Communications in Information and Systems

Volume 21 (2021)

Number 3

COVID-19 data sharing and collaboration

Pages: 325 – 340



Dominique Duncan (Laboratory of Neuro Imaging, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, Calif., U.S.A.)


There is an immediate need to study COVID-19, and the COVID-19 Data Archive (COVID-ARC) 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 has been spreading rapidly across the world, and there are still many unknowns about 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 study transmission after vaccination. COVID-ARC 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. We have developed a platform of networked and centralized web-accessible data archives to store multimodal data related to COVID-19 and make them broadly available and accessible to the world-wide scientific community to expedite research in this area. 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.


informatics, datasets, harmonization, image segmentation, COVID-19, archive, machine learning, data analysis

A joint work with Alexis Bennett, Alexander Bruckhaus, Aksh Garg, Rachael Garner, Azrin Khan, Marianna La Rocca, Jiaju Liu, Aubrey Martinez, Noor Nouaili, Sana Salehi, and Yujia Zhang.

Received 9 December 2020

Published 4 June 2021