Work

Time-series dataset of honey bee colony dynamics before, during, and after sunflower pollination

Year: 2026

Type: article

Abstract: OBJECTIVES: Precision beekeeping is an integral part of precision agriculture, which relies on sensor technologies and high-quality datasets to quantify and optimize ecosystem services such as crop pollination. To support reproducible research and the planning and evaluation of crop pollination campaigns in precision beekeeping, we release a time-series dataset that characterizes colony dynamics before, during, and after pollination, using sunflower as a case study. DATA DESCRIPTION: We release synchronized, non-invasive time series from nine smart hives (Apis mellifera) monitored in Ukraine (Europe/Kyiv) from 01 May to 31 Aug 2024, including a sunflower pollination service window (07-23 Jul 2024) and a documented attractant intervention. Sensors record hive weight, in-hive and ambient temperature, in-hive and ambient relative humidity, and device signals (processor temperature and stabilized solar voltage). The repository includes raw telemetry exports, cleaned hourly series aligned to a fixed local time grid, and a beekeeper event log, together with reproducible scripts and a documented processing protocol.

Source: BMC Research Notes

DOI: 10.1186/s13104-026-07812-8

Authors: Igor Kurdin, Aleksandra Kurdina

Institution: Warsaw University of Life Sciences

Language: English

Topic: Insect and Pesticide Research

Subfield: Insect Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDG: Zero hunger

Open Access status: gold

Estimated APC paid: $1,690