When you are doing research, it can feel like looking at your data is the last thing
you do, after you figure out your algorithm or set up your apparatus. We spend a
lot of time, though, in a cycle where we take a look at data, tweak something in
the experiment, and check out the data again.
Watson's model of scientific investigation, shown above,
stresses that the goal of visualization is the generation of new insights.
The key to achieving that goal is keeping the user at the center of the cycle.
Practically, that means increasing the mobility of data to visualization and increasing
the expressiveness of the visualizations.
While the basic goal of this workshop is to list some of the more common plotting
and visualization packages on Windows, it will try to show examples of how those
packages can work more closely with your scientific programs. We won't cover what
makes an image informative, but we will try to rank tools based on how they fit
in with the way that scientists work.
- How quickly can you recompute your data and get another look at it?
- Can you play with all of your data at once and try new views of it?
- Will you be able to figure out later what it is that you did?
- How long will it take to learn this tool?
Insight is the measure of good visualization.
