Dr. Isaac Cho is an assistant professor in the Department of Computer Science in the College of Engineering at N.C. A&T. Cho and a group of collaborators representing the University of North Carolina at Charlotte, Kansas State University, Notre Dame University, the University of Rochester, the University of Virginia, the University of Rhode Island and Cornell University are building a data aggregating, mining and visualization tool which will allow users to access federal data across all fifty states simultaneously.
The project (called F-DASH, short for the Federalism Data and Advanced Statistics Hub) represents a single, comprehensive source of data and analysis on governing institutions and public policies across the states of the United States of America.
Dr. Jason Windett at the University of North Carolina at Charlotte is the lead-PI of this project, funded by a $1 million dollar NSF Convergence Accelerator grant which commenced in September 2019. NSF Convergence Accelerator grants are designed to support team-based, multi-disciplinary efforts that address challenges of national importance that show potential for deliverables in the near future. Cho’s grant will conclude in May 2020, at which time the team expects follow-on funding to move their prototype to the next phase.
Though Cho is a computer scientist, he is applying his knowledge to solving a daunting public policy issue. While the 50 states represent an ideal venue for understanding the effects of policy choices because they routinely experiment with different solutions to important challenges, the decentralized nature of the American federal system impedes efforts to compare economic, social, or other outcomes across the states. Indeed, researchers must often engage in 50 separate data collection processes due to the unique challenges of data acquisition in each state!
For this reason, researchers or government agencies commonly collect only the minimum they need to address a particular question. Moreover, they often do not share these data, and those who do make data publicly available have no obvious way to connect their data to data collected by others!
Public policy at the state level has a direct impact on citizens’ daily lives, including their health, education and employment. The F-DASH tool will provide a centralized location for data collection and analysis related to institutions, policies and their implications. The data will include policy documents produced by government such as legislative bills, as well as commonly-used derivative measures.
Cho’s team has created an F-DASH prototype which they are testing during this initial nine-month funding period. If the proof-of-concept is successful, Cho’s team will move forward in its effort to build the web-based F-DASH application which people can access it through a web-browser without installation. Users will be able to download the application to access an unprecedented amount policy, social, and economic data, as well as harness analytical tools to help them easily explore, visualize, and analyze this information. F-DASH has the potential to provide significant time-saving benefits to researchers, educators and the public.
The F-DASH project is truly a cross-institutional, interdisciplinary research effort. Utilizing expertise from eight universities, the effort incorporates perspectives from applied statistics, computer science, geography, philosophy, political science, public policy and sociology. The project also draws on technical expertise from partner organizations such as Open States, the Society for Public Health Educators and the State Politics and Policy Section of the American Political Science Association.
These collaborations and the overall F-DASH effort should facilitate breakthroughs by researchers, innovations by practitioners, resulting in new classroom materials for teachers and new opportunities for engagement by citizens. The F-DASH will also promote innovation in complex database design and provide an extensive test-bed for researchers interested in text mining, large-scale relational databases, static and dynamic network analysis and many other topics.