Course support material overview
Here you find supporting material for the IMSCIA C21 – Data Visualization course.
You will find the course bibliography, some more general reading material and pointers to software for testing.
Then there are the lecture specific materials:
L01 – Visualization fundamentals
L02 – Prepare and know your data
L03 – Visualization flavors
L04 – Scientific visualization
L05 – Information visualization 1
L06 – Information visualization 2
L07 – Non conventional techniques
L08 – See and act
L09 – Visual communication
L10 – Choice and evaluation
Same of the links are credits to the source of images or information; other interesting additional data ad references. Please report any broken link or new references you think are worthwhile.
Now you can return to the C21 course page.
Course bibliography
- Information Visualization: Perception for Design, 2nd edition, Colin Ware, Morgan Kaufman (2004)
- Visual Explanations – Images and Quantities, Evidence and Narrative, 3rd edition, Edward R. Tufte, Graphics Press (February 1997)
- The Visual Display of Quantitative Information, 2nd edition, Edward R. Tufte, Graphics Press (May 2001)
- Envisioning Information, Edward R. Tufte, Graphics Press (May 1990)
- 14 Ways to Say Nothing with Scientific Visualization, A. Globus, E. Raible, IEEE Computer, Vol. 27, No. 7, pp. 86-88; July 1994.
- The Visualization Toolkit – An Object-Oriented Approach To 3D Graphics by Will Schroeder, Ken Martin, Bill Lorensen, 3rd Edition 2003 Kitware, Inc.
- Scientific Visualization: Advances and Challenges,
L. Rosenblum, R.A. Earnshaw, J. Encarnacao, H. Hagen, A. Kaufman, S. Klimenko, G. Nielson, F. Post, D. Thalmann (eds), 1994 Academic Press.
Part II, IV (especially chapters 15 and 18), VI (only chapters 23 and 26).
- Scientific Visualization, G. Nielson, H. Hagen, H. Müller (eds), 1997 IEEE Computer Society Press.
Chapters: 1, 2, 7, 8, 9, 10
Other reading material
- Information Visualization, Robert Spence, Addison-Wesley (2001)
- Visual cues: practical data visualization, Peter R. Keller, Mary M. Keller – Los Alamitos, CA IEEE Computer Society Press [etc.] cop. 1993
A little old, but has some really interesting ideas on visualization selection.
- Readings in information visualization : using vision to think, Stuart K. Card, Jock D. Mackinlay, Ben Shneiderman
San Francisco, Calif. – 1999 Morgan Kaufmann Publishers
It is a compilation of papers on Information Visualization.
- Curriculum for Visualization (SIGGRAPH Education Committee).
Software for testing
Free
- XGobi
- Information visualization.
- GGobi
- A more recent version of XGobi.
- XmdvTool
- Information visualization tool.
- VTK
- Visualization toolkit.
- ParaView
- Scientific visualization tool for end user.
- Gnuplot
- Plotting package.
Commercial
- Matlab
- End User tool plus some visualization component.
- AVS/Express
- Visualization development system geared toward scientific visualization.
- Spotfire
- Information visualization end user tool.
L1 – Visualization fundamentals
PA – Visualization definition and goals
[status: recorded, slides: 31]
- Richard Hamming.
- Visualization in Scientific Computing, B. McCormick, T. DeFanti, and M. Brown eds., ACM Siggraph Computer Graphic, vol. 21, no. 6, Nov. 1987, p. 3
- John Snow cited by Tufte in Visual Explanations.
- Tufte, Visual Explanations for Playfair and Michael Florent.
- Kekulè dream.
- The history of “a picture’s worth…rdquo;
- Why Visualize? We think this Quicktime animation (2.6 MB) about visualizing plaque on an artery wall tells the story quite well. It shows the stages in going from the original 2D ultrasound scans to a full color 3D representation of the artery – which is much easier to interpret and understand. This visualization was created by Colin Currie, Dept. of Medicine, University of Sydney.
