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1. Affective, Hand-Sculpted Glyph Forms for Engaging and Expressive Scientific Visualization

   Zeller, Stephanie ; Samsel, Francesca ; Bartram, Lyn ( October 2022 , IEEE VIS Arts Program)

   Hinrichs, Ute ; Perin, Charles (Ed.)

   As scientific data continues to grow in size, complexity, and density, the representation scope of three-dimensional spaces, data sampling methods, and transfer functions have improved in parallel, allowing visualization practitioners to produce richer multidimensional encodings. Glyphs, in particular, have become an essential encoding tool due to their versatile applications in co-located multivariate volumetric datasets. While prior work has been conducted investigating the perceptual attributes of computationally-generated three-dimensional glyph-forms for scientific visualization, their affective and expressive qualities have yet to be examined. Further, our prior work has demonstrated the benefits of artist hand-created glyph forms in contrast to commonly-used synthetic forms in increasing visual diversity, discrimination, and expressive association in complex environmental datasets. In order to begin to address this gap, we establish preliminary groundwork for an affective design space for hand-created glyph forms, produce a novel set of glyph forms based on this design space, describe a non-verbal method for discovering affective classifications of glyph-forms adopted from current affect theory, and report the results of two studies that explore how these three-dimensional forms produce consistent affective responses across assorted study cohorts. 

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2. Boxcars on Potatoes: Exploring the Design Language for Tangible Visualizations of Scalar Data Fields on 3D Surfaces

   Herman, Bridger ; Keefe, Daniel F. ( October 2018 , Proceedings of the IEEE VIS 2018 Workshop: Toward a Design Language for Data Physicalization)

   We present a design-based exploration of the potential to reinterpret glyph-based visualization of scalar fields on 3D surfaces, a traditional scientific visualization technique, as a data physicalization technique. Even with the best virtual reality displays, users often struggle to correctly interpret spatial relationships in 3D datasets; thus, we are motivated to understand the extent to which traditional scientific visualization methods can translate to physical media where users may simultaneously leverage their visual systems and tactile senses to, in theory, better understand and connect with the data of interest. This pictorial traces the process of our design for a specific user study experiment: (1) inspiration, (2) exploring the data physicalization design space, (3) prototyping with 3D printing, (4) applying the techniques to different synthetic datasets. We call our most recent and compelling visual/tactile design boxcars on potatoes, and the next step in the research is to run a user-based evaluation to elucidate how this design compares to several of the others pictured here. 

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3. Artifact-Based Rendering: Harnessing Natural and Traditional Visual Media for More Expressive and Engaging 3D Visualizations

   https://doi.org/10.1109/TVCG.2019.2934260  

   Johnson, Seth ; Samsel, Francesca ; Abram, Gregory ; Olson, Daniel ; Solis, Andrew J. ; Herman, Bridger ; Wolfram, Phillip J. ; Lenglet, Christophe ; Keefe, Daniel F. ( August 2019 , IEEE Transactions on Visualization and Computer Graphics)

   We introduce Artifact-Based Rendering (ABR), a framework of tools, algorithms, and processes that makes it possible to produce real, data-driven 3D scientific visualizations with a visual language derived entirely from colors, lines, textures, and forms created using traditional physical media or found in nature. A theory and process for ABR is presented to address three current needs: (i) designing better visualizations by making it possible for non-programmers to rapidly design and critique many alternative data-to-visual mappings; (ii) expanding the visual vocabulary used in scientific visualizations to depict increasingly complex multivariate data; (iii) bringing a more engaging, natural, and human-relatable handcrafted aesthetic to data visualization. New tools and algorithms to support ABR include front-end applets for constructing artifact-based colormaps, optimizing 3D scanned meshes for use in data visualization, and synthesizing textures from artifacts. These are complemented by an interactive rendering engine with custom algorithms and interfaces that demonstrate multiple new visual styles for depicting point, line, surface, and volume data. A within-the-research-team design study provides early evidence of the shift in visualization design processes that ABR is believed to enable when compared to traditional scientific visualization systems. Qualitative user feedback on applications to climate science and brain imaging support the utility of ABR for scientific discovery and public communication. 

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4. SoniSpace: Expressive Movement Interaction to Encourage Taking Up Space with the Body

   https://doi.org/10.1145/3563703.3596659  

   Sun, Ruojia ; Wallop, Althea Vail ; Leslie, Grace ; Do, Ellen Yi-Luen ( July 2023 , ACM)

   Movement forms the basis of our thoughts, emotions, and ways of being in the world. Informed by somaesthetics, we design for “taking up space” (e.g. encouraging expansive body movements), which may in turn alter our emotional experience. We demonstrate SoniSpace, an expressive movement interaction experience that uses movement sonification and visualization to encourage users to take up space with their body. We apply a first-person design approach to embed qualities of awareness, exploration, and comfort into the sound and visual design to promote authentic and enjoyable movement expression regardless of prior movement experience. Preliminary results from 20 user experiences with the system show that users felt more comfortable with taking up space and with movement in general following the interaction. We discuss our findings about designing for somatically-focused movement interactions and directions for future work. 

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5. TAC: Optimizing Error-Bounded Lossy Compression for Three-Dimensional Adaptive Mesh Refinement Simulations

   https://doi.org/10.1145/3502181.3531458  

   Wang, Daoce ; Pulido, Jesus ; Grosset, Pascal ; Jin, Sian ; Tian, Jiannan ; Ahrens, James ; Tao, Dingwen ( June 2022 , The 31st ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC 2022))

   Today's scientific simulations require a significant reduction of data volume because of extremely large amounts of data they produce and the limited I/O bandwidth and storage space. Error-bounded lossy compression has been considered one of the most effective solutions to the above problem. However, little work has been done to improve error-bounded lossy compression for Adaptive Mesh Refinement (AMR) simulation data. Unlike the previous work that only leverages 1D compression, in this work, we propose to leverage high-dimensional (e.g., 3D) compression for each refinement level of AMR data. To remove the data redundancy across different levels, we propose three pre-process strategies and adaptively use them based on the data characteristics. Experiments on seven AMR datasets from a real-world large-scale AMR simulation demonstrate that our proposed approach can improve the compression ratio by up to 3.3X under the same data distortion, compared to the state-of-the-art method. In addition, we leverage the flexibility of our approach to tune the error bound for each level, which achieves much lower data distortion on two application-specific metrics. 

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