{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T06:04:22Z","timestamp":1785305062480,"version":"3.55.0"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T00:00:00Z","timestamp":1781481600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T00:00:00Z","timestamp":1781481600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["390523135"],"award-info":[{"award-number":["390523135"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Vis"],"published-print":{"date-parts":[[2026,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Machine learning and computer vision methods have a major impact on the study of natural animal behavior, as they enable the (semi-) automatic analysis of vast amounts of video data. Such large-scale analysis is essential not only to mere behavioral research but its applicability spans across a large range of disciplines. Mice are the standard mammalian model system in most behavioral research fields, but the datasets available today to refine such methods focus either on isolated or social behaviors. In contrast, datasets in which animals interact with a physical apparatus, which is highly relevant across disciplines that study learning, are as of yet unavailable. In this work, we present a video dataset of individual mice solving (multi-step) mechanical puzzles, so-called lockboxes. The more than 110\u00a0hours of total playtime show their behavior recorded from three different perspectives. As a benchmark for frame-level action classification methods, we provide human-annotated labels for all videos of two different mice, equaling 13% of our dataset. Our action labels provide two levels of complexity, requiring the classification of both what the mouse is doing as well as which object is targeted. Additionally, as an initial comparison against the human-annotated labels, we used two different keypoint\u00a0(pose) tracking-based action classification frameworks as well as an autoencoder-based framework, which illustrates the challenges of automated labeling of fine-grained behaviors, such as the manipulation of objects. We hope that our work will help accelerate the advancement of automated action and behavior classification in a task-driven environment. Our dataset, including videos and human-annotated labels, is publicly available at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/doi.org\/10.14279\/depositonce-23850\" ext-link-type=\"uri\">https:\/\/doi.org\/10.14279\/depositonce-23850<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1007\/s11263-026-02908-x","type":"journal-article","created":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T08:59:11Z","timestamp":1781513951000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Mouse Lockbox Dataset: Behavior Recognition for Mice Solving Lockboxes"],"prefix":"10.1007","volume":"134","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9442-2532","authenticated-orcid":false,"given":"Patrik","family":"Reiske","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9288-1292","authenticated-orcid":false,"given":"Marcus