Other material
- Exploratory Experimentation: Goethe, Land, and Color Theory, Neil Ribe and Friedrich Steinle – Physics Today July 2002 p. 43-49
Usually the researcher uses the experiment to prove or disprove some theory. This is the usual theory oriented experimentation. Instead the exploratory experimentation defining characteristic is the systematic and extensive variation of experimental conditions to discover which of them influence or are necessary to the phenomena under study.
- An Atlas of Cyberspaces
- Gallery of Data Visualization.
PB – Visualization reference model
[status: recorded, slides: 18]
- Reference model: Systematic Approaches to Visualization: Is a Reference Model Needed? P. Robertson and L. De Ferrari, in Scientific Visualization, 1994, Advances and Challenges, Ed: L. Rosenblum, R.A. Earnshaw, J. Encarnacao, H. Hagen, A. Kaufman, S. Klimenko, G. Nielson, F. Post, D. Thalmann , Academic Press.
- Visualization Pipeline: Visualization Idioms: A Conceptual Model for Scientific Visualization Systems, R.B. Haber, and D. A. McNabb, in Visualization in Scientific Computing, G. M. Nielson, B. Shriver and L.J. Rosenblum (eds), IEEE Computer Society Press.
- What Is VTK?
- AVS/Express introduction.
PC – Human visual perception issues
[status: recorded, slides: 36]
- Optical Illusions (Italian).
- The Joy of Visual Perception.
- Gestalt Theory.
L2 – Prepare and know your data
PA – Logical data types
[status: recorded, slides: 26]
- Software horror stories.
- Collection of Software Bugs by Prof. Thomas Huckle (Institut für Informatik TU München).
- Example of data without inherent geometry.
- Scales of Measurement from “On the Theory of Measurement”, S.S. Stevens, Science, 103, pp.677-680. 1946.
- One example of logical data structure.
- Example of data fusion.
PB – Physical data formats
[status: recorded, slides: 16]
- HDF5 page.
- XML and, of course, the World Wide Web Consortium standardization effort for XML.
- An interesting discussion on scientific data format requirements.
- Mostly Data Formats and Storage contains pointers to other data formats definitions.
- Other XML based scientific data formats.
- Scientific Data Format Information FAQ.
PC – Scientific data management
[status: recorded, slides: 15]
- NEESgrid data repository project. Contains interesting references to metadata harvesting and general information on the data management problems in scientific research.
- “Designing Metadata for the NEESgrid Data repository”. April 2003
[PPT]
This presentation contains an introduction to metadata and the comparison of old (one shot) and new (reuse) research methods. - Ensembl Genome Browser.
- Protein Data Bank.
Other material
- My collection of Scientific Data Management material.
- PubGene.
- “Building a Repository of Distributed, Heterogeneous Scientific Data for NEESgrid”. November 2001
[PPT]
Introduces the kind of research enabled by cheap data storage.
- “Technology Strategies for Integrating Scientific Data Collections”. November 2001
[PPT]
A brief explanation of XML technologies related to metadata plus federation and harvesting pros and cons.
PD – Operative suggestions
[status: recorded, slides: 11]
- Metacrap: Putting the torch to seven straw-men of the meta-utopia.
- Beyond FITS. A discussion about scientific data format requirements for astronomy.
- Metadata training for a specific sector.
L3 – Visualization flavors
PA – Application fields subdivision
[status: recorded, slides: 13]
- Does the differences between Information and Scientific Visualization Really Matter? IEEE Computer Graphic and Applications vol. 23, no. 3 May/June 2003.
PB – Traditional techniques
[status: recorded, slides: 37]
Other material
- Chart usage in the SETI@home project.
Check the Science Newsletter and especially the issues:
#4 Multiple Detection Search (June 19, 2000)
#6 Distinguishing Possible ET Signals From Noise and RFI (February 28, 2001)
#7 Examining Radio Signal Spikes Using Clickplots (May 4, 2001)
PC – Other traditional techniques
[status: recorded, slides: 14]
L4 – Scientific visualization
PA – Introduction to scientific visualization
[status: recorded, slides: 7]
PB – Color for scalar data visualization
[status: recorded, slides: 35]
- Color Principles – Hue, Saturation, and Value.
- Digital Image Processing 2nd edition, W. K. Pratt – Wiley Interscience 1991 (fig. 2.5-4).
