Pose estimation and tracking dataset for multi-animal behavior analysis on the China Space Station

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.