The HuTwin (Human Cyber Twin) dataset represents a comprehensive multimodal collection capturing human behavioral dynamics during collaborative Virtual Reality interactions. The dataset encompasses synchronized multimodal recordings from 40 participants (organized in 20 collaborative pairs) engaged in a VR-based (cooking-game) collaborative task across multiple experimental sessions. Each participant completed three distinct experimental sessions under varying conditions, yielding a total of 120 comprehensive recordings. The dataset integrates four primary data modalities: (1) objective behavioral measurements, including body motion capture, facial expression tracking, and speech acoustic features, and (2) subjective experiential assessments encompassing QoE ratings, presence measures, and emotional self-reports. The experimental design implemented a within-subjects approach with systematic manipulation of multiple independent variables: Avatar Type (Chef vs. Humanoid), Connection Type (Host vs. Client, determining emotion communication direction), Role assignment (Student vs. Teacher), and Network Quality conditions (Delay: 0ms vs. 500ms; Jitter: 0ms vs. 500ms). This factorial design enables a comprehensive investigation of how avatar representation, emotional expressivity, task roles, and network impairments interact to influence collaborative performance, emotional states, and perceived quality of experience in immersive virtual environments.