ACM SIGGRAPH Education Committee - Visualization

Visualization Curriculum Workshop

wobbe — Sat, 19 May 2012 04:00:00 +0000

On May 19, 2012, a „Visualization Curriculum Workshop“ was held at the University of Cagliari, Sardinia, sponsored by the SIGGRAPH Education Committee. It was a follow-up on the Visualization Curriculum Panel at Eurographics 2012. Participants were David Ebert, Beatriz Sousa Santos, Holly Rushmeier and Gitta Domik.

During the morning, workshop participants followed up on the issues as discussed at the Eurographics Panel “Visualization Curriculum Panel – or the Changes We Have Made to Our Visualization Courses over the Last 10 Years”. Since the early 1990s, teaching Visualization has been part of the Computer Science curriculum at many universities world-wide, but applications, methods and technologies have changed since then. It seems currently appropriate to make a distinction between

data visualization,
information visualization and
visual analytics

though many commonalities exist and all of these can be summarized under the heading of “visualization”. To shortly summarize some significant data differences,

in data visualization, data dimensions usually coincide with physical dimensions, such as in medical or remote sensing or flow data,
information visualization typically deals with multidimensional data as in finances, business intelligence, or large databases and mostly physical dimensions are not present
in visual analytics, data is characterized by large, complex, and heterogeneous data sets.

The rest of the workshop was spent in discussing the following proposed outline of the following 11 themes to teach “visualization”, where additions and distinctions must be used if focusing on a specific type of visualization.

The 10 themes were taken from previous curriculum suggestions and are as follows:

Definitions and History of Visualization.
Data. This includes models, transformations, data characterization as well as data manipulations
The User. This includes the human information processing limitations and capabilities as well as the task that a user brings to the visualization problems
Design stage. This stage describes a careful mapping of data components to visual attributes
Visual Presentation Techniques. This should include a wealth of visualization solutions, sorted by data characteristics, by application domain, and/or by task, and described by their various parameters. This theme can be presented at the breath-level by showing and discussing (interactive) visualizations. It can be trained by using available tools at the breadth-level or in-depth by developing interactive visualization techniques on a GPU, or in any breadth/depth stage in between, depending on the qualifications of students.
Interaction techniques. While interaction techniques are a demand per se for Visual Analytics, they are increasingly also present and demanded for data and information visualization, where GPU techniques can reach the necessary processing speed.
Communication. A focus on production, presentation and dissemination as part of the visualization process was raised by visual analytics, but needs to be observed as well for data visualization and information visualization.
Collaboration. Interactivity aids collaboration, but other issues are of concern in the collaboration among stakeholders.
Evaluation. Evaluation is seen as a continuous process, starting with the requirement analysis of the visualization problem, continuing as a constant awareness process of the human-in-the-loop of software processes towards a visualization goal, and ending with evaluation techniques to secure reaching this goal for the specific visualization problem.
Displays. Different capabilities of displays (size, memory, processors) pose problems on visualization techniques, interactivity and communicaton and need to be treated at least at the Mapping/ Design stage.

It was decided to have these preliminary discussions continued at the BOF "Visualization and Visual Analytics Curriculum" at SIGGRAPH 2012.

Volume Visualization Data Sets

wobbe — Tue, 11 Apr 2000 04:00:00 +0000

The Chapel Hill Volume Rendering Test Data Set, Volume I

These were originally on an ftp site at the University of North Carolina, Chapel Hill.

SoftLab Software Systems Laboratory
University of North Carolina
Department of Computer Science
Chapel Hill, NC 27599-3175

The Chapel Hill Volume Rendering Test Data Set, Volume I is a collection of the following files:

Head data (zip file of 9.2 mbytes)
A 109-slice MRI data set of a human head. Complete slices are stored consecutively as a 256 x 256 array. Pixels consist of 2 consecutive bytes making one binary integer. Data taken on the Siemens Magnetom and provided courtesy of Siemens Medical Systems, Inc., Iselin, NJ.

Head data information article - An ASCII file containing acknowledgements for the head data files.

Knee data (zip file of 12.2 mbytes)
A 127-slice MRI data set of a human knee. Complete slices are stored consecutively as a 256 x 256 array. Pixels consist of 2 consecutive bytes making one binary integer. Data taken on the Siemens Magnetom and provided courtesy of Siemens Medical Systems, Inc., Iselin, NJ.