- Why Should Engineers and Scientists Be Worried About Color? Bernice E. Rogowitz and Lloyd A. Treinish, IBM Thomas J. Watson Research Center Yorktown Heights, NY.
- Color illusions.
- Meteorological color map.
- Daltonize the colors used to check effect on colorblind people.
- Color Use Guidelines for Data Representation
- Color Brewer to study color schemas.
- Preattentive Processing Introduction.
- Perceptual Principles for Effective Visualizations.
- Perceptual Issues in Visualization, Georges Grinstein and Haim Levkowitz, eds. Springer-Verlag, pp. 59-74.
PC – Scalar data visualization
[status: recorded, slides: 16]
- Catalog of Visualization Techniques. It is based on AVS/Express and introduces the more common scientific visualization techniques.
PD – Vector and tensor visualization
[status: recorded, slides: 17]
- Lagrangian-Eulerian Advection applied to Meteorological application and the MeteoSwiss 72 hours forecast wind textures.
- The Care and Feeding of Vector Fields.
- Image Based Flow Visualization is a nice tool to experiment interactively with flow field visualization techniques.
PE – Spatial visual cues
[status: recorded, slides: 16]
- Applications of the Method of Perspective in Renaissance Art.
- 3D Magic Eye. How to See 3D.
Other material
L5 – Information visualization 1
PA – Introduction to information visualization
[status: recorded, slides: 9]
- Charles Minard’s depiction of Napoleon’s 1815 March on Moscow and some revisiting of this famous map.
Other material
PB – Multidimensional techniques
[status: recorded, slides: 11]
PC – Geometric techniques
[status: recorded, slides: 9]
- Parallel coordinates applied to material science.
- A case study on ethnography of stockbrokers and their trading methods.
- The father of Parallel Coordinates.
PD – Icon and pixel based techniques
[status: recorded, slides: 12]
PE – Navigation strategies
[status: recorded, slides: 10]
PF – Dynamic techniques
[status: recorded, slides: 8]
L6 – Information visualization 2
PA – Trees and hierarchies
[status: recorded, slides: 25]
- Treemaps.
- T. Barlow and P. Neville, Comparison of 2D Visualizations of Hierarchies.
- Smartmoney.
- Coffee Selector.
- Million Items Treemap.
- SunBurst.
- Botanical Visualization of Huge Hierarchies.
PB – Graphs and networks
[status: recorded, slides: 15]
- H3 hyperbolic browser.
- Network Visualization: case studies.
- The Opte Project – Map of The Internet.
- Graph Drawing links.
- Graph Drawing Software Series: Mathematics and Visualization Jünger, Michael; Mutzel, Petra (Eds.) 2004, XII, 378 p. 220 illus., 183 in color., Hardcover ISBN: 3-540-00881-0 Springer-Verlag
- If you want to see a really huge network, take a look at the metabolic paths chart.
- An example of interaction map with the related article.
L7 – Non conventional techniques
PA – Change your frame of mind
[status: recorded, slides: 26]
- Nine Dots Game.
- In the 1960’s the Scientific Subroutine Library on IBM mainframe computers included a random number generator named RND or RANDU. It was a multiplicative congruential with parameters a = 65539, c = 0, and m = 231. With a 32-bit integer word size, arithmetic mod 231 can be done quickly. Furthermore, because a = 216 + 3, the multiplication by a can be done with a shift and an addition. Such considerations were important on the computers of that era, but they gave the resulting sequence a very undesirable property: there is an extremely high correlation among three successive random integers of the sequence generated by RANDU.
- Convolution in 1-D does convolution in 2-D. Deconvolution in 1-D does deconvolution in 2-D. The “Helix” paper.
- Diagram to 3D geons.
- Ulam Spiral. This construction was first made by Polish-American mathematician Stanislaw Ulam (1909-1986) in 1963 while doodling during a boring talk at a scientific meeting. While drawing a grid of lines, he decided to number the intersections according to a spiral pattern, and then began circling the numbers in the spiral that were primes. Surprisingly, the circled primes appeared to fall along a number of diagonal straight lines or, in Ulam’s slightly more formal prose, it “appears to exhibit a strongly nonrandom appearance” (Stein et al. 1964). The spiral appeared on the March 1964 cover of Scientific American magazine.
