{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,8]],"date-time":"2026-11-08T03:27:48Z","timestamp":1794108468759,"version":"4.8.0"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T00:00:00Z","timestamp":1764892800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T00:00:00Z","timestamp":1767657600000},"content-version":"vor","delay-in-days":32,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Data"],"DOI":"10.1038\/s41597-025-06326-5","type":"journal-article","created":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T10:07:05Z","timestamp":1764929225000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["BECA: A Computer Vision Dataset for Long-Term Recognition In Beef Cattle"],"prefix":"10.1038","volume":"13","author":[{"given":"Yuqi","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-1378-2070","authenticated-orcid":false,"given":"Longxiang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunyang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sen","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yue","family":"Rong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kai","family":"Niu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiqiang","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,12,5]]},"reference":[{"key":"6326_CR1","doi-asserted-by":"crossref","unstructured":"EFSA Panel on Animal Health and Welfare (AHAW). Scientific Opinion on the welfare of cattle kept for beef production and the welfare in intensive calf farming systems. EFSA Journal 10(5), 2669 (2012).","DOI":"10.2903\/j.efsa.2012.2669"},{"key":"6326_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1139\/cjas-2020-0022","volume":"101","author":"SA Terry","year":"2020","unstructured":"Terry, S. A., Basarab, J. A., Guan, L. L. & McAllister, T. A. Strategies to improve the efficiency of beef cattle production. Canadian Journal of Animal Science 101, 1\u201319 (2020).","journal-title":"Canadian Journal of Animal Science"},{"key":"6326_CR3","doi-asserted-by":"publisher","first-page":"102047","DOI":"10.1016\/j.adhoc.2019.102047","volume":"98","author":"RS Alonso","year":"2020","unstructured":"Alonso, R. S., Sitt\u00f3n-Candanedo, I., Garc\u00eda, \u00d3., Prieto, J. & Rodr\u00edguez-Gonz\u00e1lez, S. An intelligent Edge-IoT platform for monitoring livestock and crops in a dairy farming scenario. Ad Hoc Networks 98, 102047 (2020).","journal-title":"Ad Hoc Networks"},{"key":"6326_CR4","doi-asserted-by":"crossref","unstructured":"Nleya, S. M., & Ndlovu, S. Smart dairy farming overview: innovation, algorithms and challenges. Smart Agriculture Automation Using Advanced Technologies: Data Analytics and Machine Learning, Cloud Architecture, Automation and IoT, 35-59. (2021).","DOI":"10.1007\/978-981-16-6124-2_3"},{"key":"6326_CR5","doi-asserted-by":"publisher","first-page":"4242805","DOI":"10.1155\/2020\/4242805","volume":"2020","author":"MO Akbar","year":"2020","unstructured":"Akbar, M. O. et al. IoT for development of smart dairy farming. Journal of Food Quality 2020, 4242805 (2020).","journal-title":"Journal of Food Quality"},{"key":"6326_CR6","doi-asserted-by":"crossref","unstructured":"GB, A. K. et al.Design and Development of Intelligent System for Dairy Farming. In Proceedings of the 2021 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT) (pp. 960\u2013964). IEEE. (2021).","DOI":"10.1109\/RTEICT52294.2021.9573538"},{"key":"6326_CR7","doi-asserted-by":"publisher","first-page":"107945","DOI":"10.1016\/j.compag.2023.107945","volume":"211","author":"Q Fan","year":"2023","unstructured":"Fan, Q., Liu, S., Li, S. & Zhao, C. Bottom-up cattle pose estimation via concise multi-branch network. Computers and Electronics in Agriculture 211, 107945 (2023).","journal-title":"Computers and Electronics in Agriculture"},{"key":"6326_CR8","unstructured":"Khanam, R. & Hussain, M. Yolov11: An overview of the key architectural enhancements. arXiv preprint arXiv:2410.17725. (2024)."