N.","family":"Boon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3596-0795","authenticated-orcid":false,"given":"Niek","family":"Andresen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-8101-8669","authenticated-orcid":false,"given":"Sole","family":"Traverso","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-5949-3121","authenticated-orcid":false,"given":"Marieatou","family":"Daniels","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6681-9367","authenticated-orcid":false,"given":"Katharina","family":"Hohlbaum","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0202-4351","authenticated-orcid":false,"given":"Lars","family":"Lewejohann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0782-2755","authenticated-orcid":false,"given":"Christa","family":"Th\u00f6ne-Reineke","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2871-9266","authenticated-orcid":false,"given":"Olaf","family":"Hellwich","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0690-3553","authenticated-orcid":false,"given":"Henning","family":"Sprekeler","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,15]]},"reference":[{"key":"2908_CR1","doi-asserted-by":"publisher","first-page":"13665","DOI":"10.1038\/s41598-020-70688-6","volume":"10","author":"A Alameer","year":"2020","unstructured":"Alameer, A., Kyriazakis, I., & Bacardit, J. (2020). Automated recognition of postures and drinking behaviour for the detection of compromised health in pigs. Scientific Reports, 10, 13665. https:\/\/doi.org\/10.1038\/s41598-020-70688-6","journal-title":"Scientific Reports"},{"issue":"1","key":"2908_CR2","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.neuron.2014.09.005","volume":"84","author":"DJ Anderson","year":"2014","unstructured":"Anderson, D. J., & Perona, P. (2014). Toward a Science of Computational Ethology. Neuron, 84(1), 18\u201331. https:\/\/doi.org\/10.1016\/j.neuron.2014.09.005","journal-title":"Neuron"},{"key":"2908_CR3","unstructured":"Batty, E., Whiteway, M., Saxena, S., Biderman, D., Abe, T., Musall, S., & Paninski, L. (2019). BehaveNet: nonlinear embedding and Bayesian neural decoding of behavioral videos. Advances in Neural Information Processing Systems, 32,"},{"key":"2908_CR4","doi-asserted-by":"crossref","unstructured":"Baum, M., Schattenhofer, L., R\u00f6ssler, T., Osuna-Mascar\u00f3, A., Auersperg, A., Kacelnik, A., & Brock, O. (2022). Yoking-Based Identification of Learning Behavior in Artificial and Biological Agents. From Animals to Animats, 16, 67\u201378.","DOI":"10.1007\/978-3-031-16770-6_6"},{"key":"2908_CR5","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-024-02319-1","volume-title":"Lightning Pose: improved animal pose estimation via semi-supervised learning","author":"D Biderman","year":"2024","unstructured":"Biderman, D., Whiteway, M. R., Hurwitz, C., Greenspan, N., Lee, R. S., Vishnubhotla, A., & Paninski, L. (2024). Lightning Pose: improved animal pose estimation via semi-supervised learning. Bayesian ensembling and cloud-native open-source tools: Nature Methods. https:\/\/doi.org\/10.1038\/s41592-024-02319-1"},{"key":"2908_CR6","doi-asserted-by":"publisher","unstructured":"Bohnslav, J.P., Wimalasena, N.K., Clausing, K.J., Dai, Y.Y., Yarmolinsky, D.A., Cruz, T., & Harvey, C.D. (2021). DeepEthogram, a machine learning pipeline for supervised behavior classification from raw pixels. eLife, 10, e63377, https:\/\/doi.org\/10.7554\/eLife.63377","DOI":"10.7554\/eLife.63377"},{"key":"2908_CR7","doi-asserted-by":"publisher","DOI":"10.1101\/2024.07.29.605658","author":"MN Boon","year":"2025","unstructured":"Boon, M. N., Andresen, N., Traverso, S., Meier, S., Schuessler, F., Hellwich, O., & Hohlbaum, K. (2025). Mechanical problem solving in mice. bioRxiv. https:\/\/doi.org\/10.1101\/2024.07.29.605658","journal-title":"Mechanical problem solving in mice. bioRxiv"},{"key":"2908_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.neubiorev.2023.105243","volume":"151","author":"J Bordes","year":"2023","unstructured":"Bordes, J., Miranda, L., M\u00fcller-Myhsok, B., & Schmidt, M. V. (2023). Advancing social behavioral neuroscience by