Author
McGuinness, Deborah; Shaikh, Abdul; Moser, Richard; Hesse, Bradford; Morgan, Glen; Hunt, Yvonne; Tatalovich, Zaria; Willis, Gordon; Blake, Kelly; Courtney, Paul; Finney, Lila; Sanders, Amy; Ding, Li; Lebo, Tim; McCusker, Jamie; Contractor, Noshir; Huang, Yun; Devlin, Hugh
Other Contributors
Date Issued
2011-06-14
Subject
Population Science Grid
Degree
Terms of Use
Abstract
We describe our semantically-enabled approach to integrate, visualize, and explore health data. The project was conducted in a trans-disciplinary setting with population and behavioral scientists, social network scientists, data analysts, and computer scientists focused on making complex health-related data available, accessible, and understandable. One of the primary goals was to allow policy makers to explore potential correlations between health-related policies and behavior change. Other goals focused on demonstrating the value of linking open data and semantic technologies for exploration of data by research and consumer audiences. The initial setting includes comparison of smoking prevalence with potentially related data including cigarette taxes, price per pack, and policies limiting smoking in workplaces, restaurants, and bars, as well as personal information including education levels, employment, and various health statistics. The collaborative process, semantic data platform, demonstrations, and benefits of Linked Data for consumer data portals are also discussed.;
Department
Relationships
https://tw.rpi.edu/project/PopSciGrid;
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