ArticleScientific data2025
Pose estimation and tracking dataset for multi-animal behavior analysis on the China Space Station.
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic review of artificial intelligence use in behavioral analysis of invertebrate and larval model organisms: methods, applications and future recommendations.Frontiers in artificial intelligence · 2026Pooled it
- Automated behavioral segmentation and markerless pose tracking of mice during spaceflight.bioRxiv : the preprint server for biology · 2026Article
- Adoption of MMPose, a general purpose pose estimation library, for animal tracking.bioRxiv : the preprint server for biology · 2026Article
- Structure-Aware Multi-Animal Pose Estimation for Space Model Organism Behavior Analysis.Animals : an open access journal from MDPI · 2025Article
- A Deep Learning Framework for Multi-Object Tracking in Space Animal Behavior Studies.Animals : an open access journal from MDPI · 2025Article
- Pose estimation and tracking dataset for multi-animal behavior analysis on the China Space Station.Scientific data · 2025Article
Corrections and comments
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Authors and funding
11 authors.
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Abstract
Non-contact behavioral study through intelligent image analysis is becoming increasingly vital in animal neuroscience and ethology. The shift from traditional "black box" methods to more open and intelligent approaches is driven by advances in deep learning-based pose estimation and tracking. These technologies enable the extraction of key points and their temporal relationships from sequence images. Such approach is particularly crucial for investigating animal behaviors in outer space, with microgravity, high radiation, and hypomagnetic field. However, the limited image data of space animal and the lack of publicly accessible datasets with ground truth annotations have hindered the development of effective evaluation tools and methods. To address this challenge, we present the SpaceAnimal Dataset-the first multi-task, expert-validated dataset for multi-animal behavior analysis in complex scenarios, including model organisms such as Caenorhabditis elegans, Drosophila, and zebrafish. Additionally, this paper provides evaluation code for deep learning models, establishing benchmarks to guide future research. This dataset will advance AI technology innovation in this field, contributing to the discovery of new behavior patterns in space animals.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.