This week we turned our focus toward Data Visualization and explored it from multiple angles. The parts of the module that paid attention to digital accessibility were the most interesting to me. This may be because Tableau scares me and I have a low-medium confidence level with data thus far. In any case, I was taken my the accessibility connection. In my previous Graduate Assistantship, I was responsible for some YouTube videos that went up on our department’s page and those would always get sent to ATI to get captions and other kinds of accessibility edits. In that position, I also maintained our webpage (attached to the GMU main page) and I would get pointers here and there about yellow not being easy to read against light backgrounds and text size. However, there was never any formal digital accessibility process in place for anything besides video captions. This module has given me some tools that I wish I’d had then, including a literal compliance checklist. Obviously these tools are not exhaustive and perfection isn’t possible, but if I’d had this checklist to use before, our material likely would have been much higher quality in this regard. The Wave tool was very user friendly and I’ll be implementing that into my process when I inevitably decide to redesign my website again, which incidentally is pretty compliant given that it is basically black & white. I found Color Brewer 2.0 to be really interesting and is something I can see myself revisiting for a multitude of purposes. Given our module, the consideration of color contrast, color blindness, etc… is incredibly relevant and important when creating data visualizations. These principles of color make visualizations of any kind easier to understand, even for audience members who do not have any visual impairments.
The cognitive accessibility reading also piqued my interest. A lot of the little things we can do as digital creators to improve our material’s cognitive accessibility have to do with design both visual and content wise. Cognitive accessibility seems to marry the principles of color consideration with the DH version of not wanting to make a meeting out of what could have just been an email. That’s something my group really enjoyed about exploring the Aging President Tableau project we looked at in class. We talked about how on one hand, the visualization assumed a level of US history knowledge in the audience which could be problematic. On the other hand, it was so visually clear and direct in terms of the information it was presenting. There were opportunities (in the hover function) for users to ask further questions and get additional information, but the base level interaction with the visualization did not require a heavy interpretational lift.
Playing around with Tableau and creating a data visual reminded me of Open Refine and I wonder how perhaps they can be complementary. Maybe you use Open Refine to clean and structure data for a specific question so that when that data is uploaded to Tableau? That piece of it is what vexed me the most in creating my test viz. I tried to use my own data at first, and could not get it to make sense or be categorized how I wanted it to be… like how the sample data just perfectly populated. Anyway, Tableau seems pretty user friendly once you get the hang of it, and I’m not sure I’m quite there but this is something I think I could revisit and teach myself when I am in need.
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