Datavisualization.ch is keeping a list of the tools that they “use to create interactive and dynamic data visualizations:”

That’s why we have put together a selection of tools that we use the most and that we enjoy working with. We called it selection.datavisualization.ch. It includes libraries for plotting data on maps, frameworks for creating charts, graphs and diagrams and tools to simplify the handling of data. Even if you’re not into programming, you’ll find applications that can be used without writing one single line of code. We will keep this list as a living repository and add / remove things as technology develops. We hope this will help you find the best tool for your next job.

I have mentioned many of these tools like ColorBrewer (many times), Processing, Processing.js, and Many Eyes.

Pathline is a visualization tool for genetic data created in collaboration between designers and geneticists:

We recently attended an interdisciplinary visualization workshop that was all about creating a dialogue between scientists, technologists and designers. It was interesting to discuss the different ways in which these groups think about visualization and how they use it for different purposes. Very bluntly put, each group lacks something another group knows and cares deeply about, be it an understanding of colour [we met in the UK] or an understanding of statistics.

The before and after images show a striking simplification that apparently helps even trained geneticists:

Two main advantages of the new tool were found. First, there was a massive gain in efficiency. The study of a heatmap took up to a half-hour before but can be done at a glance at the curvemap now. More importantly, though, the scientists made new discoveries of gene properties they didn’t know about before. What was hidden in the data before, is now very clear, even to an untrained eye.

I’ve been catching up on Brian Hayes’ great “Computing Science” column in American Scientist. The last issue’s “Pixels or Perish” discusses the move to interactive graphics from static print graphics:

Methods for producing scientific illustrations—and for reproducing them in publications—have been changing. Printing plates for figures were once engraved by hand, then made by a photographic process, and in recent years have been created by digital techniques. Now we are about to turn the page—if not close the book—on yet another chapter in publishing history. After centuries of reading and writing on paper, we seem to be headed for a world where most documents will be distributed online and viewed on a display screen of some kind. How will this transition to a new medium affect the practice of scientific illustration?

via FlowingData

NASA is hosting a series of challenges relating to pulling information from its collection of over 100 terabytes of data stored in its planetary data system (PDS):

But, while rich in depth and breath, the PDS databases have developed in a disparate fashion over the years with different architectures and formats for different scientific needs; thereby making acquisition of data problematic!

So, NASA is holding a series of Challenges to generate some simply awesome ideas for mobile or web based applications that will appeal to general users, to search and display compelling facts about the data. Instead of just scientists, our audience will be the millions of school age students, their teachers and parents, game designers and general civilians of the world. We want to deliver this incredible data to users in a way that excites them – and thus, to help them understand the value and potential of this data.

Prizes range from $500 to $10,000, as well as being named “Space Coder of the Galaxy 2012″. Some contests are restricted to teachers or high school students.

The winners of the NSF’s 2010 International Science and Engineering Visualization Challenge were announced today.

See the past winners: 2009, 2008, 2007, 2006, 2005, 2004, 2003.

Link via Wired Science.

MetaOptimize has a forum for asking and answering questions about data visualization, like comp.lang.idl-pvwave for visualization issues:

Where data geeks ask and answer questions on machine learning, natural language processing, artificial intelligence, text analysis, information retrieval, search, data mining, statistical modeling, and data visualization!

Haven’t asked/answered a question yet, but it looks like a good resource.

Link via FlowingData.

I have wanted to create something like this for awhile.

Link via Chart Porn.

The next visualization type summarized by Juice Analytics in their excellent “Better know a visualization” series: motion charts.

Motion charts are essentially animated bubble charts. A bubble chart shows data using the x-axis, y-axis, and the size and color of the bubble. A motion chart displays changes over time by showing movement within the two-dimensional space and changes in the size and color of the bubbles.

Motion charts are used quite effectively by Hans Rosling in his great TED talks.

xkcd just released the results of a color survey it ran for a month. It contains many interesting, and funny, results. Specifically, it reveals a list of 949 color names with matching values that were frequently used by participants.

I’ve added the xkcd color names to my vis_color routine, so that now you can do things like this:

IDL> erase, vis_color('booger', /index, /xkcd)

You will need vis_color.pro (docs), vis_index2rgb.pro, xkcdcolors.dat, and htmlcolors.dat. The default is to use HTML color names if you don’t set the XKCD keyword. If you want the entire vis library, you can grab it via Subversion:

$ svn co http://svn.idldev.com/vis/trunk vis

Another great overview of a visualization technique by Juice Analytics—this time the misunderstood parallel coordinates. I particularly like the clearly written “What problem does this solve?” section of these posts.