Knee data information article - An ASCII file containing acknowledgements for the knee data files.

CT Cadaver Head data (zip file of 7.4 mbytes)
A 113-slice MRI data set of a CT study of a cadaver head. Slices are stored consecutively as a 256 x 256 array with dimensions of z-113 y-256 x-256 in z-y-x order. Format is 16-bit integers -- two consecutive bytes make up one binary integer. 14,811,136 bytes total file size. Data taken on the General Electric CT Scanner and provided courtesy of North Carolina Memorial Hospital.

CT Cadaver Head data information article - An ascii file containing acknowledgements for the CT cadaver head data files.

MR Brain data (zip file of 7.9 mbytes)
A 109-slice MRI data set of a head with skull partially removed to reveal brain. 256 x 256 array. with dimensions of Z=109 Y=256 X=256 in z-y-x order. Format is 16-bit integers -- two consecutive bytes make up one binary integer. 14,286,848 bytes total file size. Data taken on the Siemens Magnetom and provided courtesy of Siemens Medical Systems, Inc., Iselin, NJ. Data edited (skull removed) by Dr. Julian Rosenman, North Carolina Memorial Hospital.

MR Brain data information article - An ascii file containing acknowledgements for the MR brain data files.

The data sets were written on a Digital Equipment Corporation (DEC) VAX computer. Each file contains only pixels, stored in row major order with 2-byte integers per pixel. To use the images on machines that have normal byte order (DECs use reverse byte order), you should swap alternate bytes, for example using the 'dd' command in UNIX. A sample command that does this for the 3dknee data set is:

% dd if=3dknee of=3dknee.new conv=swab

We do not object to your further distributing these files, but we request that full acknowledgement of the source of the data accompany such distribution. If you are going to send a data set to someone, please also send the accompanying information file (*.info) and this file (Announcement).

The Computer Science Department, University of North Carolina only distributes these files by anonymous FTP.

We do not provide any software for displaying these data.

There is no information available about the data provided here other than that present in these files. For example, missing information about the means of data collection cannot be provided.

HyperVis - Teaching Scientific Visualization

wobbe — Sun, 24 Oct 1999 04:00:00 +0000

A project of the ACM SIGGRAPH Education Committee, the National Science Foundation (DUE-9752398), and the Hypermedia and Visualization Laboratory, Georgia State University.

Project Director: G. Scott Owen

This resources has been archived and can still be accessed here

Note: moved to new location July 2019

Undergraduate Faculty Enhancement Workshop in Scientific Visualization

wobbe — Wed, 21 May 1997 04:00:00 +0000

Detailed workshop outline

1 Definitions and Goals of Visualization

For any visualization course it is important to discuss background, definitions, and goals in order to provide a common understanding of visualization.

We will discuss the following subtopics in the tutorial:

History of (scientific) visualization
Definitions of visualization
Goals of visualization

2 Abstract Visualization Concepts

It is necessary to establish a framework for the use of visualization. Participants should learn how to make use of concepts and paradigms, specifically of the ones they are not yet familiar with (e.g., paradigms from Fine Arts for Computer Science students). We will discuss the following subtopics in the tutorial:

General visualization models and taxonomy
Examples of specific visualization models and paradigms

3 Human Perception Concepts

This section will enhance the understanding of how to use graphics tools to support human perception in order to gain insight into phenomena that we seek to interpret. We will discuss the following subtopics in the tutorial:

The human visual system (biological, psychophysical and cognitive issues, visual phenomena, texture and color perception)
Perception theories
Presenting complex information to the human visual system (e.g. data exploration, natural computing, integrated displays, using senses additional to vision)
Practical considerations (e.g. expressiveness, effectiveness, interactivity, annotations, avoiding pitfalls)
Evaluation methods

4 Scientific Methods and Concepts

This theme explains the relationship between the 'real world' and the 'models' we have available in order to understand the real world and the 'empirical (data) measurements' we have of the real world. Non-science students have usually little approach to models, data concepts and reality. We will discuss the following subtopics in the tutorial:

Scientific concepts: what is a model; model vs. acquiring; going from macro-to micro worlds
Modeling concepts: mathematical methods to represent reality; mathematical concepts; computational models
Data concepts: how to represent reality; data collections; errors

5 Aspects of Data

Various aspects of data, such as acquisition, classification, storage and retrieval of data, will be discussed. Appropriate subtopics are

Acquisition of data (Simulation vs. measuring devices)
Discipline-independent classification of information sources
Data base issues
Query languages
Reliability of data

6 Visualization Techniques

This section provides tutorial participants with a wealth of ideas for visual representations and teaches them how to apply appropriate tools. We will discuss 2-d, 3-d and multi-dimensional visualization techniques, such as color transformations, glyphs for high dimensional data sets, visualization of gaseous and fluid information, volume rendering, isolines and isosurfaces, coloring, particle tracing, animation, techniques in virtual environments, and interactive steering.