integrating ethology and comparative psychology methods through machine learning. Neuroscience & Biobehavioral Reviews, 151, Article 105243.","journal-title":"Neuroscience & Biobehavioral Reviews"},{"key":"2908_CR9","doi-asserted-by":"publisher","first-page":"495","DOI":"10.1038\/s42256-021-00326-x","volume":"3","author":"B Brattoli","year":"2021","unstructured":"Brattoli, B., B\u00fcchler, U., Dorkenwald, M., Reiser, P., Filli, L., Helmchen, F., & Ommer, B. (2021). Unsupervised behaviour analysis and magnification (uBAM) using deep learning. Nature Machine Intelligence, 3, 495\u2013506. https:\/\/doi.org\/10.1038\/s42256-021-00326-x","journal-title":"Nature Machine Intelligence"},{"issue":"8","key":"2908_CR10","doi-asserted-by":"publisher","first-page":"3086","DOI":"10.1007\/s11263-024-02003-z","volume":"132","author":"O Brookes","year":"2024","unstructured":"Brookes, O., Mirmehdi, M., Stephens, C., Angedakin, S., Corogenes, K., Dowd, D., & Burghardt, T. (2024). PanAf20K: A Large Video Dataset for Wild Ape Detection and Behaviour Recognition. International Journal of Computer Vision, 132(8), 3086\u20133102. https:\/\/doi.org\/10.1007\/s11263-024-02003-z","journal-title":"International Journal of Computer Vision"},{"key":"2908_CR11","doi-asserted-by":"crossref","unstructured":"Burgos-Artizzu, X. P., Doll\u00e1r, P., Lin, D., Anderson, D. J., & Perona, P. (2012). Social behavior recognition in continuous video. IEEE\/CVF Conference on Computer Vision and Pattern Recognition (pp. 1322\u20131329)","DOI":"10.1109\/CVPR.2012.6247817"},{"issue":"1","key":"2908_CR12","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.neuron.2019.09.038","volume":"104","author":"SR Datta","year":"2019","unstructured":"Datta, S. R., Anderson, D. J., Branson, K., Perona, P., & Leifer, A. (2019). Computational Neuroethology: A Call to Action. Neuron, 104(1), 11\u201324. https:\/\/doi.org\/10.1016\/j.neuron.2019.09.038","journal-title":"Neuron"},{"key":"2908_CR13","doi-asserted-by":"publisher","first-page":"564","DOI":"10.1038\/s41592-021-01106-6","volume":"18","author":"TW Dunn","year":"2021","unstructured":"Dunn, T. W., Marshall, J. D., Severson, K. S., Aldarondo, D. E., Hildebrand, D. G. C., Chettih, S. N., & \u00d6lveczky, B. P. (2021). Geometric deep learning enables 3D kinematic profiling across species and environments. Nature Methods, 18, 564\u2013573. https:\/\/doi.org\/10.1038\/s41592-021-01106-6","journal-title":"Nature Methods"},{"key":"2908_CR14","unstructured":"Duporge, I., Kholiavchenko, M., Harel, R., Wolf, S., Rubenstein, D., Crofoot, M., & Stewart, C. (2024). BaboonLand Dataset: Tracking Primates in the Wild and Automating Behaviour Recognition from Drone Videos. https:\/\/arxiv.org\/abs\/2405.17698v3"},{"key":"2908_CR15","unstructured":"Eyjolfsdottir, E., Branson, S., Burgos-Artizzu, P., & X., Hoopfer, E.D., Schor, J., Anderson, D.J., Perona, P. (2021). Fly v. CaltechDATA: Fly Dataset."},{"key":"2908_CR16","unstructured":"Fazzari, E., Romano, D., Falchi, F., & Stefanini, C. (2024). Animal Behavior Analysis Methods Using Deep Learning: A Survey. https:\/\/arxiv.org\/abs\/2405.14002v1"},{"key":"2908_CR17","doi-asserted-by":"publisher","first-page":"1325001330","DOI":"10.1111\/2041-210X.12584","volume":"7","author":"O Friard","year":"2016","unstructured":"Friard, O., & Gamba, M. (2016). BORIS: a free, versatile open-source event-logging software for video\/audio coding and live observations. Methods in Ecology and Evolution, 7, 1325001330. https:\/\/doi.org\/10.1111\/2041-210X.12584","journal-title":"Methods in Ecology and