将被存入TigerPrints（数字共享），这是克莱姆森大学图书馆的开放获取机构知识库和出版/传播平台。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

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.