Virtual Reality (VR) is a promising technology platform for immersive visual analytics. However, the design space of VR analytics interface design is vast and difficult to explore using traditional A/B comparisons in controlled experiments. One factor that complicates such comparisons is the dataset. Exposing participants to the same dataset in all conditions introduces a learning effect. On the other hand, using different datasets for all experimental conditions introduces the dataset as an uncontrolled variable. In this paper we propose to rectify this by introducing a generative process for synthesizing clustered datasets for VR analytics experiments. This process generates datasets that are distinct while simultaneously allowing systematic comparisons in experiments. In addition, these datasets can also be used in an iterative design process. In a two-part experiment we demonstrate the validity of the process and demonstrate how new insights in VR-based visual analytics can be gained using synthetic datasets.

Here, we are providing the Python scripts used to generate the datasets used in the above study as well as the six datasets (A, B, C, D, E, F) themselves.