Evolution"},{"key":"2908_CR18","doi-asserted-by":"publisher","unstructured":"Hohlbaum, K., Andresen, N., Mieske, P., Kahnau, P., Lang, B., Diedrich, K., & Lewejohann, L. (2024). Lockbox enrichment facilitates manipulative and cognitive activities for mice. Open Research Europe, 4(108), , https:\/\/doi.org\/10.12688\/openreseurope.17624.2","DOI":"10.12688\/openreseurope.17624.2"},{"key":"2908_CR19","doi-asserted-by":"publisher","first-page":"5188","DOI":"10.1038\/s41467-021-25420-x","volume":"12","author":"AI Hsu","year":"2021","unstructured":"Hsu, A. I., & Yttri, E. A. (2021). B-SOiD, an open-source unsupervised algorithm for identification and fast prediction of behaviors. Nature Communications, 12, 5188. https:\/\/doi.org\/10.1038\/s41467-021-25420-x","journal-title":"Nature Communications"},{"key":"2908_CR20","doi-asserted-by":"publisher","first-page":"13554","DOI":"10.1038\/s41598-023-40738-w","volume":"13","author":"B Hu","year":"2023","unstructured":"Hu, B., Seybold, B., Yang, S., Sud, A., Barron, K., Liu, Y., Cha, P., & Ross, D. A. (2023). 3D mouse pose from single-view video and a new dataset. Scientific Reports, 13, 13554. https:\/\/doi.org\/10.1038\/s41598-023-40738-w","journal-title":"Scientific Reports"},{"key":"2908_CR21","doi-asserted-by":"publisher","unstructured":"Jia, Y., Li, S., Guo, X., Lei, B., Hu, J., Xu, X.-H., Zhang, W. (2022). Selfee, self-supervised features extraction of animal behaviors. eLife, 11, e76218, https:\/\/doi.org\/10.7554\/eLife.76218","DOI":"10.7554\/eLife.76218"},{"key":"2908_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.conb.2022.102549","volume":"74","author":"A Kennedy","year":"2022","unstructured":"Kennedy, A. (2022). The what, how, and why of naturalistic behavior. Current Opinion in Neurobiology, 74, Article 102549. https:\/\/doi.org\/10.1016\/j.conb.2022.102549","journal-title":"Current Opinion in Neurobiology"},{"key":"2908_CR23","doi-asserted-by":"crossref","unstructured":"Kholiavchenko, M., Kline, J., Ramirez, M., Stevens, S., Sheets, A., Babu, R.. Stewart, C. (2024). KABR: In-Situ Dataset for Kenyan Animal Behavior Recognition from Drone Videos. 2024 IEEE\/CVF Winter Conference on Applications of Computer Vision Workshops (p.31-40).","DOI":"10.1109\/WACVW60836.2024.00011"},{"key":"2908_CR24","doi-asserted-by":"publisher","unstructured":"Kuo, J. Y., Denman, A. J., Beacher, N. J., Glanzberg, J. T., Zhanga, Y., Li, Y., & Lin, D.-T. (2022). Using deep learning to study emotional behavior in rodent models. Frontiers in Behavioral Neuroscience,16. https:\/\/doi.org\/10.3389\/fnbeh.2022.1044492","DOI":"10.3389\/fnbeh.2022.1044492"},{"key":"2908_CR25","doi-asserted-by":"publisher","first-page":"497","DOI":"10.1038\/s41597-024-03312-1","volume":"11","author":"C Li","year":"2024","unstructured":"Li, C., Mellbin, Y., Krogager, J., Polikovsky, S., Holmberg, M., Ghorbani, N., & Hernlund, E. (2024). The Poses for Equine Research Dataset (PFERD). Science Data, 11, 497. https:\/\/doi.org\/10.1038\/s41597-024-03312-1","journal-title":"Science Data"},{"key":"2908_CR26","doi-asserted-by":"publisher","first-page":"1267","DOI":"10.1038\/s42003-022-04080-7","volume":"5","author":"K Luxem","year":"2022","unstructured":"Luxem, K., Mocellin, P., Fuhrmann, F., K\u00fcrsch, J., Miller, S. R., Palop, J. J., & Bauer, P. (2022). Identifying behavioral structure from deep variational embeddings of animal motion. Communications Biology, 5, 1267. https:\/\/doi.org\/10.1038\/s42003-022-04080-7","journal-title":"Communications Biology"},{"key":"2908_CR27","doi-asserted-by":"publisher","unstructured":"Luxem, K., Sun, J.J., Bradley, S.P., Krishnan, K., Yttri, E., Zimmermann, J., & Laubach, M. (2023). Open-source