A Dataset for Evaluating Quantum Spin Glyph Visualizations

This dataport will be useful to those interested in visual design for complex physics phenomena. We have included two quantum physics data sample datasets and our empirical study results. 

(1) evaluation results from two experiments (20 participants in each and 40 in total) to empirically validate that separable bivariate pairs of large-magnitude-range vector 

magnitude representations are more efficient than integral pairs. 

(2) quantum physics simulation data used in the first experiment. The simulation data contains more than 10K vector spins, and the samples contain about 445-550 spins. In the study, participants performed three local tasks requiring reading no more than two glyphs. These quantum spins are large in magnitude range and thus were shown to participants (physicists) as a series of separable bivariate pairs. 

These spin vector studies supported that separable-pair visualization led to the most accurate answers and the shortest task execution time, while integral ones were among the least accurate;   unless a redundant categorical color feature was added.} 

(3) quantum simulation data from the second study when we scale up the search space and when participants must look at the entire scene of hundreds of vectors to get an answer.

We measured if more separable bivariate pairs can help viewers obtain scene structures first. Eighteen participants used three separable pairs in three global tasks: find a specific target in 20 seconds, locate the maximum magnitude in 20 seconds, and estimate the total number of regions of interests (here vector exponents) within 2 seconds. Our results revealed that the higher the separability, the higher the accuracy. We believe that the reason was that the separable glyph pairs introduced emergent global (and preattentive) scene features for viewers to manage complexity.

Associated source code, training documents, participants' results collected, statistical methods, and results at https://osf.io/4xcf5/?view_only=94123139df9c4ac984a1e0df811cd580}{$https://osf.io/4xcf5/?view_only=94123139df9c4ac984a1e0df811cd580$ for reproducible research.

If you use this data, please also cite our paper to appear in IEEE Tran. on Vis & Computer Graphics. 

Henan Zhao, Garnett W. Bryant, Wesley Griffin, Judith E. Terrill, and Jian Chen (2023). Evaluating Glyph Design for Showing Large-Magnitude-Range Quantum Spins, IEEE Transactions on Visualization and Computer Graphics, 2023. To appear.