The Impact of Collaboration: Stories from COVID Information Commons Community Members

In 2020, the pandemic ushered in a “new normal,” an era of self-isolation and mandated quarantines for our collective well-being. At the COVID Information Commons, it has been inspiring to see another new normal emerge at the same time: one of collaboration across disciplines and institutional boundaries. Members of the CIC community have reached out to each other through the NSF-funded research database and through our community events, finding ways to work together with strangers who became colleagues, to further their research on COVID-19. The benefits of this collaborative approach are growing and spreading across the country and the world.

Below are just a few stories CIC community members have shared with us on how their research has benefited from the collaborative spirit of the community.  Stay tuned - we will be sharing further such stories from the community as our work continues next year. 

If you would like to learn more and get involved, visit the COVID Information Commons at, or join us at an upcoming event - our next community webinars are January 13 and February 10, 2021.


Michael Pazzani at University of California San Diego and Dominique Duncan at University of Southern California both spoke on a CIC webinar in September 2020. Through this connection, they are now collaborating on their research. Pazzani's research, Explainable Machine Learning for Analysis of COVID-19 Chest CT, is funded via a RAPID award through NSF’s CISE directorate, Division of Information and Intelligent Systems. Pazzani and his co-PI Albert Hsiao use, adapt, and evaluate explainable machine learning techniques for diagnosis of COVID-19 pneumonia. Imaging with x-ray or computed tomography (CT) is required to confirm a diagnosis pneumonia, which is the principal cause of death with COVID-19, as it leads to acute respiratory distress syndrome (ARDS). Pazzani’s project is therefore improving the understanding of mechanisms of COVID-19 and will help mitigate its impacts.

Dominique Duncan's RAPID award, funded by NSF’s CISE directorate, Division of Computing and Communication Foundations, is developing a COVID-19 data archive called COVID-ARC. COVID-ARC will archive multimodal and longitudinal data and provide various statistical and analytic tools for researchers. The data includes computed tomography (CT) and X-rays - a valuable resource for Pazzani’s team - as well as clinical-evaluation (symptoms), vitals (spirometry, temperature, respiration rate, heart rate, etc.), demographic, geolocation, electrocardiography (EKG), position emission tomography (PET), and magnetic resonance imaging (MRI) data, as well as other data that may be collected in the coming months. 

This connection through the COVID Information Commons has been able to further research into diagnosis of COVID-19 pneumonia, with both researchers benefiting from the collaboration.


Sarah Bowman is a structural biologist at the Hauptman-Woodward Medical Research Institute of University at Buffalo, with a RAPID award for Enhanced SARS-CoV-2 High-Throughput Crystallization for Structural Studies, funded by the NSF Directorate for Biological Sciences, Division Of Biological Infrastructure. She uses the CIC COVID Research Explorer Tool Tree Maps to identify collaboration opportunities in her domain. Her international research collaboration has also been enhanced via the CIC, as colleagues from as far as South Korea have been in touch to connect with her about her research.


Erick Jones of the University of Texas - Arlington mentioned in his remarks during the International FAIR Convergence Symposium on November 30 that over 30 people have reached out to him since his September lightning talk with CIC, sparking conversation and collaboration that would not have occurred otherwise. His EAGER: AI-Enabled Optimization of the COVID-19 Therapeutics Supply Chain to Support Community Public Health, is funded by NSF Engineering, Divison of Civil, Mechanical and Manufacturing Innovation. Through this project, he is investigating methods that integrate artificial intelligence, data science, and automatic data capture technologies to design supply mechanisms that effectively deliver therapeutic medicines to underserved urban communities, who are particularly vulnerable to COVID-19. 

“I want to thank you for making this happen,” Jones said at the outset of his remarks on November 30. “The COVID Information Commons is a wonderful source of information for students and faculty alike.”

Katie Naum
December 16, 2020