  In this paper, we present a multimodal dataset obtained from a honey bee
colony in Montr\'eal, Quebec, Canada, spanning the years of 2021 to 2022. This
apiary comprised 10 beehives, with microphones recording more than 2000 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.
