eagereyes

The academic visualization community largely comes from computer science: most of the professors teach in computer science (or similar) departments, most of the students doing research are computer science students of some flavor or other. It's interesting to consider how the field might be different if visualization had emerged from a different discipline.

This is a guest posting by my Tableau Research colleague Michael Correll.

I recently attended CHI 2018 in Montréal, QC. Normally conferences leave me excited for the next idea or the next direction (and also physically exhausted). This was one of the first where I was left feeling terrified: A lot of the work did an excellent job of highlighting core problems about our assumptions as visualization researchers, and poked at big intractable issues that I had mostly been ignoring for a long time. There were also exactly 666 accepted papers, which is not a good omen either.

It can be hard to get excited about the standard datasets that we keep using to show how visualization and statistics work. But if that's the case for you, it's not the datasets's fault, it's you! Here’s how to keep that spark going!