Accepted Tutorials

Here is the list of the accepted tutorials.

Color Matters in Visualization

Sunday, October 24: 8:00am-11:25am

Theresa-Marie Rhyne, Visualization Consultant

We provide an overview of the basics of color theory and demonstrate how to apply the concepts to visualization. Our tutorial is intended for a broad audience of individuals interested in understanding the mysteries of color. Our journey includes the introduction to the concepts of color models and harmony, a review of color vision principles, the defining of color gamut, spaces and systems, and demonstrating online and mobile apps for performing color analyses of digital media. Freely available commercial and research tools for your continued use in color selection and color deficiency assessments are highlighted. The tutorial includes concepts from art and design such as extending the fundamentals of the Bauhaus into data visualization as well as overviews of color perception and appearance principals for vision. New for this year: the Hue Chroma Luminance (HCL) color model is featured with freely available tools for building HCL color schemes demonstrated.

Topological Analysis of Ensemble Scalar Data with TTK

Sunday, October 24: 8:00am-11:25am

Christoph Garth, TU Kaiserslautern
Charles Gueunet, Kitware
Pierre Guillou, Sorbonne Université
Lutz Hofmann, Heidelberg University
Joshua A Levine, University of Arizona
Jonas Lukasczyk, TU Kaiserslautern
Julien Tierny, CNRS / Sorbonne Université
Jules Vidal, Sorbonne Université
Bei Wang, University of Utah
Florian Wetzels, TU Kaiserslautern

This tutorial presents topological methods for the analysis and visualization of scientific data from a user’s perspective, with the Topology ToolKit (TTK), an open-source library for topological data analysis. In particular, this year’s tutorial has a special focus on ensemble data analysis with TTK. Topological methods have gained considerably in popularity and maturity over the last twenty years and success stories of established methods have been documented in a wide range of applications (combustion, chemistry, astrophysics, material sciences, etc.) with both acquired and simulated data, in both post-hoc and in-situ contexts. This tutorial provides a beginner’s introduction to topological methods for practitioners, researchers, students, and lecturers, with a special emphasis towards ensemble data analysis. In particular, instead of focusing on theoretical aspects and algorithmic details, this tutorial focuses on how topological methods can be used in practice to reduce ensemble datasets into concise yet meaningful topological data representations and how these representations can support advanced analysis. The tutorial describes in detail how to achieve these tasks with TTK. In comparison to the last iterations of this tutorial [14, 15, 17] (the 2020 edition video recordings are fully available online [15]), this iteration focuses on the practical usage of TTK for ensemble data analysis. First, we provide a general introduction to topological methods and their application in data analysis, and a brief overview of TTK’s main entry point for end users, namely ParaView, will be presented. Second, we detail TTK’s software infrastructure for ensemble data analysis, including TTK’s Docker support (to facilitate its deployment on computing servers), a tour of the topological data representations supported by TTK, TTK’s python support and lastly TTK’s cinema support (to manipulate ensemble of topological data representations with a database formalism). Third, we will present concrete use cases of ensemble data analysis and visualization, using contour tree alignment, mandatory critical points and ensemble clustering and summarization with persistence diagrams. Presenters of this tutorial include experts in topological methods, core authors of TTK as well as active users, coming from academia and industry. This tutorial mostly targets students, practitioners and researchers who are not necessarily experts in topological methods but who are interested in using them in their daily tasks. We also target researchers already familiar to topological methods and who are interested in using or contributing to TTK. We kindly ask potential attendees to optionally pre-register at the following address, in order for us to reach out to them ahead of the tutorial with information updates (for instance, last minute updates, instructions for the download of the tutorial material package, etc.): https://forms.gle/Me4mqYmJYJsgEQYU9 Tutorial web page (including all material, TTK pre-installs in virtual machines, code, data, demos, video tutorials, slides, etc): https://topology-tool-kit.github.io/ieeeVisTutorial.html

Visualization Analysis and Design

Sunday, October 24: 8:00am-3:15pm

Tamara Munzner, University of British Columbia

This introductory tutorial will provide a broad foundation for thinking systematically about visualization systems, built around the idea that becoming familiar with analyzing existing systems is a good springboard for designing new ones. The major data types of concern in visual analytics, information visualization, and scientific visualization will all be covered: tables, networks, and sampled spatial data. This tutorial is focused on data and task abstractions, and the design choices for visual encoding and interaction; it will not cover algorithms. No background in computer science or visualization is assumed.

