IDRN — Research Data Collection & Dairy Analytics Platform
Researchers design their own survey instruments, field teams enter the data, and the dashboards update — without anyone touching a spreadsheet.
A research institute running field studies has the same problem on every project: the questionnaire is different, so the data structure is different, so the collection tool is different. In practice that means a new spreadsheet per study, distributed by email, filled in inconsistently by different field staff, and merged manually into something analysable — by which point the errors are baked in and the original responses are unrecoverable. Reporting is worse: monthly production figures that should roll up automatically get recalculated by hand each cycle, and there's no single place to see how a study is progressing. And existing data usually already lives in a legacy database that has to come across, not be re-keyed.
A Django platform where the survey instrument itself is data. A project is built from sections and questions with typed answer codes, so a researcher defines the study's structure through the interface and the data-entry screen is generated from it — a new study needs no new code. Fixed value sets and column ordering are configurable per project, so the exported data comes out in the shape the analyst expects. Projects are assigned to specific researchers, who see and enter only their own studies, and collected data can be listed and downloaded for analysis at any point. Alongside the generic collection layer sits a dedicated dairy analytics module with its own metrics and time-series points, monthly milk production results, and summary records, feeding two purpose-built dashboards. A SQL import job model handles bringing legacy datasets in as a tracked operation rather than a one-off script, and internal messaging with conversation threads keeps coordination between researchers on the platform.
Outcome
A working research platform where a new study is configured rather than built — the institute defines its own instruments, assigns them to field researchers, collects responses against a consistent structure, and exports analysable data without a developer in the loop. The dairy module turns what were manual monthly calculations into live reporting dashboards, and the import layer brought the institute's existing records onto the platform as a repeatable, tracked job. Deployed on cPanel under Passenger.
Key features
- Survey instruments defined as data — projects built from sections and typed questions
- Data-entry screens generated from the instrument definition, so a new study needs no new code
- Configurable fixed value sets and column ordering per project
- Project assignment scoping researchers to their own studies
- Data listing and download for analysis
- Dairy metrics with time-series data points
- Monthly milk production results with a dedicated reporting dashboard
- Summary records with a second analytics dashboard
- Tracked SQL import jobs for bringing legacy datasets across
- Internal messaging with conversation threads
- Researcher profiles with a custom authentication model
- Landmark and site settings management
- Rich-text editing for study content
- Rate limiting on sensitive endpoints
Tech stack
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