{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T20:50:53Z","timestamp":1792788653799,"version":"4.5.0"},"reference-count":37,"publisher":"Society for Neuroscience","issue":"9","license":[{"start":{"date-parts":[[2027,3,1]],"date-time":"2027-03-01T00:00:00Z","timestamp":1803859200000},"content-version":"vor","delay-in-days":199,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-sa\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000923","name":"Australian Research Council","doi-asserted-by":"publisher","award":["DE220100691"],"award-info":[{"award-number":["DE220100691"]}],"id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000923","name":"Australian Research Council","doi-asserted-by":"publisher","award":["DP220103812"],"award-info":[{"award-number":["DP220103812"]}],"id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000923","name":"Australian Research Council","doi-asserted-by":"publisher","award":["DP230102614"],"award-info":[{"award-number":["DP230102614"]}],"id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014370","name":"Simons Foundation Autism Research Initiative","doi-asserted-by":"publisher","award":["625793"],"award-info":[{"award-number":["625793"]}],"id":[{"id":"10.13039\/100014370","id-type":"DOI","asserted-by":"publisher"}]},{"name":"DHAC | National Health and Medical Research Council","award":["2027072"],"award-info":[{"award-number":["2027072"]}],"id":[{"id":"https:\/\/ror.org\/011kf5r70","id-type":"ROR","asserted-by":"crossref"}]},{"name":"National Institute of Neurological Disorders and Stroke of the National Institutes of Health","award":["R01NS118406"],"award-info":[{"award-number":["R01NS118406"]}],"id":[{"id":"https:\/\/ror.org\/01s5ya894","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":["eneuro.org"],"crossmark-restriction":true},"short-container-title":["eNeuro"],"accepted":{"date-parts":[[2026,8,10]]},"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:p>Zebrafish are an important model system in behavioral neuroscience due to their rapid development and suite of distinct, innate behaviors. In this study, we tracked the behavior of 6\u2005d postfertilization larval zebrafish (of indeterminate sex, as larvae at this developmental stage are sexually undifferentiated). Quantifying many of these larval behaviors requires detailed tracking of eye and tail kinematics, which in turn demands imaging at high spatial and temporal resolution, ideally using semi- or fully automated tracking methods for throughput efficiency. However, creating and validating accurate tracking models is time-consuming and labor-intensive, with many research groups duplicating efforts on similar images. With the goal of developing a useful community resource, we trained pose estimation models using a diverse array of video parameters and a 15-keypoint pose model. We deliver an annotated dataset of free-swimming and head-embedded behavioral videos of larval zebrafish, along with four pose estimation networks from DeepLabCut and SLEAP (two variants of each). We also evaluated model performance across varying imaging conditions to guide users in optimizing their imaging setups. This resource will allow other researchers to skip the tedious and laborious training steps for setting up behavioral analyses, guide model selection for specific research needs, and provide ground-truth data for benchmarking new tracking methods.<\/jats:p>","DOI":"10.1523\/eneuro.0071-26.2026","type":"journal-article","created":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T17:50:15Z","timestamp":1786729815000},"page":"ENEURO.0071-26.2026","update-policy":"https:\/\/doi.org\/10.1523\/eneuro.crossmarkpolicy","source":"Crossref","is-referenced-by-count":0,"title":["Automated Behavioral Tracking of Zebrafish Larvae with DeepLabCut and SLEAP: Pretrained Networks and Datasets of Annotated Poses"],"prefix":"10.1523","volume":"13","author":[{"given":"Leandro A.","family":"Scholz","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tessa","family":"Mancienne","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sarah J.","family":"Stednitz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3150-9216","authenticated-orcid":false,"given":"Ethan