Abstract
Early detection of lambing is essential for improving animal welfare and farm management, as it enables timely intervention and reduces complications. Wearable inertial sensors have been applied to sheep monitoring, with frequent transitions between standing and lying identified as key behavioral indicators of lambing. However, unlike in larger livestock, no accelerometry-based system currently provides real-time detection for small ruminants, and existing studies remain limited to preliminary approaches. This study monitored 61 ewes using accelerometers sampling at 20 Hz, while lambing was simultaneously recorded on video to establish precise birth times for 113 events. Video analysis also documented litter size and the need for assistance. Data were organized per ewe, supplemented with information such as birth year, previous lambing records, and ultrasound results. A video of one birth was included to illustrate behavior during the process. The dataset provides a valuable foundation for developing algorithms capable of classifying birth-related behaviors, thereby supporting future automated lambing detection systems.
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Data availability
Data is available for download at figshare repository under the address https://doi.org/10.6084/m9.figshare.28815974.v1.
Code availability
Code for processing samples is available from the zenodo repository (https://doi.org/10.5281/zenodo.16902750).
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Acknowledgements
This work was supported by FCT - Fundação para a Ciência e Tecnologia, I.P. by project reference UIDB/50008, and DOI identifier 10.54499/UIDB/50008. The manuscript writing has also been supported by the EU project TechCare, which has received funding from the European Union’s Horizon 2020 Research and Innovation Program under grant agreement No. 862050. Authors would like to extend our sincere gratitude to Farmer Tom for his invaluable assistance in handling the sheep during the monitoring process. His expertise and dedication made a significant difference in ensuring the smooth and successful completion of the task. We truly appreciate his support and commitment to the project.
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Conceptualization, P.G., S.N. and G.J.; methodology, P.G., S.N. and G.J.; software, P.G. and G.C.; validation, P.G., S.N. and G.J.; formal analysis, P.G. and S.N.; investigation, P.G., S.N. and G.J.; re-sources, G.C. and G.J.; data curation, P.G. and G.C.; writing—original draft preparation, P.G.; writing—review and editing, P.G., S.N. and G.J.; visualization, P.G., S.N. and G.J. All authors have read and agreed to the published version of the manuscript.
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Goncalves, P., Nyamuryekung’e, S., Corrente, G. et al. Tjotta accelerometer monitored lambing dataset. Sci Data (2026). https://doi.org/10.1038/s41597-026-06660-2
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DOI: https://doi.org/10.1038/s41597-026-06660-2
