RAPID: COVID Information Commons (CIC)
- Funded by National Science Foundation (NSF)
- Total publications:0 publications
Grant number: 2028999
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Key facts
Disease
COVID-19Start & end year
20202020Known Financial Commitments (USD)
$200,000Funder
National Science Foundation (NSF)Principal Investigator
Jeannette WingResearch Location
United States of AmericaLead Research Institution
Columbia UniversityResearch Priority Alignment
N/A
Research Category
13
Research Subcategory
N/A
Special Interest Tags
Data Management and Data Sharing
Study Type
Not applicable
Clinical Trial Details
N/A
Broad Policy Alignment
Pending
Age Group
Not Applicable
Vulnerable Population
Not applicable
Occupations of Interest
Not applicable
Abstract
Office of the Director - This project will create a COVID Information Commons (CIC) website to facilitate knowledge sharing and collaboration across various COVID research efforts, especially focusing on all the NSF-funded COVID Rapid Response Research (RAPID) projects. The CIC will serve as a resource for researchers as well as decision-makers from government, academia, not-for-profit and industry to leverage each other's findings, and invest in and accelerate the most promising research to mitigate the broad societal impacts of the COVID-19 pandemic. It will also serve as a model for integrated knowledge sharing and collaboration on other public health challenges, in benefit to society. Projects will be able to enter and publish information about their efforts in ways that are most relevant and user-friendly for a variety of potential stakeholders from academia, industry, government, and non-profit sectors. Information will be organized in multiple ways, for example, by research topics areas and by geography. In addition to information from NSF COVID-19 RAPID projects, the COVID Information Commons will incorporate coronavirus-related information from NSF Open Knowledge Network projects, as well as from other NSF research projects in general.
The COVID Information Commons will utilize information science methods to bring together information about the collection of COVID-19 RAPID projects funded by the National Science Foundation. A wide array of research efforts are underway to study the impacts of the pandemic in fields as far ranging as biophysics, social justice/inequity, behavioral science, public health, supply chains, and risk management. The CIC will semantically link information across projects to provide a more holistic view across distinct efforts, including efforts such as the COVID projects in the NSF Open Knowledge Network. The resulting, concise, curated, integrated resource will provide insight into NSF-funded COVID RAPID projects and facilitate collaborations among such efforts. These objectives will be achieved using information science approaches to 1) compile a comprehensive list of NSF COVID RAPID awards, along with relevant details for each project, 2) link to any publicly available data sets and data feeds, 3) organize the information and data feeds, for example, by categories of research areas and/or geography, using a meta-data schema developed for the resource and existing taxonomy and semantic frameworks; 4) design and develop a web portal to allow project teams to publish their data, or links to the data, and present project information in ways that are most relevant and user friendly for researchers in academia, industry, and government; 5) integrate the schema.org COVID-19 annotated data to enable more effective identification, retrieval, and integration of relevant data. A Minimum Viable Product for the website will be developed first, working with stakeholders in the community to prioritize features and add new functionality. In addition to the Information Commons, the project will also assess the effort and feasibility of implementing a data and model commons?to share datasets as well as data-driven models, such as machine learning models related to COVID-19.
This RAPID award is made by the Convergence Accelerator program in the Office of Integrative Activities with funds from the Coronavirus Aid, Relief, and Economic Security (CARES) Act.
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.
The COVID Information Commons will utilize information science methods to bring together information about the collection of COVID-19 RAPID projects funded by the National Science Foundation. A wide array of research efforts are underway to study the impacts of the pandemic in fields as far ranging as biophysics, social justice/inequity, behavioral science, public health, supply chains, and risk management. The CIC will semantically link information across projects to provide a more holistic view across distinct efforts, including efforts such as the COVID projects in the NSF Open Knowledge Network. The resulting, concise, curated, integrated resource will provide insight into NSF-funded COVID RAPID projects and facilitate collaborations among such efforts. These objectives will be achieved using information science approaches to 1) compile a comprehensive list of NSF COVID RAPID awards, along with relevant details for each project, 2) link to any publicly available data sets and data feeds, 3) organize the information and data feeds, for example, by categories of research areas and/or geography, using a meta-data schema developed for the resource and existing taxonomy and semantic frameworks; 4) design and develop a web portal to allow project teams to publish their data, or links to the data, and present project information in ways that are most relevant and user friendly for researchers in academia, industry, and government; 5) integrate the schema.org COVID-19 annotated data to enable more effective identification, retrieval, and integration of relevant data. A Minimum Viable Product for the website will be developed first, working with stakeholders in the community to prioritize features and add new functionality. In addition to the Information Commons, the project will also assess the effort and feasibility of implementing a data and model commons?to share datasets as well as data-driven models, such as machine learning models related to COVID-19.
This RAPID award is made by the Convergence Accelerator program in the Office of Integrative Activities with funds from the Coronavirus Aid, Relief, and Economic Security (CARES) Act.
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.