tools for behavioral video analysis: Setup, methods, and best practices. eLife, 12, e79305, https:\/\/doi.org\/10.7554\/eLife.79305","DOI":"10.7554\/eLife.79305"},{"key":"2908_CR28","doi-asserted-by":"crossref","unstructured":"Ma, X., Kaufhold, S., Su, J., Zhu, W., Terwilliger, J., Meza, A., & Wang, Y. (2023). ChimpACT: A Longitudinal Dataset for Understanding Chimpanzee Behaviors. Advances in Neural Information Processing Systems (Vol. 36,","DOI":"10.52202\/075280-1196"},{"key":"2908_CR29","doi-asserted-by":"crossref","unstructured":"Marshall, J. D., Klibaite, U., Gellis, A., Aldarondo, D. E., \u00d6lveczky, B. P., & Dunn, T. W. (2021). The PAIR-R24M Dataset for Multi-animal 3D Pose Estimation. Advances in Neural Information Processing Systems (Vol. 35,","DOI":"10.1101\/2021.11.23.469743"},{"key":"2908_CR30","doi-asserted-by":"publisher","first-page":"1281","DOI":"10.1038\/s41593-018-0209-y","volume":"21","author":"A Mathis","year":"2018","unstructured":"Mathis, A., Mamidanna, P., Cury, K. M., Abe, T., Murthy, V. N., Mathis, M. W., & Bethge, M. (2018). DeepLabCut: markerless pose estimation of user-defined body parts with deep learning. Nature Neuroscience, 21, 1281\u20131289. https:\/\/doi.org\/10.1038\/s41593-018-0209-y","journal-title":"Nature Neuroscience"},{"key":"2908_CR31","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/j.conb.2021.07.014","volume":"70","author":"MH McCullough","year":"2021","unstructured":"McCullough, M. H., & Goodhill, G. J. (2021). Unsupervised quantification of naturalistic animal behaviors for gaining insight into the brain. Current Opinion in Neurobiology, 70, 89\u2013100. https:\/\/doi.org\/10.1016\/j.conb.2021.07.014","journal-title":"Current Opinion in Neurobiology"},{"key":"2908_CR32","doi-asserted-by":"publisher","unstructured":"McHugh, M.L. (2012). Interrater reliability: the kappa statistic. Biochemia Medica, 22(3), 276\u2013282, https:\/\/doi.org\/10.11613\/bm.2012.031","DOI":"10.11613\/bm.2012.031"},{"issue":"5","key":"2908_CR33","doi-asserted-by":"publisher","first-page":"2073","DOI":"10.1109\/TVCG.2020.2973063","volume":"26","author":"H Naik","year":"2020","unstructured":"Naik, H., Bastien, R., Navab, N., & Couzin, I. D. (2020). Animals in virtual environments. IEEE Transactions on Visualization and Computer Graphics, 26(5), 2073\u20132083.","journal-title":"IEEE Transactions on Visualization and Computer Graphics"},{"key":"2908_CR34","doi-asserted-by":"publisher","DOI":"10.1038\/s41596-019-0176-0","author":"T Nath","year":"2019","unstructured":"Nath, T., Mathis, A., Chen, A. C., Patel, A., Bethge, M., & Mathis, M. W. (2019). Using deeplabcut for 3d markerless pose estimation across species and behaviors. Nature Protocols. https:\/\/doi.org\/10.1038\/s41596-019-0176-0","journal-title":"Nature Protocols"},{"key":"2908_CR35","doi-asserted-by":"crossref","unstructured":"Ng, X. L., Ong, K. E., Zheng, Q., Ni, Y., Yeo, S. Y., & Liu, J. (2022). Animal Kingdom: A Large and Diverse Dataset for Animal Behavior Understanding. 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (pp. 19001\u201319012)","DOI":"10.1109\/CVPR52688.2022.01844"},{"key":"2908_CR36","doi-asserted-by":"crossref","unstructured":"Pedersen, M., Haurum, J. B., Hein Bengtson, S., & Moeslund, T. B. (2020). 3D-ZeF: A 3D Zebrafish Tracking Benchmark Dataset. 