RAPID: Dashboard for COVID-19 Scientific Development
- Funded by National Science Foundation (NSF)
- Total publications:0 publications
Grant number: unknown
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Key facts
Disease
COVID-19Start & end year
20202021Known Financial Commitments (USD)
$198,587Funder
National Science Foundation (NSF)Principal Investigator
Ying DingResearch Location
United States of AmericaLead Research Institution
University of Texas at AustinResearch 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
Scientific discovery depends on the accumulation of knowledge. There are thousands of articles on any given topic, but no one person can read them all. This limitation is more important in the COVID-19 era, where dependable knowledge can mean the difference between life and death. This project works to improve methods related to the synthesis of scientific knowledge by developing a visual dashboard to summarize COVID-19 related research efforts. The main goal is to integrate a current COVID-19 literature dataset from the Whitehouse with a knowledge graph from PubMed and a drug discovery knowledge graph developed by Data2Discovery. This would enable the creation of the ?Fight COVID-19 Dashboard,? a visualization tool that would centralize and visualize crucial, up to date data and scientific information related to COVID. This dashboard will help scientists and clinicians access and visualize the most recent information about COVID. Such information is also crucial for mining publications to generate research hypotheses and for identifying patterns of collaboration and innovation in scientific communication working to stop the spread of COVID. The PIs will make their data and the codes for constructing the dashboard open to the public to enable future efforts and enhance public trust in science through transparency.
This project develops the Fight COVID-19 Dataset and visual dashboard to advance information science and aid in the fight against COVID-19. This is accomplished by integrating a current COVID-19 literature dataset from the White House with a knowledge graph from PubMed and a drug discovery knowledge graph interlinking dozens of publicly available databases in pre-clinical drug discovery. This dashboard will display COVID-19 related information, including: 1) the currently most mentioned biological entities (e.g., drugs, diseases, vaccines, genes) in PubMed and clinical trials; 2) the evolution of related biological entities according to PubMed literature and clinical trials; 3) the network connections of related biological entities; 4) the lists of active scientists, teams, and institutions and their research topics; and 5) collaborations of scientific teams to enable networking and inspire potential collaborations to fight against COVID-19. This research will advance textual analysis methods by moving beyond keyword analysis towards advanced understanding of the objects that the keywords indicate and propelling textual methods towards knowledge graph-based analysis. It also adds a longitudinal element to recent investigations of the evolving pathway of COVID-19 scientific studies related to bio entities and links the science of science to related research domains in new and potentially innovative ways.
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.
This project develops the Fight COVID-19 Dataset and visual dashboard to advance information science and aid in the fight against COVID-19. This is accomplished by integrating a current COVID-19 literature dataset from the White House with a knowledge graph from PubMed and a drug discovery knowledge graph interlinking dozens of publicly available databases in pre-clinical drug discovery. This dashboard will display COVID-19 related information, including: 1) the currently most mentioned biological entities (e.g., drugs, diseases, vaccines, genes) in PubMed and clinical trials; 2) the evolution of related biological entities according to PubMed literature and clinical trials; 3) the network connections of related biological entities; 4) the lists of active scientists, teams, and institutions and their research topics; and 5) collaborations of scientific teams to enable networking and inspire potential collaborations to fight against COVID-19. This research will advance textual analysis methods by moving beyond keyword analysis towards advanced understanding of the objects that the keywords indicate and propelling textual methods towards knowledge graph-based analysis. It also adds a longitudinal element to recent investigations of the evolving pathway of COVID-19 scientific studies related to bio entities and links the science of science to related research domains in new and potentially innovative ways.
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.