Evaluating Glyph Design for Showing Large-Magnitude-Range Quantum Spins

Henan Zhao, Garnett W. Bryant, Wesley Griffin, Judith E. Terrill, Jian Chen

Room: 109

2023-10-25T23:57:00ZGMT-0600Change your timezone on the schedule page
Exemplar figure, described by caption below
We introduce a novel glyph design capable of conveying both overall scene structures and local numerical values with precision akin to a text display. The glyph scene has been employed to analyze highly complex three-dimensional quantum spins at the National Institute of Standards and Technology. In this design, colors and glyph sizes are employed to cluster similar data, facilitating the visual comprehension of the distribution of spin charge densities, with a very large dynamic range. Our use of size visual attributes simplifies the search process of locating specific items, enabling quantum physicists to quickly extract quantitative data from the glyphs, thereby enhancing their research efficiency.

Separable and integral dimension pairs;bivariate glyph;3D glyph;quantitative visualization;large-magnitude-range


We present experimental results to explore a form of bivariate glyphs for representing large-magnitude-range vectors. The glyphs meet two conditions: (1) two visual dimensions are separable; and (2) one of the two visual dimensions uses a categorical representation (e.g., a categorical colormap). We evaluate how much these two conditions determine the bivariate glyphs’ effectiveness. The first experiment asks participants to perform three local tasks requiring reading no more than two glyphs. The second experiment scales up the search space in global tasks when participants must look at the entire scene of hundreds of vector glyphs to get an answer. Our results support that the first condition is necessary for local tasks when a few items are compared. But it is not enough for understanding a large amount of data. The second condition is necessary for perceiving global structures of examining very complex datasets. Participants’ comments reveal that the categorical features in the bivariate glyphs trigger emergent optimal viewers’ behaviors. This work contributes to perceptually accurate glyph representations for revealing patterns from large scientific results. We release source code, quantum physics data, training documents, participants’ answers, and statistical analyses for reproducible science at https : //osf.io/4xcf5/?viewonly = 94123139df9c4ac984a1e0df811cd580.