Award Abstract #2027456

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

NSF Directorate:
CSE - Directorate for Computer and Information Science and Engineering
NSF Division:

Division of Computing and Communication Foundations

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Program Manager:

Phillip Regalia

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Dominique Duncan [email protected] (Principal Investigator)


University of Southern California
LOS ANGELES CA 900074304

NSF Program:
Smart and Connected Health
COVID-19 Research
Communications, and Information Foundations
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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.