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UrBAN: Urban Beehive Acoustics and PheNotyping Dataset.
Mahsa Abdollahi1, Yi Zhu1, Heitor R Guimarães1
1INRS-EMT, Université du Québec, Montréal, Canada.
Scientific Data
|March 31, 2025
Summary
This study introduces a multimodal dataset from 10 honey bee colonies in Montréal, recording over 3000 hours of audio and environmental data. The data aids in predicting honey bee population dynamics using audio analysis.
Area of Science:
- Ecology
- Animal Behavior
- Data Science
Background:
- Honey bee populations face numerous threats, impacting agriculture and ecosystems.
- Monitoring hive health and population dynamics is crucial for conservation efforts.
- Multimodal data offers a comprehensive approach to understanding colony behavior.
Purpose of the Study:
- To present a novel multimodal dataset for honey bee colony research.
- To detail the data collection, sensor information, and dataset structure.
- To demonstrate the utility of the dataset in predicting colony population.
Main Methods:
- Collected over 3000 hours of high-quality audio data from 10 beehives.
- Integrated sensor data including temperature and humidity.
- Performed periodic hive inspections to record population, queen status, and health metrics (e.g., Varroa mites).
Main Results:
- A comprehensive multimodal dataset spanning 2021-2022 was established.
- Audio features were extracted to predict colony population size.
- The dataset provides insights into factors influencing hive health and resilience.
Conclusions:
- The presented multimodal dataset is a valuable resource for honey bee research.
- Audio analysis shows promise for non-invasive monitoring of honey bee populations.
- This data can support strategies for improving honey bee colony health and sustainability.