- Carlis, J., Konstan, J., “Interactive visualization of serial periodic data”, Proc. ACM UIST '98, 29-38.
- Mastering Interactive Virtual Bronchioscopy on a Low-End PC, by Rainer Wegenkittl, Anna Vilanova, Balint Hegedüs, Daniel Wagner, Martin C. Freund, and Eduard Gröller. Published in IEEE Visualization 2000, Conference Proceedings, pages 461-464, October 2000.
- GeneVis: info & sci viz techniques.
- Cartography applied to non cartographic info.
- Global Visualization and Alignments of Whole Bacterial Genomes, Pak Chung Wong, Kwong Kwok Wong, Harlan Foote, and Jim Thomas. IEEE Transactions on Visualization and Computer Graphics, Vol. 9, No. 3, Jul-Sep 2003.
- VideoCube paper and application.
Other material
- This picture explains the relation between 1-D and 2-D filtering. To filter, you screw the filter onto the data. Convolution in 1-D does convolution in 2-D. Deconvolution in 1-D does deconvolution in 2-D. Likewise for spectral factorization, etc. Makes a great preconditioner for inversion. Rapidly solves [1+Dxx+Dyy]u=v. The entire “Helix” paper (16 pages of .ps) or html.
- SOM-Based Data Visualization Methods. Juha Vesanto (1999). In Intelligent Data Analysis, Volume 3, Number 2, Elsevier Science, pp. 111-126. © 1999 IOS Press. By permission.
PB – Visualization for assimilation
[status: recorded, slides: 14]
- Chemical elements periodic table.
- ThemeRiver and ThemeView. Now they are called Story Flow.
- PhotoMesa.
- Tarantula software test results visualization.
Other material
PC – Graphical visual thinking techniques
[status: recorded, slides: 23]
- Jim Foley Getting There: Top Ten Problems Left, IEEE Computer Graphics and Applications – January 2000, pp. 66-68
- Mindmap introduction.
- Concept mapping. A nice example of concept map used to announce a Concept Mapping Conference.
- Another article on Conceptual Maps.
Other material
- Creating
Creativity for Everyone: User Interfaces for Supporting Innovation, Ben Shneiderman (February 1999).
A challenge for human-computer interaction researchers and user interface designers is to construct information technologies that support creativity. This ambitious goal can be attained by building on an adequate understanding of creative processes. This paper offers the four-phase genex framework for generating excellence: – Collect: learn from previous works stored in digital libraries – Relate: consult with peers and mentors at early, middle and late stages – Create: explore, compose, and evaluate possible solutions – Donate: disseminate the results and contribute to the digital libraries Within this integrated framework, this paper proposes eight activities that require human-computer interaction research and advanced user interface design. A scenario about an architect illustrates the process of creative work within a genex environment.
- Models for the Creative Process by Paul E. Plsek (1996).
L8 – See and act
PA – Interaction methods
[status: recorded, slides: 28]
- Investigating the effect of texture orientation on shape perception Victoria Interrante – Department of Computer Science and Engineering – University of Minnesota.
- Power of Ten explanation and interactive applet. View the Milky Way at 10 million light years from the Earth. Then move through space towards the Earth in successive orders of magnitude until you reach a tall oak tree just outside the buildings of the National High Magnetic Field Laboratory in Tallahassee, Florida. After that, begin to move from the actual size of a leaf into a microscopic world that reveals leaf cell walls, the cell nucleus, chromatin, DNA and finally, into the subatomic universe of electrons and protons.
- Collaborative AVS. An example of collaborative visualization environment.
- Multidimensional Grand Tour methods. Especially interesting the original one (compressed Postscript).
- Risk of dequantification: the Venus Globe animation.
Other material
- Visualizing Visualizations: User Interfaces for Management and Exploration of Scientific Visualization Data, IEEE Computer Graphics and Applications, September/October 2000.
