Work

Data paper: A goat behaviour dataset combining labelled behaviours and accelerometer data for training Machine Learning detection models

Year: 2025

Type: article

Abstract: This paper presents a dataset of accelerometer data and corresponding video-annotated behaviours from eight indoor dairy Alpine goats. Animals were equipped with 3D-accelerometers attached to their ears for 24 consecutive hours and recorded at a frequency of 5 Hz. Video recordings for this period were also obtained. Activities associated with positional, feeding and social behaviours were annotated over two daylight periods, for a total of 11 hours per goat, by a trained observer assuring high precision and consistency. This dataset can be used independently or complement an existing dataset for training supervised Machine Learning models for the detection of goat behaviour. It contributes to improving the robustness of such models by incorporating behavioural signals specific to indoor-housed goats.

Source: Animal - Open Space

DOI: 10.1016/j.anopes.2025.100095

Authors: S. Mauny, J. Kwon, N.C. Friggens, C. Duvaux-Ponter, M. Taghipoor

Institutions: AgroParisTech, Université Paris-Saclay, Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement, Modélisation Systémique Appliquée aux Ruminants, Mathématiques et Informatique Appliquées

Language: English

Topic: Animal Behavior and Welfare Studies

Subfield: Small Animals

Field: Veterinary

Domain: Health Sciences

SDG: Zero hunger

Open Access status: hybrid

Estimated APC paid: $1,530