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Predictive Bee Colony Health Dataset
Synthetic Biology & Genetic Engineering
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Free
About
Specifically tailored to assist in monitoring and forecasting the health status of honeybee colonies. It combines rigorous colony inspection records, quantified by the Healthy Colony Checklist protocol, with concurrent hive weight data for enhanced analysis. The inclusion of external weather patterns allows researchers to explore the interrelationship between environmental conditions and internal hive dynamics, supporting the creation of predictive models for apiary management.
Columns
The sample data structure includes location identifiers critical for geographical analysis:
- ApiaryID: A unique numerical identifier assigned to each apiary location.
- Apiary: The name or identifier of the specific apiary location.
- City: The municipality where the apiary is situated, such as Durham or Salt Lake City.
- State: The United States state where the apiary is located (NC or UT).
Distribution
The source data is often presented in a CSV format. The sample file, Apiary_Information.csv, is quite small at 374 B. The underlying dataset tracks metrics across 13 unique Apiary IDs. The data product is static and is not expected to receive future updates.
Usage
This dataset is suitable for developing and testing ecological modelling techniques, particularly those focusing on animal colony health. It can be used to build machine learning models to forecast colony health based on combined hive metrics and meteorological conditions. Researchers can also analyse the impact of localised weather on beekeeping outcomes and weight fluctuations.
Coverage
The geographical scope covers apiaries located in two distinct regions of the United States: North Carolina (NC) and Utah (UT). North Carolina locations account for the majority of the apiary records, with Durham being the most frequent city. Utah apiaries are represented primarily by Salt Lake City data. The dataset does not currently offer details on the time range.
License
CC0: Public Domain
Who Can Use It
- Ecological Modellers: For creating predictive algorithms concerning environmental impacts on animal populations.
- Apiary Scientists and Biologists: To study the effectiveness of the Healthy Colony Checklist protocol and its correlation with hive weight dynamics.
- Environmental Data Scientists: To integrate weather variables into biological forecasting models.
Dataset Name Suggestions
- Apiary Intelligence and Weather Metrics
- Honeybee Health Prediction Data
- Predictive Bee Colony Health Dataset
- Hive Metrics and HCC Data
Attributes
Original Data Source: Predictive Bee Colony Health Dataset
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