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We present a longitudinal multi-sensor dataset collected from honey beecolonies (Apis mellifera) with rich phenotypic measurements. Data werecontinuously collected between May-2020 and April-2021 from 53 hives located attwo apiaries in Qu\'ebec, Canada. The sensor data included audio features,temperature, and relative humidity. The phenotypic measurements containedbeehive population, number of brood cells (eggs, larva and pupa), Varroadestructor infestation levels, defensive and hygienic behaviors, honey yield,and winter mortality. Our study is amongst the first to provide a wide varietyof phenotypic trait measurements annotated by apicultural science experts,which facilitate a broader scope of analysis. We first summarize the datacollection procedure, sensor data pre-processing steps, and data composition.We then provide an overview of the phenotypic data distribution as well as avisualization of the sensor data patterns. Lastly, we showcase several hivemonitoring applications based on sensor data analysis and machine learning,such as winter mortality prediction, hive population estimation, and thepresence of an active and laying queen.

MSPB: a longitudinal multi-sensor dataset with phenotypic trait measurements from honey bees