},{"key":"6326_CR9","doi-asserted-by":"publisher","first-page":"10727","DOI":"10.3168\/jds.2020-20002","volume":"104","author":"NA Carpinelli","year":"2021","unstructured":"Carpinelli, N. A. et al. Effects of peripartal yeast culture supplementation on lactation performance, blood biomarkers, rumen fermentation, and rumen bacteria species in dairy cows. Journal of Dairy Science 104, 10727\u201310743 (2021).","journal-title":"Journal of Dairy Science"},{"key":"6326_CR10","doi-asserted-by":"crossref","unstructured":"Dos Santos Daltro, D. et al. Heterosis in the lactation curves of Girolando cows with emphasis on variations of the individual curves. Journal of Applied Animal Research (2019).","DOI":"10.1080\/09712119.2019.1575223"},{"key":"6326_CR11","doi-asserted-by":"publisher","first-page":"815915","DOI":"10.3389\/fmicb.2022.815915","volume":"13","author":"Y Zheng","year":"2022","unstructured":"Zheng, Y. et al. Metagenomic and transcriptomic analyses reveal the differences and associations between the gut microbiome and muscular genes in Angus and Chinese Simmental cattle. Frontiers in Microbiology 13, 815915 (2022).","journal-title":"Frontiers in Microbiology"},{"key":"6326_CR12","first-page":"148","volume":"6","author":"AY Isyanto","year":"2015","unstructured":"Isyanto, A. Y. & Dehen, Y. A. Sustainability analysis of beef cattle fattening in Ciamis regency, West Java Province, Indonesia. Journal of Economics and Sustainable Development 6, 148\u2013154 (2015).","journal-title":"Journal of Economics and Sustainable Development"},{"key":"6326_CR13","unstructured":"Wen-jun, Z. Several Key Measures on Improving Beef Cattle Fattening Effect. China Cattle Science. (2011)."},{"key":"6326_CR14","doi-asserted-by":"publisher","first-page":"109","DOI":"10.2527\/jas.2011-4245","volume":"90","author":"DP Berry","year":"2012","unstructured":"Berry, D. P. & Crowley, J. J. Residual intake and body weight gain: a new measure of efficiency in growing cattle. Journal of Animal Science 90, 109\u2013115 (2012).","journal-title":"Journal of Animal Science"},{"key":"6326_CR15","doi-asserted-by":"crossref","unstructured":"Hales, K. E., Galyean, M., & Smith, Z. K. Evaluating the Difference between Formulated Dietary Net Energy Values and Net Energy Values Determined from Growth Performance and Estimates of Shrunk Body Weight Gain in Finishing Beef Cattle. Journal of Animal Science, 100. (2022).","DOI":"10.1093\/jas\/skac247.077"},{"key":"6326_CR16","doi-asserted-by":"publisher","first-page":"106871","DOI":"10.1016\/j.compag.2022.106871","volume":"196","author":"Z Weng","year":"2022","unstructured":"Weng, Z., Meng, F., Liu, S., Zhang, Y., Zheng, Z. & Gong, C. Cattle face recognition based on a Two-Branch convolutional neural network. Computers and Electronics in Agriculture 196, 106871 (2022).","journal-title":"Computers and Electronics in Agriculture"},{"key":"6326_CR17","doi-asserted-by":"publisher","first-page":"106848","DOI":"10.1016\/j.compag.2022.106848","volume":"195","author":"Z Li","year":"2022","unstructured":"Li, Z., Lei, X. & Liu, S. A lightweight deep learning model for cattle face recognition. Computers and Electronics in Agriculture 195, 106848 (2022).","journal-title":"Computers and Electronics in Agriculture"},{"key":"6326_CR18","doi-asserted-by":"publisher","first-page":"107579","DOI":"10.1016\/j.compag.2022.107579","volume":"204","author":"Y Qiao","year":"2023","unstructured":"Qiao, Y., Guo, Y. & He, D. Cattle body detection based on YOLOv5-ASFF for precision livestock farming. Computers and Electronics in Agriculture 204, 107579 (2023).","journal-title":"Computers and Electronics in Agriculture"},{"key":"6326_CR19","doi-asserted-by":"publisher","first-page":"108006","DOI":"10.1016\/j.compag.2023.108006","volume":"211","author":"W Yang","year":"2023","unstructured":"Yang, W. et al. Deformable convolution and coordinate attention for fast cattle detection. Computers and Electronics in Agriculture 211, 108006, https:\/\/doi.org\/10.1016\/j.compag.2023.108006 (2023).","journal-title":"Computers and Electronics in Agriculture"},{"key":"6326_CR20","doi-asserted-by":"publisher","first-page":"108044","DOI":"10.1016\/j.compag.2023.108044","volume":"212","author":"S Han","year":"2023","unstructured":"Han, S. et al. Deep learning-based multi-cattle tracking in crowded livestock farming using video. Computers and Electronics in Agriculture 212, 108044 (2023).","journal-title":"Computers and Electronics in Agriculture"},{"key":"6326_CR21","doi-asserted-by":"publisher","first-page":"106133","DOI":"10.1016\/j.compag.2021.106133","volume":"185","author":"W Andrew","year":"2021","unstructured":"Andrew, W. et al. Visual identification of individual Holstein-Friesian cattle via deep metric learning. Computers and Electronics in Agriculture 185, 106133 (2021).","journal-title":"Computers and Electronics in Agriculture"},{"key":"6326_CR22","unstructured":"Gao, J., Burghardt, T., Andrew, W., Dowsey, A. W. & Campbell, N. W.Towards Self-Supervision for Video Identification of Individual Holstein-Friesian Cattle: The Cows2021 Dataset. In Proceedings of the Conference on Computer Vision and Pattern Recognition Workshop on Computer Vision for Animal Behavior Tracking and Modeling (CV4Animals) (2021)."},{"key":"6326_CR23","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1016\/j.ifacol.2019.12.558","volume":"52","author":"Y Qiao","year":"2019","unstructured":"Qiao, Y. et al. Individual cattle identification using a deep learning based framework. IFAC-PapersOnLine 52, 318\u2013323 (2019).","journal-title":"IFAC-PapersOnLine"},{"key":"6326_CR24","doi-asserted-by":"publisher","unstructured":"Xu, X. et al. Few-shot cow identification via meta-learning. Information Processing in Agriculture. https:\/\/doi.org\/10.1016\/j.inpa.2024.04.001 (2024).","DOI":"10.1016\/j.inpa.2024.04.001"},{"key":"6326_CR25","doi-asserted-by":"publisher","first-page":"120365","DOI":"10.1016\/j.eswa.2023.120365","volume":"228","author":"Y Lu","year":"2023","unstructured":"Lu, Y., Weng, Z., Zheng, Z., Zhang, Y. & Gong, C. Algorithm for cattle identification based on locating key area. Expert Systems with Applications 228, 120365, https:\/\/doi.org\/10.1016\/j.eswa.2023.120365 (2023).","journal-title":"Expert Systems with Applications"},{"key":"6326_CR26","doi-asserted-by":"publisher","first-page":"108703","DOI":"10.1016\/j.compag.2024.108703","volume":"218","author":"Z Xu","year":"2024","unstructured":"Xu, Z., Zhao, Y., Yin, Z. & Yu, Q. Optimized BottleNet Transformer model with Graph Sampling and Counterfactual Attention for cow individual identification. Computers and Electronics in Agriculture 218, 108703 (2024).","journal-title":"Computers and Electronics in Agriculture"},{"key":"6326_CR27","doi-asserted-by":"publisher","unstructured":"Yang, L., Xu, X., Zhao, J. & Song, H. Fusion of RetinaFace and improved FaceNet for individual cow identification in natural scenes. Information Processing in Agriculture. https:\/\/doi.org\/10.1016\/j.inpa.2023.09.001 (2023).","DOI":"10.1016\/j.inpa.2023.09.001"},{"key":"6326_CR28","unstructured":"Myat Noe, S., Zin, T. T., Kobayashi, I. and Tin, P. Optimizing black cattle tracking in complex open ranch environments using YOLOv8 embedded multi-camera system. Scientific Reports,"},{"key":"6326_CR29","doi-asserted-by":"publisher","first-page":"216","DOI":"10.1109\/OJIES.2025.3533663","volume":"6","author":"WHE Mg","year":"2025","unstructured":"Mg, W. H. E. Automated cattle monitoring system for calving time prediction using trajectory data embedded time series analysis. IEEE Open Journal of the Industrial Electronics Society 6, 216\u2013234, https:\/\/doi.org\/10.1109\/OJIES.2025.3533663 (2025).","journal-title":"IEEE Open Journal of the Industrial Electronics Society"},{"key":"6326_CR30","doi-asserted-by":"crossref","unstructured":"Zhou, K., Yang, Y., Cavallaro, A. & Xiang, T. Omni-scale feature learning for person re-identification. In Proceedings of the IEEE\/CVF