基于加速度数据与行为标注，构建了120只绵羊的放牧行为数据集。
EweAcT: Ewe behaviour aligned to accelerometer data for activity monitoring in extensive grazing systems
- 用颈圈加速度计采集绵羊放牧数据，结合视频人工标注行为。
- 包含79小时三轴数据，覆盖采食、反刍、休息等5类主要行为。
- 适合研究畜牧行为监测、动物福利评估及智能算法训练者使用。
在广域放牧系统中监测牲畜行为可为评估动物对环境扰动（如热浪、寄生虫、捕食者攻击）的适应能力提供关键信息。本研究通过佩戴在颈圈上的加速度计采集数据，并结合人工智能模型分析动物行为。为建立准确的行为预测模型，需大量与行为标注对齐的加速度数据，尤其在多样代表性的条件下。本数据集包含120只2021至2024年出生的罗曼绵羊的79小时三轴加速度数据，来自两个遗传系（低/高社交吸引力，低/高对人耐受性），在法国南部拉法日实验区（UEF, INRAE）280公顷草场上进行放牧。第一批次数据于2024年3月、6月和7月采集，涵盖坡地牧场与热浪期；每只羊佩戴专为幼羊设计的颈圈，分组在试验围栏内停留4至8小时，自由获取新鲜牧草和饮水，并由高空监控摄像头同步录像。行为标注使用BORIS软件，聚焦主要放牧行为：采食、反刍、休息、移动和“其他”（其余活动）。通过Python时间同步流程将标注与加速度序列对齐。第二批次数据于2025年11月采集，补充移动行为：羊只从圈舍区域沿小路步行至牧场（约10分钟），记录起止时间以对齐移动期间的加速度数据。最终数据集已合并，可供直接用于训练人工智能模型，实现从加速度数据中分类绵羊在放牧系统中的五大核心行为。
原文摘要 · Abstract (English)
Monitoring livestock behaviour under extensive conditions would provide valuable insights to assess animal adaption to environmental perturbations in agroecological systems (e.g., heat waves, parasitism, predator attacks). Animal behaviour can be monitored using accelerometer data collected from neck-collars combined with artificial intelligence models. However, large amounts of accelerometer data aligned with annotated behaviours are necessary to develop accurate models of behaviour prediction. In particular, developing reliable models for extensive systems requires data collected across a wide range of representative conditions. The dataset includes 79 hours of tri-axial accelerometer data aligned with behaviours manually annotated from video recordings for 120 Romane ewes born between 2021 and 2024. The ewes were derived from two divergent genetic lines after three and four generations of selection started 10 years ago: low and high social attractiveness, noted S-and S+, and low and high tolerance towards humans, noted H-and H+. They were reared under the extensive system applied to the Experimental Unit of La Fage (UEF, INRAE, Saint-Jean-et-saint Paul, Aveyron) where 250 sheep were reared exclusively outdoors on 280 hectares of rangeland in southern France. First batch of data was collected on March, June and July 2024 at the UEF under a range of extensive conditions, including sloping pastures and heat-wave periods. Ewes were equipped with accelerometer neck-collars specifically designed for young sheep on pasture. They were grouped on experimental paddocks for 4 to 8 hours and provided with fresh grass and ad libitum access to water. The animals were simultaneously video-recorded using an elevated CCTV camera. Behaviour annotation was carried out using Behavioral Observation Research Interactive Software focusing on the main behaviours on pasture: Grazing, Ruminating, Resting, Moving, and ''Other'', grouping all remaining activities. Annotations and corresponding accelerometer sequences were aligned using Python language, based on a time synchronization procedure. A second batch of data was acquired on November 2025 to supplement the dataset with the moving activity. For that purpose, ewes were equipped with the accelerometer collars and moved on tracks from the housing area to the pastures, corresponding to an approximately 10 minute-walk. The start and end times of the moves for each ewe were used to align the corresponding accelerometer data with the moving activity. These data were then merged with the dataset from the first batch. The resulting dataset is ready to use for applying artificial intelligence models to classify the 5 main behaviours of sheep under extensive grazing systems from accelerometer data.
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