IEEE VIS Full Paper Review Model and Process: Becoming a (Better) Program Committee Member

Monday, October 25: 11:50am-3:15pm

Bongshin Lee, Microsoft Research
Petra Isenberg, Inria
Anastasia Bezerianos, Université Paris-Sud / Université Paris-Saclay

Two fundamental tenets of scientific research are that it can be scrutinized and built-upon. Both require that the collected data, supporting materials, and decision timing be shared, so others can examine, reuse, and extend them. This tutorial will teach how you can share the artifacts of your own research. You will learn about the benefits gained by making different components or stages of research transparent, including decision timing, data collection procedures, raw data, and analysis & code. And for each, there will be tips and tricks as well as a walkthrough on how to share your work using the Open Science Framework. Bringing your laptop is highly encouraged.

We will also discuss what to do (and what you do not need to do) when reviewing a paper with open materials. You will hopefully walk away with an improved ability to make your own research more empirically replicable and computationally reproducible. These skills can enable you to have a greater impact on the field by facilitating reuse and further development of your ideas by both other researchers and those who wish to apply your work.

User-Centred Evaluation in Visualization

Monday, October 25: 8:00am-11:25am

Camilla Forsell, Linköping University
Matthew Cooper, Linköping University
Niklas Rönnberg, Linköping University

The objective of this half-day introductory tutorial in user-centred evaluation in visualization is to introduce the topic and explain its importance, provide general knowledge about what is important to consider and what resources are available to support further study in this area. Participants will also learn to better judge the relevance and quality of a publication which includes an evaluation since similar rules apply. By providing fundamental insights into the subject the tutorial also encourages participants to further deepen their knowledge after completion of the tutorial by self-study or participating in further courses.

Riemannian Geometry for Scientific Visualization

Monday, October 25: 11:50am-3:15pm

Markus Hadwiger, KAUST
Thomas Theussl, KAUST
Peter Rautek, KAUST

This tutorial introduces the most important basics of Riemannian geometry and related concepts with a specific focus on applications in scientific visualization. The main concept in Riemannian geometry is the presence of a Riemannian metric on a differentiable manifold, comprising a second-order tensor field that defines an inner product in each tangent space that varies smoothly from point to point. Technically, the metric allows defining and computing distances and angles in a coordinate-independent manner. However, even more importantly, it in a sense is the major structure that defines the space where scientific data, such as scalar, vector, and tensor fields live. However, the concept of a metric, and crucial related concepts such as connections and covariant derivatives, are not often used explicitly in visualization. In contrast to concepts of differential topology, which have been used extensively in visualization, for example in scalar and vector field topology, we believe that concepts from Riemannian geometry have been underrepresented in the visualization literature. One reason for this might be that most visualization techniques are developed for scalar, vector, or tensor fields given in Euclidean space R2 or R3, and data given on curved surfaces are usually treated explicitly through their embedding in R3. However, the presence of a Riemannian metric on a manifold has very important implications even for data given in Euclidean space, for example regarding the physical meaning of visualizations as well as for the use of non-Cartesian coordinates. Therefore, considering the metric tensor field explicitly provides several important benefits. In this tutorial, we will particularly highlight the additional insight that can be gained from employing concepts from Riemannian geometry in scientific visualization. Although we believe that insight is the most important benefit to be gained from using these concepts, we will also discuss computational advantages. In addition to Riemannian metrics, we will also introduce the most important related concepts from modern, coordinate-free differential geometry, in particular general (non-Cartesian) tensor fields and differential forms, smooth mappings between manifolds, Lie derivatives, and Lie groups and Lie algebras. Throughout the tutorial, we will use several examples from the scientific visualization literature, dealing with scalar, vector, or tensor fields, respectively, and highlight their implicit or explicit connections to Riemannian geometry.

Observable: Quick and Effective Visualization Prototyping with Reactive Notebooks

Monday, October 25: 8:00am-3:15pm

John A. Guerra-Gomez, Northeastern University

Creating custom made data visualizations tends to be an arduous and frustrating process. In the past, you were either forced to use commercial tools which offer a limited set of pre-designed visualizations, or you had to juggle the myriad of software required for setting up a local development environment. Then, after figuring that out and after choosing the perfect advanced visualization from the set of examples of the D3 or Vega library, you can spend days trying to apply them to your own data. And when you finally succeed, your next challenge will be to share it with your target audience. In this tutorial I’ll teach you how to go from the theory of data visualization into practice. You will learn how to reuse examples from the community to speed up the application of advanced visualization techniques with your own data. Moreover, you will be able to learn how to leverage reactive notebooks to move to the next step and created your own custom made visualizations. Finally, you will learn how to export your results, embed them on your own page or project and distribute it to your stakeholders or peers.