Abdollahi Mahsa, Zhu Yi, Guimarães Heitor R, Coallier Nico, Maucourt Ségolène, Giovenazzo Pierre, Falk Tiago H
INRS-EMT, Université du Québec, Montréal, Canada.
Nectar Technologies Inc., Montréal, Canada.
Sci Data. 2025 Mar 31;12(1):536. doi: 10.1038/s41597-025-04869-1.
In this paper, we present a multimodal dataset obtained from a honey bee colony in Montréal, Quebec, Canada, spanning the years of 2021 to 2022. This apiary comprised 10 beehives, with microphones recording more than 3000 hours of high quality raw audio, and also sensors capturing temperature, and humidity. Periodic hive inspections involved monitoring colony honey bee population changes, assessing queen-related conditions, and documenting overall hive health. Additionally, health metrics, such as Varroa mite infestation rates and winter mortality assessments were recorded, offering valuable insights into factors affecting hive health status and resilience. In this study, we first outline the data collection process, sensor data description, and dataset structure. Furthermore, we demonstrate a practical application of this dataset by extracting various features from the raw audio to predict colony population using the number of frames of bees as a proxy.
在本文中，我们展示了一个从加拿大魁北克省蒙特利尔的一个蜂群获取的多模态数据集，时间跨度为2021年至2022年。这个养蜂场由10个蜂箱组成，麦克风记录了超过3000小时的高质量原始音频，还有传感器记录温度和湿度。定期的蜂箱检查包括监测蜂群中蜜蜂数量的变化、评估与蜂王相关的状况以及记录蜂箱的整体健康状况。此外，还记录了诸如瓦螨感染率和冬季死亡率评估等健康指标，为影响蜂箱健康状况和恢复力的因素提供了有价值的见解。在本研究中，我们首先概述数据收集过程、传感器数据描述和数据集结构。此外，我们通过从原始音频中提取各种特征，以蜜蜂的帧数作为代理来预测蜂群数量，展示了该数据集的实际应用。
