Methods for Facilitating Multi-Institutional Data Collaboration Using Shiny
Lighting Talk, 3:15 - 3:35 PM
Research Objective:
To develop a data visualization tool for complex policy data that is normally difficult to access and analyze, and facilitate collaboration across multi-institutional research teams.
Study Design:
We developed a generalizable framework for accessible data sharing using R Shiny and AI-assisted code generation. Generative AI tools were used for rapid prototyping, generation of Shiny modules, and debugging.
Population Studied:
A comprehensive database of Assisted Living regulations from 2019-2023 across all 50 U.S. states and the District of Columbia.
Principal Findings:
These methods successfully produced a functional Shiny application standardizing regulatory data from all 50 states and DC, enabling interactive analysis of memory care policy trends across a five year span. Cross-university access was established for over 14 collaborators across six institutions, facilitating both asynchronous data exploration and live collaborative analyses.
Conclusions:
This development process is a replicable model for other interdisciplinary teams facing barriers to data use.
Implications for Policy or Practice:
This approach to a Shiny platform may prove useful for R users building data-sharing tools for large, multi-institutional teams. AI assistance accelerated development significantly, enabling the creation of an interface that made complex regulatory data accessible to researchers across disciplines.
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Pronouns: she/herPortland, OR, USA |