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Tutorials

Topological Data Analysis Made Easy with the Topology ToolKit, A Sequel

Sunday, October 20: 9:00am-12:20pm

Martin Falk, Linköping University
Charles Gueunet, Kitware
Joshua A Levine, University of Arizona
Jonas Lukasczyk, Technische Universität Kaiserslautern
Julien Tierny, CNRS/Sorbonne Université

This tutorial presents topological methods for the analysis and visualization of scientific data from a user’s perspective, with the Topology ToolKit (TTK), a recently released open-source library for topological data analysis. 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. Last year, we held the first iteration of this tutorial, that aimed to cover this area at a software level and from a user’s point-of-view. This tutorial aims to continue to fill a gap by providing a beginner’s introduction to topological methods for practitioners, researchers, students, and lecturers. In particular, instead of focusing on theoretical aspects and algorithmic details, this tutorial focuses on how topological methods can be useful in practice for concrete data analysis tasks such as segmentation, feature extraction or tracking. The tutorial describes in detail how to achieve these tasks with TTK. First, after an introduction to topological methods and their application in data analysis, a brief overview of TTK’s main entry point for end users, namely ParaView, will be presented. Second, an overview of TTK’s main features will be given. A running example will be described in detail, showcasing how to access TTK’s features via ParaView, Python, VTK/C++, and C++. Third, hands-on sessions will concretely show how to use TTK in ParaView for multiple, representative data analysis tasks. Fourth, the usage of TTK will be presented for developers, in particular by describing several examples of visualization and data analysis projects that were built on top of TTK. Finally, some feedback regarding the usage of TTK as a teaching platform for topological analysis will be given. Presenters of this tutorial include experts in topological methods, core authors of TTK as well as active users, coming from academia, labs, or industry. A large part of the tutorial will be dedicated to hands-on exercises and a rich material package (including TTK pre-installs in virtual machines, code, data, demos, video tutorials, etc. see last year’s tutorial website [11]) will be provided to the participants. 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/gn7yn3JwzdBN4Mgr7

VTK-m - A ToolKit for Scientific Visualization on Many-Core Processors

Sunday, October 20: 9:00am-12:40pm

Hank Childs, University of Oregon
Kenneth Moreland, Sandia National Laboratories
David Pugmire, Oak Ridge National Laboratory
Robert Maynard, Kitware

This tutorial will cover the VTK-m software, i.e., the many-core version of VTK (a popular open source toolkit for scientific visualization). VTK-m provides a portable performance infrastructure, which enables efficient performance on multi-core CPUs and NVIDIA GPUs, and provides an easy extensibility path to future architectures. Tutorial presentations will focus both on how to use VTK-m and on how to develop VTK-m code.

Visualizing Multivariate Networks

Sunday, October 20: 2:20pm-5:40pm

Carolina Nobre, University of Utah
Marc Streit, Johannes Kepler University
Alexander Lex, University of Utah

Multivariate networks (MVNs) are made up of nodes and their relationships (links), but also data about those nodes and links as attributes. Visualizing MVNs, however, is challenging, especially when both the topology of the network and the attributes need to be considered concurrently. In this tutorial, we will explore how MVNs can be visualized, including real-world examples and practical guidelines on choosing an appropriate technique. The tutorial starts with a network sketching activity to get participants engaged and thinking about the challenges inherent to visualizing MVNs. The second section is devoted to understanding network types and analysis tasks, including how MVN tasks can be described as a function of attributes and target topological structures. We divide the remaining time into four parts, each focused on a category of MVN visualization techniques: node-link layouts, tabular layouts, implicit layouts, and multiple coordinated views. Each section will contain an explanation of the technique, followed by real-world usage examples. We conclude each section with a set of actionable guidelines on when to use the proposed techniques based on network characteristics and analyst tasks. These guidelines are also available for future reference in a companion website, available at https://vdl.sci.utah.edu/mvnv/. The website provides an overview of all techniques as well as a wizard designed to help users select a technique for a specific data and task combination.

Better Presentations for Visualization Research

Sunday, October 20: 2:20pm-5:40pm

Jon Schwabish, Urban Institute/PolicyViz

Good visualization research presentations—as well as presentations for clients, administrators, and other stakeholders—can be crucial for helping audiences understand the impacts of research and, ultimately, for adopting research approaches and recommendations. Too many researchers and practitioners prepare presentations by simply converting reports to slides—text becomes bullet points, tables and figures get copied and pasted. Presentations, however, are a fundamentally different form of communication than writing. When researchers treat their presentations and papers identically, they miss this important distinction, as well as the opportunity to share their work as effectively as possible.

Visualization researchers commonly face the following challenges when presenting their work to an audience:

  • Deciding how much detail to include when presenting the experiment design
  • presenting key results in a digestible way
  • explaining algorithms and the math behind them
  • deciding whether to perform demonstrations or show videos, and
  • time constraints (e.g., presenting detailed research in 15 minutes)

The goal of this workshop is to provide attendees the tools and strategies to address these challenges and help their audiences understand their research and analysis.

In this half-day workshop, attendees learn how to better present their work to researchers, practitioners, decisionmakers, and others. The workshop is divided into three main instruction sections and includes hands-on exercises where attendees work with worksheets and sample slides. A sample agenda can be found in section 2 of this proposal.

