Tabletop Whale is an original science illustration blog.

Made with love by a biology grad student at the University of Washington. Charts, infographics, and animations about any and all things science.

A field guide to dangerous bacteria

This week I’m excited to announce a special project! During the past year and a half I’ve been working with the wonderful people at Nerdcore Medical on a set of 20 medical infographics.

Nerdcore Medical is the studio behind science communication projects like the Bacterionomicon and the Occam’s Razor card game. For this project I worked with Dr. Arun Mathews, a chief medical officer in Texas.

We wanted to make a set of beautiful posters that would combine art with study guides for medical school exams. This first one is an ID guide to common bacterial infections in human patients. The flowchart shows the steps you would take to identify a bacterial infection, and what each of the results might mean.

I’d never really thought much about lab tests before, so this was a really fun infographic to research and put together. Arun was an amazing collaborator - he fact-checked every single poster, picked the most important medical topics to cover, and even sent me reference books used by medical students.

Most of our posters are pretty specific to the medical field, but I’m going to be posting some of my favorites on this blog as they’re released. If you’re into medical infographics I would definitely recommend checking out the Nerdcore Medical Patreon to see the complete set of our work.


Half past orange: A rainbow color clock

This week’s post is a little less science and a little more design - it’s actually a side project I’ve been working on to practice R and Python.

I thought it would be fun to find an algorithm to define “what Eleanor thinks is a pretty color.” I think generative design is pretty cool, so the idea was to write something I could use to automatically pick colors for maps or vector patterns or graphs.

To define “pretty colors” I used R to fit adaptively smoothed splines to 264 colors I picked by hand. So that was a total of 792 data points - 264 for each RGB channel. (I thought about using CIELAB, but it was less intuitive for me to define “pretty colors” in LAB space).

These interlocking graphs describe every color I like, and none I don’t like: I’m still a beginner in R and Python, so this actually took a surprising amount of time. But I ended up learning a lot about different color spaces and the biology of human color vision.

For example, I wanted to include a hue variable like the “H” in HSV. But HSV doesn’t account for the fact that human eyes aren’t equally sensitive across the color spectrum. So I manually adjusted my own scale to look more consistent.

A linear HSV (HSV1) has too much green and blue. A HSV sine wave (HSV2) is better, but still doesn’t have much yellow. For mine I got rid of the ugly neon colors and increased the amount of orange and yellow: To test out the finished algorithm I made a rainbow color clock. The clock cycles through the color wheel every 12 hours using only colors I like. So that means no neon colors, muddy yellows, extremely bright pastels, or artificial-looking colors like 100% blue.

The clock also changes brightness every hour so that the half-hour mark is the brightest and the hour-mark is the darkest. I wanted fairly basic rules so you could actually use the color to tell time.

The color names are pulled from Chirag Mehta’s Name that Color Javascript library. It’s an awesome open source tool that finds the closest named color in a huge database of color names. I think my favorite names are Mantis green and Meteorite purple.

This is what the rainbow clock looks like in its original equation form. Each of the RGB channels oscillates up and down once an hour. The finished rainbow color clock lives here as a responsive web page. I think I’ll eventually laser-cut a plywood frame for my old phone so I can have a nice wall-mounted version in my office at work.

Virus trading cards

April 11 2016

This week I made a set of virus trading cards! Viruses are surprisingly symmetrical, and I love them because they remind me of a biological version of snowflakes. Each trading card shows you the structure of the viral capsid - the protein shell protecting the genetic material inside a virus.

To make the 3D animations I used UCSF Chimera, a free molecular modeling program. When scientists discover a new protein structure they upload it to the worldwide Protein Data Bank. Each entry is assigned a unique ID number, which you can use to call up the structure in programs like Chimera or PyMol.

I used Tom Goddard’s tutorial to learn how to display viral capsids, and it’s actually a fairly simple process. You can even 3D print structures straight from Chimera , which is awesome and might have to be my next project.

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