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (pp. 2423\u20132433)","DOI":"10.1109\/CVPR42600.2020.00250"},{"issue":"12","key":"2908_CR37","doi-asserted-by":"publisher","first-page":"1537","DOI":"10.1038\/s41593-020-00734-z","volume":"23","author":"TD Pereira","year":"2020","unstructured":"Pereira, T. D., Shaevitz, J. W., & Murthy, M. (2020). Quantifying behavior to understand the brain. Nature neuroscience, 23(12), 1537\u20131549.","journal-title":"Nature neuroscience"},{"key":"2908_CR38","doi-asserted-by":"publisher","first-page":"486","DOI":"10.1038\/s41592-022-01426-1","volume":"19","author":"TD Pereira","year":"2022","unstructured":"Pereira, T. D., Tabris, N., Matsliah, A., Turner, D. M., Li, J., Ravindranath, S., & Murthy, M. (2022). SLEAP: A deep learning system for multi-animal pose tracking. Nature Methods, 19, 486\u2013495. https:\/\/doi.org\/10.1038\/s41592-022-01426-1","journal-title":"Nature Methods"},{"key":"2908_CR39","unstructured":"Rogers, M., Gendron, G., Valdez, D.A.S., Azhar, M., Chen, Y., Heidari, S., & Delmas, P. (2023). Meerkat Behaviour Recognition Dataset. https:\/\/arxiv.org\/abs\/2306.11326v1"},{"key":"2908_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2021.106559","volume":"192","author":"H Russello","year":"2022","unstructured":"Russello, H., van der Tol, R., & Kootstra, G. (2022). T-LEAP: Occlusion-robust pose estimation of walking cows using temporal information. Computers and Electronics in Agriculture, 192, Article 106559. https:\/\/doi.org\/10.1016\/j.compag.2021.106559","journal-title":"Computers and Electronics in Agriculture"},{"key":"2908_CR41","doi-asserted-by":"publisher","unstructured":"Segalin, C., Williams, J., Karigo, T., Hui, M., Zelikowsky, M., Sun, J.J., & Kennedy, A. (2021). The Mouse Action Recognition System (MARS) software pipeline for automated analysis of social behaviors in mice. eLife, 10, e63720, https:\/\/doi.org\/10.7554\/eLife.63720","DOI":"10.7554\/eLife.63720"},{"key":"2908_CR42","doi-asserted-by":"publisher","first-page":"1942","DOI":"10.1038\/s41386-020-0776-y","volume":"45","author":"O Sturman","year":"2020","unstructured":"Sturman, O., von Ziegler, L., Schl\u00e4ppi, C., Akyol, F., Privitera, M., Slominski, D., & Bohacek, J. (2020). Deep learning-based behavioral analysis reaches human accuracy and is capable of outperforming commercial solutions. Neuropsychopharmacology, 45, 1942\u20131952. https:\/\/doi.org\/10.1038\/s41386-020-0776-y","journal-title":"Neuropsychopharmacology"},{"key":"2908_CR43","unstructured":"Sun, J.J., Karigo, T., Chakraborty, D., Mohanty, S.P., Wild, B., Sun, Q., & Kennedy, A. (2021). The Multi-Agent Behavior Dataset: Mouse Dyadic Social Interactions. Advances in Neural Information Processing Systems (Vol.\u00a035)."},{"key":"2908_CR44","doi-asserted-by":"crossref","unstructured":"Sun, J. J., Kennedy, A., Zhan, E., Anderson, D. J., Yue, Y., & Perona, P. (2021). Task Programming: Learning Data Efficient Behavior Representations. 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (pp. 2875\u20132884)","DOI":"10.1109\/CVPR46437.2021.00290"},{"issue":"1","key":"2908_CR45","doi-asserted-by":"publisher","first-page":"410","DOI":"10.1111\/j.1439-0310.1963.tb01161.x","volume":"20","author":"N Tinbergen","year":"1961","unstructured":"Tinbergen, N. (1961). On aims and methods of ethology. Zeitschrift f\u00fcr Tierpsychologie, 20(1), 410\u2013433. https:\/\/doi.org\/10.1111\/j.1439-0310.1963.tb01161.x","journal-title":"Zeitschrift f\u00fcr Tierpsychologie"},{"key":"2908_CR46","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1038\/s41386-020-0751-7","volume":"46","author":"L von Ziegler","year":"2021","unstructured":"von Ziegler, L., Sturman, O., & Bohacek, J. (2021). Big behavior: challenges and opportunities in a new era of deep behavior