Three primary themes drive the entire workshop, from design to delivery. First, presenters should visualize their content. Studies have consistently shown that we better comprehend and retain information when we have pictures to accompany or replace text. Second, presenters should unify the elements of their presentation. This means consistency in colors and fonts, in slide formats, and in integrating images and verbal commentary. Finally, presenters need to focus their audience’s attention on specific bits of text, graphic elements, or slide objects. Rather than putting up as much information as possible on every slide—which many presenters do because it’s easy and it reminds them to cover each point—slides should be simple and free of clutter so that they can direct the audience’s attention to the desired position on the screen. Attendees are provided with worksheets and other handouts, as well as copies of the instructor’s book, Better Presentations: A Guide for Scholars, Researchers, and Wonks.

Visual Analysis and Design

Monday, October 21: 9:00am-12:20pm

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.

Statistical Data Representation, Visualization, and Uncertainty Analysis

Monday, October 21: 9:00am-12:20pm

Soumya Dutta, Los Alamos National Laboratory
Hanqi Guo, Argonne National Laboratory
Hans-Christian Hege, Zuse Institute Berlin
Han-Wei Shen, The Ohio State University

Efficient analysis and visualization of data using statistical methods have benefited the visualization community for many years. As the size of data grows rapidly, researchers are increasingly relying on techniques aiming primarily at the efficient identification and analysis of regions that are characterized by the presence of characteristic features, instead of looking at the data at its entirety. Using statistical distributions, statistical characteristics of data can be compactly represented, efficiently analyzed and visualized. Recent developments have demonstrated the broad applicability of statistical distributions in data visualization by introducing novel stochastic algorithms and addressing important problems such as feature identification, extraction, and tracking; multi-variable relationship exploration; query-driven visualization; in-situ data summary, and many more.

Besides being able to compactly represent statistical data properties, a key advantage of statistical distribution-based data analysis techniques is the ability to quantify uncertainty during visualization. Uncertainty-aware visualization algorithms developed using statistical methods and distribution-based data representations can successfully communicate the trustworthiness of the visual representation to the application scientists so that the scientists can draw meaningful conclusions from the visualization results. As we step into the era of big data, the relevance of statistical distribution-based methods has become even more prominent since statistical distributions can be used to generate compact data summaries, which are significantly smaller than the full-resolution raw data and such data triage can be performed in situ. As a result, a variety of visualization applications using statistical distributions has been developed which evidently indicate that such uncertainty-aware statistical methods will provide a promising path forward in the future.

Considering the aforementioned benefits of statistical methods in data visualization, we propose to organize a half-day tutorial on statistical data representation, visualization, and uncertainty analysis. The tutorial will highlight concepts related to general statistical methods of data visualization, with a focus on statistical distribution-based techniques. A comprehensive discussion on the state of the art of uncertainty-aware visualization algorithms using distributional data will be conducted, serving as a basis for audiences interested in research in statistical data representation and processing, with applications in data analysis, visualization, and uncertainty quantification. We will systematically introduce different categories of visual-analytics algorithms that use and benefit from statistical methods. In addition, concepts and applications of uncertainty-aware visualization techniques will be presented. Finally, the latest research trends and applications utilizing statistical data representations will be discussed. The tutorial will conclude, by highlighting future scopes and open problems that have to be solved for the further advancement of statistically supported methods of data visualization.

Beyond Paper Types: How to Evaluate and Communicate VIS Research Contributions

Monday, October 21: 9:00am-12:20pm

Bongshin Lee, Microsoft Research
Petra Isenberg, Inria
John Stasko, Georgia Institute of Technology
Daniel Weiskopf, University of Stuttgart
Ross Maciejewski, Arizona State University

Peer review plays a critical role in ensuring and improving the quality of academic research publications, and has a huge influence on culture of the research community. However, compared to the amount of advice and training one can find on how to write a research paper, little training or material is available on how to provide constructive feedback as a reviewer. The goal of our tutorial is to teach basic yet practical skills needed to evaluate visualization research papers, focusing on research contributions, and convey their assessments in a constructive way. Students can use this skill not only to write good reviews but also to better frame their own papers as an author.

Visualization for Social Good

Monday, October 21: 2:20pm-5:40pm

Leilani Battle, University of Maryland
Michelle A. Borkin, Northeastern University
Michael Correll, Tableau Research
Lane Harrison, Worcester Polytechnic Institute
Evan Peck, Bucknell University

We propose a tutorial based around the premise that visualization researchers, teachers, and practitioners can and should use the explanatory and persuasive power of visualizations to promote social good in their communities and the wider world. The skills required to make these sorts of impacts are often excluded from standard visualization curricula, requiring considerations of presenting data to diverse audiences, working with uncertain data sources, and examining the ethical impacts of data collection and analysis. This tutorial is centered around a hackathon event in which attendees will work together with their complementary skillsets to produce any one of a number of potential contributions to a social issue: a novel visualization, a novel dataset, or even just a new perspective on an existing problem.

User-Centred Evaluation in Visualization

Monday, October 21: 2:20pm-5:40pm

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, 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 the fundamental insights in the subject, the tutorial also seeks to encourage the participants to further deepen their knowledge after completion of the tutorial by self-studies or participating in a course.