Automating Community-Engaged Research and Evaluation (CeRN): A computational framework for data collection, processing, and visualization
Community engaged research (CEnR) is an inclusive and equitable approach that partners researchers with communities throughout the entire research process. Our project is based on designing and implementing a framework for the existing natural language processes in order to make using said processes easier.
Full Project Description: Community engaged research (CEnR) is an inclusive and equitable approach that partners researchers with communities throughout the entire research process. This method emphasizes the relationship between researchers and communities, using both qualitative and quantitative methods to enhance the validity and relevance of data. Addressing health disparities by ensuring strategies and interventions are tailored to the needs of those most affected, CEnR builds trust in science by involving community members as equal partners from start to finish. The Wright Center’s CEnR Core aims to facilitate authentic engagement between researchers and the community, applying principles that leverage data to drive behavioral, social, service, or policy changes, and increase the capacity of both communities and researchers. A significant challenge is measuring the reach and impact or effectiveness of Wright Center’s CEnR programs as well as the CEnR programs being conducted from our larger VCU community. Traditionally assessed through surveys with low response rates, we are exploring new methods to evaluate our impact. Two measurable outcomes are the number of publications and IRB applications from VCU researchers that include a CEnR component. However, this is labor-intensive, requiring manual review of each publication/application. This project focuses on designing and implementing a computational framework and common data model to facilitate the collection, processing, analysis, and visualization of these data sources using NLP models. The goal is to make this process efficient and regular. Specifically, the project involves creating a prototype framework for data collection, initial processing, storage, and visualization. Students will develop the framework and API to allow any algorithm to process these datasets and return results in a common format, adhering to FAIR principles for data and FAIR4RS principles for code. The visualization component will include graphs, charts, and a user-friendly interface for non-programmers to access and analyze the data. By facilitating and speeding up the evaluation process, this project has the potential to significantly enhance the efficiency and accuracy of assessing CEnR programs, leading to more effective interventions and policies that can positively impact the community.
| Folder | Description |
|---|---|
| Documentation | all documentation the project team has created to describe the architecture, design, installation and configuratin of the peoject |
| Notes and Research | Relavent information useful to understand the tools and techniques used in the project |
| Status Reports | Project management documentation - weekly reports, milestones, etc. |
| scr | Source code |
- Amy Olex - VCU Wright Center for Clinical Trials - Sponsor
- Bridget McInnes - CS - Faculty Advisor
- Levi Thompson - CS - Student Team Member
- Abdul Koroma - CS - Student Team Member
- Jasper Early - CS - Student Team Member
- Tristan Weigand - CS - Student Team Member