EuroVisSTAR2021

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https://diglib.eg.org/handle/10.2312/2633048

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Data to Physicalization: A Survey of the Physical Rendering Process
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Djavaherpour, Hessam; Samavati, Faramarz; Mahdavi-Amiri, Ali; Yazdanbakhsh, Fatemeh; Huron, Samuel; Levy, Richard; Jansen, Yvonne; Oehlberg, Lora; Smit, Noeska and Vrotsou, Katerina and Wang, Bei
Physical representations of data offer physical and spatial ways of looking at, navigating, and interacting with data. While digital fabrication has facilitated the creation of objects with data-driven geometry, rendering data as a physically fabricated object is still a daunting leap for many physicalization designers. Rendering in the scope of this research refers to the backand- forth process from digital design to digital fabrication and its specific challenges. We developed a corpus of example data physicalizations from research literature and physicalization practice. This survey then unpacks the ''rendering'' phase of the extended InfoVis pipeline in greater detail through these examples, with the aim of identifying ways that researchers, artists, and industry practitioners ''render'' physicalizations using digital design and fabrication tools.
Uncertainty-aware Visualization in Medical Imaging - A Survey
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Gillmann, Christina; Saur, Dorothee; Wischgoll, Thomas; Scheuermann, Gerik; Smit, Noeska and Vrotsou, Katerina and Wang, Bei
Medical imaging (image acquisition, image transformation, and image visualization) is a standard tool for clinicians in order to make diagnoses, plan surgeries, or educate students. Each of these steps is affected by uncertainty, which can highly influence the decision-making process of clinicians. Visualization can help in understanding and communicating these uncertainties. In this manuscript, we aim to summarize the current state-of-the-art in uncertainty-aware visualization in medical imaging. Our report is based on the steps involved in medical imaging as well as its applications. Requirements are formulated to examine the considered approaches. In addition, this manuscript shows which approaches can be combined to form uncertainty-aware medical imaging pipelines. Based on our analysis, we are able to point to open problems in uncertainty-aware medical imaging.
Visualization in Astrophysics: Developing New Methods, Discovering Our Universe, and Educating the Earth
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Lan, Fangfei; Young, Michael; Anderson, Lauren; Ynnerman, Anders; Bock, Alexander; Borkin, Michelle A.; Forbes, Angus G.; Kollmeier, Juna A.; Wang, Bei; Smit, Noeska and Vrotsou, Katerina and Wang, Bei
We present a state-of-the-art report on visualization in astrophysics. We survey representative papers from both astrophysics and visualization and provide a taxonomy of existing approaches based on data analysis tasks. The approaches are classified based on five categories: data wrangling, data exploration, feature identification, object reconstruction, as well as education and outreach. Our unique contribution is to combine the diverse viewpoints from both astronomers and visualization experts to identify challenges and opportunities for visualization in astrophysics. The main goal is to provide a reference point to bring modern data analysis and visualization techniques to the rich datasets in astrophysics.
A Survey of Human-Centered Evaluations in Human-Centered Machine Learning
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Sperrle, Fabian; El-Assady, Mennatallah; Guo, Grace; Borgo, Rita; Chau, Duen Horng; Endert, Alex; Keim, Daniel; Smit, Noeska and Vrotsou, Katerina and Wang, Bei
Visual analytics systems integrate interactive visualizations and machine learning to enable expert users to solve complex analysis tasks. Applications combine techniques from various fields of research and are consequently not trivial to evaluate. The result is a lack of structure and comparability between evaluations. In this survey, we provide a comprehensive overview of evaluations in the field of human-centered machine learning. We particularly focus on human-related factors that influence trust, interpretability, and explainability. We analyze the evaluations presented in papers from top conferences and journals in information visualization and human-computer interaction to provide a systematic review of their setup and findings. From this survey, we distill design dimensions for structured evaluations, identify evaluation gaps, and derive future research opportunities.
Scalar Field Comparison with Topological Descriptors: Properties and Applications for Scientific Visualization
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Yan, Lin; Masood, Talha Bin; Sridharamurthy, Raghavendra; Rasheed, Farhan; Natarajan, Vijay; Hotz, Ingrid; Wang, Bei; Smit, Noeska and Vrotsou, Katerina and Wang, Bei
In topological data analysis and visualization, topological descriptors such as persistence diagrams, merge trees, contour trees, Reeb graphs, and Morse-Smale complexes play an essential role in capturing the shape of scalar field data. We present a state-of-the-art report on scalar field comparison using topological descriptors. We provide a taxonomy of existing approaches based on visualization tasks associated with three categories of data: single fields, time-varying fields, and ensembles. These tasks include symmetry detection, periodicity detection, key event/feature detection, feature tracking, clustering, and structure statistics. Our main contributions include the formulation of a set of desirable mathematical and computational properties of comparative measures, and the classification of visualization tasks and applications that are enabled by these measures.
EuroVis 2021 CGF 40-3 STARs: Frontmatter
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Smit, Noeska; Vrotsou, Katerina; Wang, Bei; Smit, Noeska and Vrotsou, Katerina and Wang, Bei

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