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Table 2 Logic model for the framework of Translational Data Analytics in Environmental Health

From: Translational data analytics in exposure science and environmental health: a citizen science approach with high school students

Needs/ Rationale Inputs Activities Outputs Short-term Outcomes Long-term Outcomes
Lack of air quality data in microenvironments (Hilliard, OH) Low-cost air quality sensors
High School teacher(s) and students (Seniors) enrolled in Engineering Design and Development Course.
Expertise in environmental health, environmental engineering, sensor calibration and data analytics.
Graduate students to perform sensor calibration and assist with computer programming.
Introduction (Phase I):
-Academic faculty introduced to each other and set expectations for collaboration
-Academic faculty introduced to a high school teacher(s) and discuss academic-community partnership and set partnership goals, expectations and timelines
-Academic faculty introduced to high school students and meet to introduce the project
Education (Phase II):
-Academic faculty educate each other about disciplinary perspectives and set boundaries based on training and experience
-Academic faculty learn from high school teacher about curricular objectives and share their expertise with high school students
Sensor Fabrication, Calibration and Deployment (Phase III):
-Students assemble sensors, fabricate sensor housing and identify potential locations for siting;
-Academic faculty provides expertise to students and facilitates sensor calibration
Data Analytics (Phase IV):
-Academic faculty provided computer programs/code for submitting data from sensors to internet cloud-based storage and maintained web-based application for visualizing and analyzing data from sensors
-High School teacher and students provided feedback on the design and analytics features of the web-based application
Translation (ongoing across phases):
-All members of the partnership participated in local media interviews about the project
-presented project goals and progress
-Students introduced the project to teachers in the school district through presentations at professional development settings
-Students raised awareness about air pollution and health impacts at local Earth Day events
1. Clear communication plan, timeline and information sharing platform (Google Drive) between all partners.
2. Sustained partnerships involving multiple cohorts of students throughout the project.
3. Multiple fabrication plans for sensor housing; schematics and working versions of the integrated circuit board for Raspberry Pi, sensors and cables.
4. Design matrix for selecting schools based on multiple objectives for exposure assessment, data collection, and socioeconomic factors.
5.Open-source code for data logging from sensor to cloud-based storage.
6. A working version of a ShinyR web-based application to visualize air quality data.
Raise awareness about air pollution levels in Hilliard, OH.
Offer hands-on training opportunities for students at the intersection of public health and engineering.
Build sustainable academic-community partnerships.
Deploy a working version of a low-cost air quality sensor.
Expand low-cost air quality sensor network to 10 or more school building in the Hilliard School District
Disseminate curricular materials based on an existing partnership that consists of plans to implement each phase of the partnership in a scalable manner.
Add additional sensors to the module, such as noise, and maintain current sensor networks through the extension of the academic-community partnership.
Continuous validation of low-cost sensors with federal reference monitors.