profiling. Neuropsychopharmacology, 46, 33\u201344. https:\/\/doi.org\/10.1038\/s41386-020-0751-7","journal-title":"Neuropsychopharmacology"},{"key":"2908_CR47","doi-asserted-by":"publisher","unstructured":"Weinreb, C., Pearl, J. E., Lin, S., Osman, M. A. M., Zhang, L., Annapragada, S., & Datta, S. R. (2024). Keypoint-MoSeq: parsing behavior by linking point tracking to pose dynamics. Nature Methods,21, 1329\u20131339. https:\/\/doi.org\/10.1038\/s41592-024-02318-2","DOI":"10.1038\/s41592-024-02318-2"},{"key":"2908_CR48","doi-asserted-by":"crossref","unstructured":"Zhang, C., Sheng, J., Li, T., Zhang, H., Zhou, C., Zhu, Q., & Han, L. (2024). Learning highly dynamic behaviors for quadrupedal robots. 2024 ieee international conference on robotics and automation (icra) (pp. 9183\u20139189).","DOI":"10.1109\/ICRA57147.2024.10610440"},{"key":"2908_CR49","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.107416","volume":"165","author":"T Zhou","year":"2023","unstructured":"Zhou, T., Cheah, C. C. H., Chin, E. W. M., Chen, J., Farm, H. J., Goh, E. L. K., & Chiam, K. H. (2023). ContrastivePose: A contrastive learning approach for self-supervised feature engineering for pose estimation and behavorial classification of interacting animals. Computers in Biology and Medicine, 165, Article 107416. https:\/\/doi.org\/10.1016\/j.compbiomed.2023.107416","journal-title":"Computers in Biology and Medicine"},{"key":"2908_CR50","unstructured":"Zia, A., Sharma, R., Arablouei, R., Bishop-Hurley, G., McNally, J., Bagnall, N., & Ingham, A. (2023). CVB: A Video Dataset of Cattle Visual Behaviors. https:\/\/arxiv.org\/abs\/2305.16555v2"}],"container-title":["International Journal of Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-026-02908-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11263-026-02908-x","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11263-026-02908-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T05:48:57Z","timestamp":1785304137000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11263-026-02908-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,15]]},"references-count":50,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2026,7]]}},"alternative-id":["2908"],"URL":"https:\/\/doi.org\/10.1007\/s11263-026-02908-x","relation":{},"ISSN":["0920-5691","1573-1405"],"issn-type":[{"value":"0920-5691","type":"print"},{"value":"1573-1405","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,15]]},"assertion":[{"value":"1 September 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 May 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 June 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"This research does not involve human subjects, sensitive data, harmful insights, nor methodologies or applications that may raise ethical concerns. Animal research\u00a0(Hohlbaum et al.,\n                      \n                      ) was conducted in compliance with the German Animal Welfare Act and Directive 2010\/63\/EU on the protection of animals used for scientific purposes. The experimental procedures and maintenance of the animals were preregistered in the Animal Study Registry (\n                      \n                      ) and approved by the Berlin State Authority, Landesamt f\u00fcr Gesundheit und Soziales (permit number G0249\/19).","order":1,"name":"Ethics","label":"Ethical Approval","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflicts of interest. No sponsorships influenced this research.","order":2,"name":"Ethics","label":"Conflicts of Interest","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"318"}}