International Conference on Computer Vision (pp. 3702\u20133712). (2019).","DOI":"10.1109\/ICCV.2019.00380"},{"key":"6326_CR31","doi-asserted-by":"publisher","unstructured":"Zhang, Y. et al. BECA: A Dataset for Long-Term Recognition In Beef Cattle. figshare https:\/\/doi.org\/10.6084\/m9.figshare.29425316 (2025).","DOI":"10.6084\/m9.figshare.29425316"},{"key":"6326_CR32","doi-asserted-by":"crossref","unstructured":"Lin, T. Y. et al. Microsoft COCO: Common Objects in Context. In Computer Vision - ECCV 2014 (pp. 740\u2013755). (2014).","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"6326_CR33","unstructured":"Neukum, G., Jaumann, R. HRSC: The high resolution stereo camera of Mars Express. In: Mars Express: the scientific payload. Ed. by Andrew Wilson, scientific coordination: Agustin Chicarro. ESA SP-1240, Noordwijk, Netherlands: ESA Publications Division, 1240, 17-35. (2004)."},{"key":"6326_CR34","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1016\/j.neucom.2020.10.081","volume":"429","author":"M Wang","year":"2021","unstructured":"Wang, M. & Deng, W. Deep face recognition: A survey. Neurocomputing 429, 215\u2013244 (2021).","journal-title":"Neurocomputing"},{"key":"6326_CR35","doi-asserted-by":"publisher","unstructured":"Dutta, A. & Zisserman, A. The VIA Annotation Software for Images, Audio and Video. In Proceedings of the 27th ACM International Conference on Multimedia (pp. 2276\u20132279). New York, NY, USA: Association for Computing Machinery. https:\/\/doi.org\/10.1145\/3343031.3350535 (2019).","DOI":"10.1145\/3343031.3350535"},{"key":"6326_CR36","doi-asserted-by":"publisher","first-page":"108572","DOI":"10.1016\/j.compag.2023.108572","volume":"217","author":"R Garc\u00eda","year":"2024","unstructured":"Garc\u00eda, R. & Aguilar, J. A meta-learning approach in a cattle weight identification system for anomaly detection. Computers and Electronics in Agriculture 217, 108572, https:\/\/doi.org\/10.1016\/j.compag.2023.108572 (2024).","journal-title":"Computers and Electronics in Agriculture"},{"key":"6326_CR37","doi-asserted-by":"publisher","first-page":"100489","DOI":"10.1016\/j.atech.2024.100489","volume":"8","author":"A de Oliveira Bezerra","year":"2024","unstructured":"De Oliveira Bezerra, A. et al. Exploring cluster analysis in Nelore cattle visual score attribution. Smart Agricultural Technology 8, 100489 (2024).","journal-title":"Smart Agricultural Technology"},{"key":"6326_CR38","doi-asserted-by":"crossref","unstructured":"Xie, S., Girshick, R., Doll\u00e1r, P., Tu, Z. & He, K. Aggregated residual transformations for deep neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1492\u20131500) (2017).","DOI":"10.1109\/CVPR.2017.634"},{"key":"6326_CR39","unstructured":"Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning (pp. 6105\u20136114)."},{"key":"6326_CR40","doi-asserted-by":"crossref","unstructured":"Tu, Z. et al. Maxvit: Multi-axis vision transformer. In European conference on computer vision (pp. 459\u2013479) (2022).","DOI":"10.1007\/978-3-031-20053-3_27"}],"container-title":["Scientific Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41597-025-06326-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41597-025-06326-5","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41597-025-06326-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T04:39:49Z","timestamp":1767760789000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41597-025-06326-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,5]]},"references-count":40,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["6326"],"URL":"https:\/\/doi.org\/10.1038\/s41597-025-06326-5","relation":{},"ISSN":["2052-4463"],"issn-type":[{"value":"2052-4463","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,5]]},"assertion":[{"value":"2 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 November 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 December 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"7"}}