7 Interaction Issues

Interaction techniques are fundamental to the design and use of visualization systems. We will discuss interaction from the view point of ergonometry, HCI and hardware techniques.

8 Existing Visualization Systems/Tools

Available visualization systems will be discussed.

9 Aesthetics in Visualization

The following subtopics will be discussed:

Aspects of successful visualizations
Comparison of good and bad visual representations

10 Related Topics

A visualization course might include fundamental aspects of mathematics and computer science. The presentation of appropriate subtopics depends on the objectives of the course and the background of the students. Appropriate subtopics may be:

Mathematical techniques (e.g. vectors, matrices, interpolation approximation, transformations for 2- and 3-d, parametric versus implicit versus explicit representations, curves, surfaces, fractals);
Computer graphics (e.g. 2-d drawing, clipping, filling; 3-d modeling, rendering, lighting; transparency, translucency; raytracing, radiosity, volume rendering; graphics standards and libraries)
General computer science (e.g. user interface design; computational geometry; computer hardware architectures, input/output technologies; data structures, data models, data formats, data transfer; programming languages)

Visualizing Scientific Data and Information

wobbe — Fri, 09 Aug 1996 04:00:01 +0000

Focusing on the Physical and Natural Sciences

SIGGRAPH 96 Course 16

Theresa-Marie Rhyne (organizer), Bill Hibbard, Mike Botts, Lloyd Treinish

PDF
Movies: Mpeg / Mov

Original Course Description:

This course demonstrates the application of visualization tools and interactive techniques for examination and interpretation of scientific datasets. Highly illustrative atmospheric, oceanographic, and geographic examples are demonstrated in real time. Also highlighted: the process of developing effective visualization paradigms for supporting high-speed networking, database management, heterogenous computing platforms, user interface design, collaborative computing, science education, implementation of animation techniques, convergence of visualization methods with the World Wide Web, and the relationship between animation techniques and scientific information exploration.

Who Should Attend

Scientific researchers, educators, and computer graphics specialists interested in exploring particular issues associated with handling visual display of scientific information and large scientific datasets. Experience with scientific visualization systems and terminology is helpful, as well as understanding of graphics programming.

ACM SIGGRAPH Education Committee - Visualization

Visualization Curriculum Workshop

Tags:

Volume Visualization Data Sets

The Chapel Hill Volume Rendering Test Data Set, Volume I

Tags:

HyperVis - Teaching Scientific Visualization

Note: moved to new location July 2019

Tags:

Undergraduate Faculty Enhancement Workshop in Scientific Visualization

Sponsored by the ACM SIGGRAPH Education Committee, the National Science Foundation (DUE-9554692), and Georgia State University

Dates: August 18-22, 1997
Location: Georgia State University, Atlanta, Georgia

Detailed workshop outline

Tags:

Visualizing Scientific Data and Information

SIGGRAPH 96 Course 16

Original Course Description:

Who Should Attend

Tags:

ACM SIGGRAPH Education Committee - Visualization

Visualization Curriculum Workshop

Tags:

Volume Visualization Data Sets

The Chapel Hill Volume Rendering Test Data Set, Volume I

Tags:

HyperVis - Teaching Scientific Visualization

Note: moved to new location July 2019

Tags:

Undergraduate Faculty Enhancement Workshop in Scientific Visualization

Sponsored by the ACM SIGGRAPH Education Committee, the National Science Foundation (DUE-9554692), and Georgia State University

Dates: August 18-22, 1997 Location: Georgia State University, Atlanta, Georgia

Detailed workshop outline

Tags:

Visualizing Scientific Data and Information

SIGGRAPH 96 Course 16

Original Course Description:

Who Should Attend

Tags:

Dates: August 18-22, 1997
Location: Georgia State University, Atlanta, Georgia