Time-series dataset of non-invasive IoT measurements in Apis mellifera colonies: honey robbing phenomenon during autumn (2023; Ukraine)

This dataset provides homogeneous time-series IoT telemetry from an apiary of 10 identical AmoHive smart hives in Kyiv, Ukraine, documenting the prerequisites, onset, and progression of a confirmed honey robbing event in one colony during early autumn 2023. The dataset covers the period 2023-09-06 to 2023-09-27 (Europe/Kyiv). The dataset contains RAW device telemetry and CLEAN standardized time series (hourly HH:15 local grid) with explicit per-variable provenance, imputation labels, and quality control metrics. Measured parameters include hive weight, internal and external temperature, internal and external relative humidity, processor temperature, battery voltage level, and solar panel output voltage. The archive additionally includes a robbing event log, QC summary tables, a detailed data dictionary, and a fully reproducible the RAW to CLEAN processing pipeline. This dataset is intended to support research on honey bee behaviour under robbing stress, ML/AI-based anomaly detection, ecological IoT monitoring, data quality and reconstruction studies, as well as modelling and machine learning applications in precision beekeeping.