ArticleScientific data2024
MSPB: a longitudinal multi-sensor dataset with phenotypic trait measurements from honey bees.
Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- When the hive speaks: detecting Varroa destructor infestation in honey bees through acoustic biomarkers and machine learning.Experimental & applied acarology · 2026Article
- Review
- Honey yield prediction and neonicotinoid risk assessment utilizing a machine learning framework in smart agriculture.Scientific reports · 2026Article
- Time-series dataset: fondant feeding in overwintering Apis mellifera colonies.BMC research notes · 2026Article
- Spectral Components of Honey Bee Sound Signals Recorded Inside and Outside the Beehive: An Explainable Machine Learning Approach to Diurnal Pattern Recognition.Sensors (Basel, Switzerland) · 2025Article
- UrBAN: Urban Beehive Acoustics and PheNotyping Dataset.Scientific data · 2025Article
- MSPB: a longitudinal multi-sensor dataset with phenotypic trait measurements from honey bees.Scientific data · 2024Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
We present a one-year-long multi-sensor dataset collected from honey bee colonies (Apis mellifera) with rich phenotypic measurements. Data were collected non-stop from April 2020 to April 2021 from 53 hives located at two apiaries in Québec, Canada. The sensor data included audio features, temperature, and relative humidity. The phenotypic measurements contained beehive population, number of brood cells (eggs, larva and pupa), Varroa destructor infestation levels, defensive and hygienic behaviors, honey yield, and winter mortality. Our study is amongst the first to combine a wide variety of phenotypic trait measurements annotated by apicultural science experts with multi-sensor data, which facilitate a broader scope of analysis. We first summarize the data collection procedure, sensor data pre-processing steps, and data composition. We then provide an overview of the phenotypic data distribution as well as a visualization of the sensor data patterns. Lastly, we showcase several hive monitoring applications based on sensor data analysis and machine learning, such as winter mortality prediction, hive population estimation, and the presence of an active and laying queen.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.