Research Station ActiveLIVE

Amphibian
Environmental
Research Lab

Real-time environmental monitoring of Litoria caerulea.IoT sensors. AI-powered health analysis. Open science.

Access Live Data→View Research↓
🐸

Coming Soon

24/7 Live stream coming soon!

SOON
0+
Data Points Collected
0%
System Uptime
0
Sensor Readings/Day
0%
Model Accuracy
Real-Time Telemetry

Environmental Metrics

24-hour sensor data visualization from our habitat monitoring system

Temperature

25.7°C
24h agoNow

Humidity

68.8%
24h agoNow

Water pH Level

7.06pH
24h agoNow

Water Quality Index

91.6%
24h agoNow

Activity Level

75%
24h agoNow

UV Index

4.8UV
24h agoNow
Research Methodology

How It Works

Combining IoT sensor networks with AI-powered analysis for comprehensive amphibian care

IoT Sensor Array

Multi-spectral environmental sensors measuring temperature, humidity, UV radiation, water chemistry, and atmospheric conditions every 30 seconds.

6 sensor types30s intervals±0.1°C accuracy

Data Pipeline

High-frequency data ingestion and processing pipeline with anomaly detection, trend analysis, and automated alerting systems.

2,880 readings/day99.97% uptimeReal-time sync

AI Health Analysis

Claude AI analyzes behavioral patterns, environmental correlations, and historical data to generate comprehensive health assessments.

Behavioral AIPattern detectionDaily reports
System Status

Always Monitoring.
Always Learning.

Our monitoring system runs 24/7, collecting environmental data and analyzing patterns to ensure optimal habitat conditions. The AI model continuously improves its predictions based on new observations.

Temperature Regulation99.2%
Humidity Control98.7%
Water Quality97.9%
pepe-monitor v2.4.1
▌

About the Research

This project explores the intersection of IoT technology, artificial intelligence, and amphibian welfare science. By creating a transparent, data-driven approach to habitat monitoring, we aim to advance understanding of optimal environmental conditions for captive Litoria caerulea.

🔬Research Goals

  • Establish baseline environmental parameters for optimal frog health
  • Develop predictive models for health anomaly detection
  • Create open-source tools for amphibian habitat monitoring
  • Contribute to amphibian conservation knowledge base

📊Open Data

All collected sensor data and AI analysis reports are publicly accessible. We believe in transparent science and encourage other researchers to utilize our datasets for their own studies.

Sensor LogsHealth ReportsAPI AccessRaw Data
Questions

FAQ

Common questions about the research project

What species is being studied?
We're studying a White's Tree Frog (Litoria caerulea), a species native to Australia and New Guinea known for their docile nature and adaptability to captive environments. They're an excellent model organism for environmental monitoring research.
What sensors are used?
Our sensor array includes: DHT22 for temperature/humidity, DS18B20 waterproof temperature probe, Atlas Scientific pH sensor, TCS34725 color/light sensor for UV monitoring, and custom water quality probes measuring TDS and conductivity.
How does the AI health analysis work?
We use Claude AI to analyze patterns in environmental data, correlate them with observed behaviors, and generate daily health assessments. The model considers temperature cycles, humidity patterns, activity levels, and historical baselines to identify potential concerns.
Is the data publicly available?
Yes! All sensor data is logged and accessible via our public API. We provide both real-time endpoints and historical data exports. Check our GitHub repository for API documentation and example scripts.
How can I contribute to the research?
We welcome contributions! You can: analyze our open datasets, suggest improvements to our monitoring methodology, help develop our open-source tools, or set up similar monitoring systems for your own amphibian habitats.
What are the optimal conditions for this species?
Based on our data: Temperature 22-28°C (with a gradient), humidity 50-80%, pH 6.5-7.5 for water features, and UVB exposure of 2-5 UVI for 10-12 hours daily. Our system maintains these ranges automatically.
🐸

Ready to Explore the Data?

Access real-time environmental metrics, historical data, and AI-generated health reports from our monitoring system.

Open Dashboard→View Source
📡Real-time Data
🔬Open Science
🤖AI-Powered Analysis