A Benchmark Dataset for Rat Social and Aggressive Behavior Classification

Social interactions are central to behavioral and systems neuroscience, yet progress in understanding their neural basis depends on reliable and scalable behavioral quantification. However, standardized datasets, transparent annotation schemes and reproducible benchmarks for social-behavior classification remain limited. Here, we present a curated video dataset of rat social interactions recorded under the resident-intruder paradigm. The dataset covers non-social, social and aggressive behaviors, with aggression-related episodes further annotated into ethologically defined fine-grained subtypes. To support automated analysis of these dynamic and contact-rich interactions, we provide a reproducible pose-based workflow that converts DeepLabCut-derived body-part trajectories into structured features describing individual movement and inter-animal spatial relationships. Using this feature representation, we benchmark representative traditional machine learning and deep learning models under a unified evaluation protocol. These benchmarks provide reference performance for both coarse-grained social behavioral classification and fine-grained aggression-related behavior recognition, while documenting differences in class-wise performance, training efficiency and model complexity. Together, this dataset and workflow provide an open resource for developing, evaluating and comparing automated methods for rat social-interaction analysis.