RAPID: COVID-ARC (COVID-19 Data Archive)

这个为期12个月的项目的目标是为与COVID-19相关的多模式（即人口统计信息、临床结果报告、成像扫描）和纵向数据建立数据档案，并为研究人员提供各种统计和分析工具。目前迫切需要研究SARS-CoV-2和COVID-19，该档案提供了数据访问以及用户友好的工具，供研究人员进行分析，以更好地了解COVID-19并鼓励在这项研究上的合作。COVID-19大流行正在全球迅速蔓延，各国政府正在实施旅行禁令、隔离法律、关闭企业和学校以及许多其他限制措施，以遏制病毒和限制传播。然而，关于SARS-CoV-2和COVID-19，我们仍有很多未知之处。世界各地的科学家迫切需要共同努力建立病毒模型，研究病毒如何随着时间的推移而变化，了解病毒如何传播，并发现疫苗。该项目的工作还可以通过建立基础设施使研究人员能够在紧急情况下迅速收集数据并进行分析，从而使科学家为未来的流行病做好准备。现有资源追踪每个地区的病例数，但缺乏成像和其他模式，这些模式结合起来，将有助于研究人员在病毒传播之外真正了解COVID-19，以寻找潜在的疫苗。该方法是开发一个联网和集中的网络数据档案平台，以存储与SARS-CoV-2和COVID-19相关的多模式数据，并使其广泛提供给全球科学界，以加快该领域的研究，因为COVID-19大流行的紧迫性。这些数据将包括临床评估（症状）、生命体征（肺活量测定、体温、呼吸频率、心率等）、人口统计学、地理位置、心电图（EKG）、计算机断层扫描（CT）、x射线、体位发射断层扫描（PET）和磁共振成像（MRI）数据以及未来几个月可能收集的其他数据。通过利用南加州大学神经成像实验室之前在开发数据存储库和存档功能方面的工作，COVID-ARC （COVID-19数据存档）旨在提供一个高效、安全的数据存储库平台，促进数据访问和分析。COVID-ARC为研究人员提供了可视化和分析各种类型数据的工具，以及一个网站，其中包含培训、公告、虚拟信息会议和知识库工具，研究人员可以在其中发布问题并从社区获得答案。该奖项反映了美国国家科学基金会的法定使命，并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，被认为值得支持。

The goal of this 12-month project is to develop a data archive for multimodal (i.e., demographic information, clinical-outcome reports, imaging scans) and longitudinal data related to COVID-19 and to provide various statistical and analytic tools for researchers. There is an immediate need to study SARS-CoV-2 and COVID-19, and this archive provides access to data along with user-friendly tools for researchers to perform analyses to better understand COVID-19 and encourage collaboration on this research. The COVID-19 pandemic is spreading rapidly across the world, and governments are imposing travel bans, quarantine laws, business and school closings, and many other restrictions in efforts to contain the virus and limit the spread. However, much is still unknown about SARS-CoV-2 and COVID-19. There is an urgent need for scientists around the world to work together to model the virus, study how the virus has changed and will change over time, understand how it spreads, and discover a vaccine. The work from this project can also prepare scientists for future pandemics by putting the infrastructure in place to enable researchers to aggregate data and perform analyses quickly in the event of an emergency. Existing resources track how many cases are tallied per region, but lack imaging and other modalities that, when combined, will facilitate the ability for researchers to truly understand COVID-19 beyond the spread of the virus, in search of potential vaccines.The approach is to develop a platform of networked and centralized web-accessible data archives to store multimodal data related to SARS-CoV-2 and COVID-19 and make them broadly available and accessible to the world-wide scientific community to expedite research in this area due to the urgent nature of the COVID-19 pandemic. The data will include clinical-evaluation (symptoms), vitals (spirometry, temperature, respiration rate, heart rate, etc.), demographic, geolocation, electrocardiography (EKG), computed tomography (CT), X-rays, position emission tomography (PET) and magnetic resonance imaging (MRI) data as well as other data that may be collecting in the coming months. By leveraging previous work in developing data repositories and archival capabilities at the Laboratory of Neuro Imaging at the University of Southern California, COVID-ARC (COVID-19 Data Archive) aims to provide an efficient and secure data-repository platform that facilitates data access and analysis. COVID-ARC provides tools for researchers to visualize and analyze various types of data as well as a website with tools for training, announcements, virtual information sessions, and a knowledgebase wherein researchers post questions and receive answers from the community.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.