On Data Science and Accessibility
Accessibility in Visualizations, Design, and Programming
January 29th started off as the best day in a while. Hot coffee, boatloads of snow, and a Saturday with nothing to do. As a Colorado native, I love snow! Er, I loved snow . . . by the end of the day, I knew why doctors called snowstorms “an orthopedist’s favorite weather”
I was having the time of my life gallivanting around in the snow with friends. I was out behind the Philadelphia Museum of Art when I suddenly took a wrong step and slipped quite far off a ledge. Snap, crackle, pop, and I couldn’t feel my toes. The swelling was really intense. Beautiful like a winter sunset. (I’ll save you from the gruesome picture!) At the Urgent Care, my worst fear was realized. My precious ankle was broken at the Tibia and the stress of the break fractured my Fibula. It was going to be months before I could walk again.
On top of this, I had to keep up with my Data Science courses. It’s been extremely difficult to juggle the pain of my injury, schoolwork, doctors appointments, insurance phone calls, and not being able to do simple things around the house on my own. Injuries always have a way of putting things in perspective. I started thinking about the difficulties in accessibility within Data Science: both understanding DS visualizations and graphics as well as performing DS techniques. After doing a bit of research, I wanted to share some things I’ve found that may aid in accessibility in learning and employing Data Science.
Accessibility in Data Visualization
In Nancy Organ’s excellent article “An Incomplete Guide to Accessible Data Visualization,” she shares many best practices for creating visualizations that cater to those visually impaired. Below is a quote I never previously considered when it came to visualizations.
There’s sometimes a misunderstanding that you shouldn’t use red or green at all in your visualizations; that doing so immediately disqualifies them from usability. That’s not entirely true, and neglecting two of the most culturally-relevant, high-association colors is unnecessarily severe.
Instead, try making the red slightly orange, and the green slightly blue. To someone with normal color vision, this small change won’t make much difference. To someone who has trouble distinguishing red and green, however, these small tweaks can make greatly improve differentiability.
I would highly recommend reading her article as she explores text, titles, and captions; colorblind friendliness; design tips and tricks; keyboard navigation and screen readers; and data sonification, which was an entirely new concept to me.
Accessibility is Good Design
Ciera Martinez’ article “How to Approach Accessibility in Academic Data Science” was also quite enlightening to read. Here’s one of my favorite quotes:
One of the root problems of doing accessible work within academia is that academia doesn’t support disabled people in these spaces. “Nothing about us, without us” rings true. We cannot, as researchers, build a truly accessible space without including people with disabilities in our design and research groups. Therefore, the first step is to make academia more accessible and support a larger population of people with disabilities in our departments and graduate programs.
She goes on to detail how designing with empathy creates designs that are better. Period. Please read this article!
Accessibility in Programming
As I’ve had trouble concentrating on programming and struggling with the material, I can’t help but think how inaccessible coding can be. For those with disabilities, even more must be overcome to code. Here are a couple suggestions from Access Computing that can make programming more accessible to everyone.
Quorum is a programming language originally designed for individuals who are blind or have low vision, but is used by other individuals as well. It was inspired by two observations: (1) much of the computer science education literature relies on visual representations and (2) text-based programming in languages with traditional syntax (e.g., C++ or Java or Python’s whitespace rules) are difficult to understand through audio. Quorum uses human factors data as an evidence-foundation. You can learn more about Quorum by viewing the video Quorum: An Accessible Programming Language.
Bootstrap is a program designed to be accessible to students with a broad range of disabilities, including visual and sensorimotor impairments. The language and all its interactive elements are accessible by screen reader. Its structured editor and block programming editor both have accessibility-enabled features, which can read code based on meaning instead of syntax and provide a hands-free drag and drop.
Myna, a vocal user interface, was created so that users can program block-based languages purely by voice. Although originally developed to work with Scratch v1.4, Myna has been extended to work with additional block-based languages (e.g., Lego Mindstorms, Scratch v2.0, Snap!, Pixly, Spherly). Myna is responsive to the problem that some individuals with motor impairments find block-based languages, such as Scratch to be difficult or impossible to use because they require the use of the mouse and keyboard.
Conclusion
My experience has given me perspective on Data Science and accessibility. Some observations I’ve made:
- It is very difficult to program with your ankle above your head. Even sitting at a desk with a laptop requires a range of mobility. When we design systems, we must make them intuitive enough that people can use them easily. You never know what circumstances a user might be going through when using your software.
- Pain medication can impair cognitive functioning. Data Science and Mathematical concepts can be quite overwhelming to even the sharpest of minds. We need to show empathy and take things slow sometimes to let everyone understand a concept. Data Science can be understood by anyone, it must not be needlessly complex.
- Data can be boring and brutal! For those struggling with attention or anxiety disorders, row after row of cold hard data may seem so intimidating that it is difficult to conceptualize. Spicing up your data analysis with visualizations, diagrams, and anecdotes not only helps us understand the data, it helps keep us engaged. After all, why would you spend time doing something if you don’t care about it?
Thank you kindly for reading!
