Scale mechanism design for data analysis in immersive environments

This dataset contains experimental results from research investigating how different scale mechanisms affect user interactions and visualization effectiveness in immersive analytics environments. Specifically, the experiments explore the impacts of interaction methods (adjusted sensitivity, interaction distance) and visualization strategies (visual density, information display levels), differentiated by spatial reference frames—allocentric (object-centered) versus egocentric (user-centered).Participants performed predefined analytical tasks across four distinct dataset scales:Small Allocentric Scale (Small Allo): Dataset smaller than one-third of user's eye level; allowing simultaneous visualization of the complete dataset.Large Allocentric Scale (Large Allo): Dataset approximately two-thirds of user's eye level.Small Egocentric Scale (Small Ego): Dataset approximately 1.5 times user's eye level, requiring internal exploration.Large Egocentric Scale (Large Ego): Dataset three times larger than user's eye level or more, requiring physical navigation and perceived as independent spatial environments.Performance metrics collected include task accuracy, completion time, subjective cognitive load, and immersion ratings. Data were gathered to empirically validate spatial cognition theories applied to immersive analytics, providing valuable insights for optimizing user experience and interface design in VR and immersive environments.This research contributes empirical evidence supporting a structured scale-based classification method for immersive data exploration interfaces, facilitating intuitive interactions and clear cognitive transitions between spatial frames.