- Movement in Visualisation from the really nice magazine of InfoVis.net: a page devoted to Information Visualization.
PB – Direct manipulation methods
[status: recorded, slides: 17]
- A Spreadsheet Interface for Visualization Exploration.
- Works of Ben Shneiderman on dynamic queries.
PC – Operative visualizations
[status: recorded, slides: 21]
- Information availability in 2D and 3D displays – IEEE Computer Graphic and Applications, September 2001 p. 51-57.
- Recognition of Descending Aircraft in a Perspective Naval Combat Display.
- Management Dashboards.
- SYNELEC produces big displays for control rooms.
- Digital Dashboards.
- Curing Information Overload With Digital Dashboards.
PD – User-in-the-loop
[status: recorded, slides: 18]
- H. Wright, K. W. Brodlie and M. J. Brown, The Dataflow Visualization Pipeline as a Problem Solving Environment, in Virtual Environments and Scientific Visualization '96, edited by M. Gobel, J. David, P. Slavik and J.J. van Wijk, pp 267 – 276, Springer-Verlag, Wien and New York.
L9 – Visual communication
PA – Visualize to communicate
[status: recorded, slides: 17]
Other material
- The Contribution of the Artist to Scientific Visualization by Vibeke Sorensen – School of Film and Video California Institute of the Arts (1989).
- 14 Ways to Say Nothing with Scientific Visualization, A. Globus, E. Raible, IEEE Computer, Vol. 27, No. 7, pp. 86-88; July 1994.
- Principles of Information Display for Visualization Practitioners by Al Globus, CSC @ NASA Ames Research Center 28 November 1994.
- Articles from the Vol.33 No.3 August 1999 SIGGRAPH newsletter especially the article of Alan Davies Bad Graphs, Good Lessons on the “creative” usage of charts by corporation annual reports.
PB – Having something to say
[status: recorded, slides: 14]
- Good and Worst charts.
PC – Be clear
[status: recorded, slides: 27]
- Wayne Lytle — The Danger of Glitziness and Other Visualization Faux Pas – SIGGRAPH 1993.
- Mapping Votes by County. County maps and the 2003 California Statewide Special Election by Jonathan Corum.
PD – Don’t lie
[status: recorded, slides: 23]
- Misleading Visualizations by Henrik Ingo.
“The Post-Enron world with its tough requirements on reliable financial reports has left US accountants and CEOs without choice. They will now have to tell us the truth and nothing but the truth about their companies financial status. Or do they?” A story about pictures that lie… - THE ANCIENT CHINESE ART OF CHI-TING. Jim Blinn explain why computer graphic must cheat (chi-ting) sometimes.
- How to display data badly, Howard Wainer, The American Statistician 1983. Especially interesting is the online Wainer lesson.
PE – Know the medium
[status: recorded, slides: 22]
- Tele-Manufacturing Facility Project.
- Horton, W., Top Ten Blunders by Visual Designers, SIGGRAPH Computer Graphics, Nov 1995 p. 20-24.
- Color Brewer to choose color schemes based on the output device.
L10 – Choice and evaluation
PA – Choose a visualization
[status: recorded, slides: 22]
PB – Visualization evaluation
[status: recorded, slides: 28]
- CAIB Report.
- EPFL Supercomputing Review on the Alinghi boat design process and an interview with prof. Quarteroni on the mathematics behind Alinghi success (Italian).
- For standard testing datasets there is the StatLib Datasets Archive for multidimensional data.
- The Lenna Test Image Story and A Brief History of The Utah Teapot (the most famous teapot).
- The pollen synthetic dataset about the geometric features of pollen grains. There are 3848 observations on 5 variables. From the 1986 ASA Data Exposition dataset, made up by David Coleman of RCA Labs. Some hints for the results to expect are in the accompanying file pollen.extra and in the Wegman article.
Other material
- Evaluation of Visualization Software Al Globus and Sam Uselton.
- VizLies sessions.
PC – Future
[status: recorded, slides: 15]
Other material
- Self-organizing maps (SOMs) are a data visualization technique invented by Professor Teuvo Kohonen which reduce the dimensions of data through the use of self-organizing neural networks.