K.","family":"Scott","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4838-8502","authenticated-orcid":false,"given":"Conrad C. Y.","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"393","published-online":{"date-parts":[[2026,8,14]]},"reference":[{"key":"2026091611005829000_13.9.ENEURO.0071-26.2026.1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-81997-9"},{"key":"2026091611005829000_13.9.ENEURO.0071-26.2026.2","doi-asserted-by":"crossref","first-page":"372","DOI":"10.1016\/j.neuron.2022.10.034","article-title":"Brainstem circuits encoding start, speed, and duration of swimming in adult zebrafish","volume":"111","author":"Berg","year":"2022","journal-title":"Neuron"},{"key":"2026091611005829000_13.9.ENEURO.0071-26.2026.3","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1186\/s12915-018-0494-7","article-title":"Measuring behavior across scales","volume":"16","author":"Berman","year":"2018","journal-title":"BMC Biol"},{"key":"2026091611005829000_13.9.ENEURO.0071-26.2026.4","doi-asserted-by":"publisher","DOI":"10.1016\/j.cub.2017.08.012"},{"key":"2026091611005829000_13.9.ENEURO.0071-26.2026.5","doi-asserted-by":"publisher","DOI":"10.1523\/JNEUROSCI.0615-07.2007"},{"key":"2026091611005829000_13.9.ENEURO.0071-26.2026.6","doi-asserted-by":"crossref","first-page":"1461264","DOI":"10.3389\/fbioe.2024.1461264","article-title":"Exploring the use of deep learning models for accurate tracking of 3D zebrafish trajectories","volume":"12","author":"Fan","year":"2024","journal-title":"Front Bioeng Biotechnol"},{"key":"2026091611005829000_13.9.ENEURO.0071-26.2026.7","doi-asserted-by":"publisher","DOI":"10.1523\/JNEUROSCI.0204-24.2025"},{"key":"2026091611005829000_13.9.ENEURO.0071-26.2026.8","doi-asserted-by":"publisher","DOI":"10.1242\/jeb.010272"},{"key":"2026091611005829000_13.9.ENEURO.0071-26.2026.9","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-30303-w"},{"key":"2026091611005829000_13.9.ENEURO.0071-26.2026.10","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-85896-x"},{"key":"2026091611005829000_13.9.ENEURO.0071-26.2026.11","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"2026091611005829000_13.9.ENEURO.0071-26.2026.12","unstructured":"Howard AG , Zhu M , Chen B , Kalenichenko D , Wang W , Weyand T , Andreetto M , Adam H (2017) MobileNets: efficient convolutional neural networks for mobile vision applications. arXiv:1704.0486. https:\/\/doi.org\/10.48550\/arXiv.1704.04861"},{"key":"2026091611005829000_13.9.ENEURO.0071-26.2026.13","doi-asserted-by":"publisher","DOI":"10.1016\/j.cub.2019.11.026"},{"key":"2026091611005829000_13.9.ENEURO.0071-26.2026.14","doi-asserted-by":"crossref","unstructured":"Jouary A , Laborde A , Silva PT , Mata JM , Marques JC , Collins E , Peterson RT , Machens CK , Orger MB (2024) Megabouts: a flexible pipeline for zebrafish locomotion analysis.","DOI":"10.1101\/2024.09.14.613078"},{"key":"2026091611005829000_13.9.ENEURO.0071-26.2026.15","doi-asserted-by":"crossref","first-page":"481","DOI":"10.3389\/fncel.2015.00481","article-title":"Methyl-CpG binding protein 2 (Mecp2) regulates sensory function through Sema5b and Robo2","volume":"9","author":"Leong","year":"2015","journal-title":"Front Cell Neurosci"},{"key":"2026091611005829000_13.9.ENEURO.0071-26.2026.16","doi-asserted-by":"crossref","first-page":"7","DOI":"10.3389\/fninf.2015.00007","article-title":"Bonsai: an event-based framework for processing and controlling data streams","volume":"9","author":"Lopes","year":"2015","journal-title":"Front Neuroinform"},{"key":"2026091611005829000_13.9.ENEURO.0071-26.2026.17","doi-asserted-by":"publisher","DOI":"10.1038\/s42003-022-03021-8"},{"key":"2026091611005829000_13.9.ENEURO.0071-26.2026.18","doi-asserted-by":"publisher","DOI":"10.3389\/fncir.2021.748535"},{"key":"2026091611005829000_13.9.ENEURO.0071-26.2026.19","doi-asserted-by":"publisher","DOI":"10.1016\/j.cub.2017.12.002"},{"key":"2026091611005829000_13.9.ENEURO.0071-26.2026.20","doi-asserted-by":"publisher","DOI":"10.1038\/s41593-018-0209-y"},{"key":"2026091611005829000_13.9.ENEURO.0071-26.2026.21","doi-asserted-by":"crossref","unstructured":"Mathis A , Biasi T , Schneider S , Y\u00fcksekg\u00f6n\u00fcl M , Rogers B , Bethge M , Mathis MW (2021) Pretraining boosts out-of-domain robustness for pose estimation. