Post-Launch Maintenance for the Micronesia Reef Monitoring Platform
🪸 Post-Launch Maintenance for the Micronesia Reef Monitoring Platform
Client Sector: Marine Conservation & Research
Client: University of Guam (UOG) Marine Lab and Micronesia Coral Reef Monitoring Network
Service Type: Full-Stack System Modernization
Technologies Used: React, Django, R Shiny, Angular, Python, Pandas, NumPy, SciPy
🔗 Explore more blog posts from the Micronesia Reef Monitoring series here
🌐 Background
After the launch of the Micronesia Reef Monitoring platform, atWare continued providing structured post-release support. Our goal was to ensure the platform operated reliably in field environments, remained aligned with evolving user workflows, and performed efficiently under real-world conditions.
This maintenance phase focused on both functional updates and experience-driven enhancements, delivered with rapid turnaround. Most improvements were completed within three business days, and deployments occurred immediately after client approval.
🔧 Support Coverage
Our post-release scope included:
- Adjustments to filtering logic and data exports
- Improvements in interactive data entry behavior
- UX/UI enhancements for field usability and visualization clarity
- Backend performance tuning and deployment automation
- Infrastructure cost monitoring through daily Slack alerts to maintain budget transparency
🔁 Support Workflow
We followed a lightweight but reliable cycle for each improvement:
- Request — Requests from the client were sent via Slack
- Evaluation — Scope and complexity were quickly assessed
- Implementation — Updates were developed and tested in the development environment
- Preview — A screenshot, short video, and working version on the development environment were shared for client confirmation
- Deployment — Changes were deployed to production immediately upon approval
All requests were acknowledged and responded to on the same day, with fixes typically completed within 1–3 business days. Urgent updates were addressed and deployed on the same day when required.
🧪 Real-World Examples of Post-Launch Improvements
All of the examples below are drawn from actual client requests. They range from functional refinements to UX enhancements—and together, they reflect the care and responsiveness we apply in maintaining a system built for real-world use.
Functional Enhancements & Data Behavior Refinement
📄 Expanded Data Export Fields
Context: New fields—such as updated coral and fish species names and site metadata—had been successfully integrated into the database and UI but were missing from downloaded CSV reports. This mismatch created a gap in data sharing and downstream processing.
Resolution: We updated the export utility to include all relevant columns, ensuring parity between on-screen views and exported datasets. The update was validated in the development environment and deployed promptly.
Client Satisfaction:
Thanks much for this. I had a good look finally. The coral update columns are perfect. This allows users to see what the old names were, and what the new names are. For the benthic, also perfect.
— Peter Houk, Principal Investigator, UOG Marine Lab and Micronesia Coral Reef Monitoring Network
🐟 Context-Aware Fish Size Adjustments
Context: The portal previously applied a uniform +5cm adjustment to all fish size values from a specific observer team. With improved measurement reliability starting in 2024, the adjustment became outdated and skewed recent results.
Resolution: We introduced conditional logic to apply the +5cm correction only to data collected before 2024. The new results were reviewed visually by the client and confirmed to match independently generated reference graphs.
Client Satisfaction: The client verified the output accuracy and responded:
Graphs based on my own data and the portal look identical. Looks perfect and ready to implement.
— Peter Houk, Principal Investigator, UOG Marine Lab and Micronesia Coral Reef Monitoring Network
Data Entry Interaction Enhancements
These updates targeted faster, more intuitive workflows for researchers entering large volumes of observational data.
⬇️ Streamlined Row Entry and Auto-Fill Interaction
Context: Users faced two common challenges during data entry: the spreadsheet view did not auto-scroll when adding rows using the down arrow key, and there were no efficient tools for copying repeated values (like replicate numbers) across multiple rows. Additionally, the Alt + Down Arrow shortcut for inserting new rows was unintuitive for some users.
Resolution: We implemented a series of enhancements to optimize data entry flow:
- Enabled automatic scrolling when new rows are added, ensuring the active row remains in view—especially useful on tablets.
- Introduced Excel-style drag-to-fill functionality for faster population of repeated values.
- Enabled automatic row extension when the ↓ key is pressed on the last row.
- Preserved Alt + Down Arrow as an option for users who prefer manual control.
- Provided a short demonstration video and invited the client to explore and validate the improvements in the development environment.
Client Satisfaction:
The auto fill is a nice fix and improvement. Thanks and perfect.
— Peter Houk, Principal Investigator, UOG Marine Lab and Micronesia Coral Reef Monitoring Network
User Experience Improvements
Based on hands-on feedback from field users, we implemented targeted UI improvements that enhanced data entry clarity and efficiency—especially on constrained screen sizes and during repetitive workflows.
🖥️ Full-Screen Data Entry Mode
To maximize working space, we added a full-screen toggle for the data entry view. This minimized distractions, simplified navigation, and improved focus during high-volume entry tasks.
💡 Tooltip Guidance for Interface Buttons
To improve usability for first-time users and reduce uncertainty during data entry, we added context-aware tooltips to key buttons and icons. This provided immediate clarification without requiring users to reference external documentation.
🧱 Technical Stack
Our modular full-stack architecture allowed us to implement fast, safe updates:
- Frontend: React, React Query, Tailwind CSS, Zod, ECharts
- Backend: Django REST Framework, PostgreSQL
- DevOps: GitHub Actions CI/CD, Docker, AWS Services
- Monitoring: Daily Slack alerts for cost tracking and uptime monitoring
🌊 Outcomes and Impact
The post-launch support model enabled sustained impact across Micronesia’s marine research operations:
- Faster data entry and lower friction for field teams
- More accurate and meaningful data visualizations
- Higher platform adoption across new reef monitoring sites
- Cost awareness maintained through daily infrastructure usage monitoring and Slack-based budget notifications
✅ Summary
- Scope: Data logic adjustments, entry interaction improvements, field-optimized UX
- Response Time: Most tasks completed within 1–3 business days
- Deployment: Changes released immediately after client sign-off
- Impact: A trusted, efficient, and evolving platform tailored to the field
📌 Conclusion
Post-launch maintenance for the Micronesia Reef Monitoring platform was not just about keeping the system operational—it was about evolving the experience to match the real-world needs of reef researchers. Through a responsive, collaborative model, we delivered enhancements that balanced usability, accuracy, and speed.
📚 Explore more about our work on Micronesia Reef Monitoring
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