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📋 Standardising Data Collection for Beekeeping Research

An apiary of sensor-monitored hives showing how protocol adherence controls data quality: sampling jitter, missing readings and noisy values in a live data dictionary.

Entomology & Insect Behaviour3DAdvanced60 FPS

An apiary of sensor-monitored hives, each firing readings into a live data dictionary log, showing exactly how protocol adherence — timing, dropout and noise — determines whether a multi-hive dataset can be trusted.

🔬 What It Demonstrates

Every hive samples on a shared interval and pushes a labelled row (hive ID, timestamp, metric, value) into the log. Lowering protocol adherence introduces sampling jitter, dropped/malformed rows and noisy values — the same data-quality failures seen in real inconsistent field studies.

🎮 How to Use

Set the number of hives, the intended sampling interval, and protocol adherence, then pick which metric is being recorded. Watch the data pillars and the log panel respond, and track total readings, missing-data rate and value spread live.

💡 Did You Know?

National monitoring networks such as COLOSS and the UK's BeeBase only produce usable pooled statistics because every contributing apiary follows the same written protocol and data dictionary — without that, identical raw numbers can mean different things at different sites.

⚙ Under the hood

An apiary of sensor-monitored hives showing how protocol adherence controls data quality: sampling jitter, missing readings and noisy values in a live data dictionary.

beesapiarysensorsdata collectionresearchhive monitoringThree.js

3D · Three.js / WebGL renderer · 60 FPS target · runs fully client-side, no install