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp 1858\u20131867. https:\/\/doi.org\/10.48550\/arXiv.1909.11229","DOI":"10.1109\/WACV48630.2021.00190"},{"key":"2026091611005829000_13.9.ENEURO.0071-26.2026.22","doi-asserted-by":"publisher","DOI":"10.1016\/j.cub.2019.11.022"},{"key":"2026091611005829000_13.9.ENEURO.0071-26.2026.23","doi-asserted-by":"publisher","DOI":"10.1016\/j.celrep.2023.112243"},{"key":"2026091611005829000_13.9.ENEURO.0071-26.2026.24","doi-asserted-by":"publisher","DOI":"10.3389\/fncir.2013.00107"},{"key":"2026091611005829000_13.9.ENEURO.0071-26.2026.25","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-018-0234-5"},{"key":"2026091611005829000_13.9.ENEURO.0071-26.2026.26","doi-asserted-by":"publisher","DOI":"10.1038\/s41593-020-00734-z"},{"key":"2026091611005829000_13.9.ENEURO.0071-26.2026.27","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-022-01426-1"},{"key":"2026091611005829000_13.9.ENEURO.0071-26.2026.28","doi-asserted-by":"crossref","first-page":"e1011566","DOI":"10.1371\/journal.pcbi.1011566","article-title":"Rapid automated 3-D pose estimation of larval zebrafish using a physical model-trained neural network","volume":"19","author":"Ravan","year":"2023","journal-title":"PLoS Comput Biol"},{"key":"2026091611005829000_13.9.ENEURO.0071-26.2026.29","doi-asserted-by":"crossref","unstructured":"Reddy G , Desban L , Tanaka H , Roussel J , Mirat O , Wyart C (2020) A lexical approach for identifying behavioral action sequences. bioRxiv 2020.08.27.270694.","DOI":"10.1101\/2020.08.27.270694"},{"key":"2026091611005829000_13.9.ENEURO.0071-26.2026.30","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-018-0295-5"},{"key":"2026091611005829000_13.9.ENEURO.0071-26.2026.31","first-page":"234","article-title":"U-Net: convolutional networks for biomedical image segmentation.","volume-title":"Medical image computing and computer-assisted intervention","volume":"Vol. 9351","author":"Ronneberger","year":"2015"},{"key":"2026091611005829000_13.9.ENEURO.0071-26.2026.32","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-023-06031-6"},{"key":"2026091611005829000_13.9.ENEURO.0071-26.2026.33","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1006699"},{"key":"2026091611005829000_13.9.ENEURO.0071-26.2026.34","doi-asserted-by":"publisher","DOI":"10.7554\/eLife.64000"},{"key":"2026091611005829000_13.9.ENEURO.0071-26.2026.35","doi-asserted-by":"crossref","first-page":"17596","DOI":"10.1038\/s41598-017-17894-x","article-title":"Automatic multiple zebrafish larvae tracking in unconstrained microscopic video conditions","volume":"7","author":"Wang","year":"2017","journal-title":"Sci Rep"},{"key":"2026091611005829000_13.9.ENEURO.0071-26.2026.36","doi-asserted-by":"crossref","first-page":"e0364262026","DOI":"10.1523\/JNEUROSCI.0364-26.2026","article-title":"Brain-wide circuitry underlying altered auditory habituation in zebrafish models of autism.","volume":"46","author":"Wilde","year":"2024","journal-title":"J Neurosci"},{"key":"2026091611005829000_13.9.ENEURO.0071-26.2026.37","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-024-48792-2"}],"container-title":["eneuro"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/syndication.highwire.org\/content\/doi\/10.1523\/ENEURO.0071-26.2026","content-type":"unspecified","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/syndication.highwire.org\/content\/doi\/10.1523\/ENEURO.0071-26.2026","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T18:01:11Z","timestamp":1789581671000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.eneuro.org\/lookup\/doi\/10.1523\/ENEURO.0071-26.2026"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,14]]},"references-count":37,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2026,9,16]]},"published-print":{"date-parts":[[2026,9]]}},"alternative-id":["10.1523\/ENEURO.0071-26.2026"],"URL":"https:\/\/doi.org\/10.1523\/eneuro.0071-26.2026","relation":{},"ISSN":["2373-2822"],"issn-type":[{"value":"2373-2822","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,14]]}}}