CIC Session - International FAIR Convergence Symposium 2020
The COVID Information Commons (CIC) is a globally accessible open website to facilitate knowledge sharing and collaboration across various COVID research efforts. Funded by the U.S. National Science Foundation (NSF) Convergence Accelerator in May 2020 and launched in July 2020, the CIC website provides a portal into information regarding 700+ NSF-funded COVID Rapid Response Research (RAPID) projects, enabling researchers, students, and professionals from around the world to leverage the award information to collaborate. The CIC serves as a resource for researchers, students and decision-makers from academia, government, not-for-profits and industry to identify collaboration opportunities, to leverage each other's research findings, and to accelerate the most promising research to mitigate the broad societal impacts of the COVID-19 pandemic. The CIC website has evolved into a COVID Information Commons Community of hundreds of researchers, students and professionals, with monthly webinars hosting multiple COVID PI lightning talks, a vibrant website with user tutorials and videos, links to research funding opportunities, global datasets, portals and groups, invitations to future events, and recordings from CIC webinars. The CIC will be enhanced to integrate information provided by the COVID RAPID Principal Investigators (PIs) to communicate real time research results and collaboration opportunities by October 2020.
The CIC delivers COVID research information abiding by FAIR data principles. We make all the COVID RAPID research award and PI provided information Findable, Accessible globally, enabling Interoperability between researchers and their projects, and Reusability as the COVID research information is downloadable for reuse as appropriate.
In this panel session, Florence Hudson, Executive Director of the Northeast Big Data Innovation Hub at Columbia University and project leader for the COVID Information Commons and Community, will demonstrate the COVID Information Commons (CIC), and discuss ways to use it for research and researcher collaboration for 20 minutes. A panel of Principal Investigators funded by the U.S. National Science Foundation through NSF COVID RAPID grants who are members of the COVID Info Commons Community will join her to present their research in lightning talk format (5-10 minutes each) to enable global collaboration to mitigate the societal impact of COVID-19.
During the session, we will discuss how to further research collaboration in COVID-19 globally, expanding the reach of the COVID Information Commons community. We request a 120 minute session.
Researchers invited to present as panelists are:
1. Debra Laefer, New York University, Tandon School of Engineering: RAPID: DETER: Developing Epidemiology mechanisms in Three-dimensions to Enhance Response. This RAPID award is focused on understanding to what extent people coming from a COVID-19-infected environment interact with the built and natural environment. Specifically, we wanted to understand in three dimensions what people touched, how much they touched, and where they went coming out of hospitals and urgent care facilities at the peak of the COVID-19 outbreak.
2. Erick Jones, University of Texas at Arlington: EAGER: AI-Enabled Optimization of the COVID-19 Therapeutics Supply Chain to Support Community Public Health. Funded by NSF Engineering / Civil, Mechanical and Manufacturing Innovation. This EArly-concept Grant for Exploratory Research (EAGER) will investigate methods that integrate Artificial Intelligence (AI), data science, and automatic data capture technologies to design supply mechanisms that effectively deliver therapeutic medicines to underserved urban communities that are particularly vulnerable to this disease. Learn more about this project here.
3. Howard Stone, Princeton University: RAPID: Flow Asymmetry in Human Breathing and the Asymptomatic Spreader. Funded by NSF Engineering / Chemical, Biological, Environmental and Transport Systems. The focus of this research will be to quantify the complex flows associated with speech and breathing during a conversation or nearby encounter and show how transmission of particles between individuals occurs. The understanding gained from this work will provide actionable mitigation strategies to reduce transmission from asymptomatic people. These outcomes can be useful in the short term by offering routes to reducing potential viral transport and future infections.
4. Michael Pazzani, University of California San Diego: RAPID: Explainable Machine Learning for Analysis of COVID-19 Chest CT. Funded by NSF Computer and Information Science and Engineering / Information and Intelligent Systems. Given the need for rapid, more accurate diagnosis, this project will use, adapt, and evaluate explainable machine learning techniques to diagnosis of COVID-19 pneumonia. This project will improve the understanding of mechanisms of COVID-19 and will help mitigate its impacts.
5. Ashok Srinivasan, University of West Florida: Collaborative: RAPID: Leveraging New Data Sources to Analyze the Risk of COVID-19 in Crowded Locations. Funded by NSF Computer and Information Science and Engineering / Advanced Cyberinfrastructure. The goal of this project is to create a software infrastructure that will help scientists investigate the risk of the spread of COVID-19 and analyze future epidemics in crowded locations using real-time public webcam videos and location based services (LBS) data.
6. Dominique Duncan, University of Southern California: RAPID: COVID-ARC (COVID-19 Data Archive). Funded by NSF Computer and Information Science and Engineering / Computing and Communication Foundations. 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.
7. Debbie Kim, University of Chicago: RAPID: Pandemic Learning Loss in U.S. High Schools: A National Examination of Student Experiences. Funded by NSF Education and Human Resources / Research on Learning in Formal and Informal Settings. This project will use two, nationally representative, existing databases of high school students to study their experiences in STEM education during the COVID-19 pandemic. The study is particularly interested in finding patterns of inequities for students in various demographic groups underserved in STEM and who may be most likely to be affected by a hiatus in formal education.
8. Nora Garza, Laredo College: RAPID: Using real life COVID-19 data to teach quantitative reasoning skills to undergraduate Hispanic STEM students. Funded by NSF Education and Human Resources / Human Resource Development. This project at Laredo College, a two-year institution, seeks to investigate the effectiveness of using a fully-virtual undergraduate research format to enhance quantitative reasoning and literacy while promoting student persistence during a pandemic of COVID-19. Additionally, this project seeks to promote faculty development in guiding undergraduate research at a community college and in an online learning environment.
9. Ajitesh Srivastava, University of Southern California: RAPID: ReCOVER: Accurate Predictions and Resource Allocation for COVID-19 Epidemic Response. Funded by NSF Computer and Information Science and Engineering / Advanced Cyberinfrastructure. This project will provide a predictive understanding of the spread of the virus by developing machine learning based computational models to study the transmission of the virus and evaluate the impact of various interventions on disease spread. It also formulates and solves a resource allocation problem that can guide the response to contain the epidemic and prevent future outbreaks.