I literally wrote that section of the paper 45 minutes ago. If you ask me what is the design of the study we used, I can tell you "yes, we showed all independent factors to each participant". But I cannot tell you if that's "within" or "between" or "across" or whatever-they-feel-like-calling-it-today
(Add another one to my growing suspicion that I have some weird form of dyslexia-spectrum thing) I can never remember if it's across-subjects or within-subjects or between-subjects. I know the _meaning_, but the word association never sticks to my head. Might as well keep that google tab open forever
Well, that's one more thing Kosara and I disagree about.
Standard datasets _are_ important. You literally don't want to be surprised by the dataset when you're trying to understand a technique.
Synthetic examples serve a similar purpose: you _know_ what's in them, so you can check that your technique is doing what you expect.
If you design a new technique and show it on a new dataset, readers can't separate one from the other.
Our paper "Using Animation to Alleviate Overdraw in Multiclass Scatterplot Matrices" will be presented at CHI 2018 on Thu 4/26 at 11am in Montréal!
Our approach uses a simple animation loop to alleviate overdraw in one of the hardest settings---multiclass SPLOMs.
It is work with our former undergrad student Helen Chen, myself, Alark Joshi, & Beste Yuksel at USF, Eric Ragan at Texas A&M, and Lane Harrison at WPI.
See a demo and more at http://vgl.cs.usfca.edu/animated-sploms/
RT @email@example.com: Only a few days left to apply for the #datavis researcher position in our group!
– German: https://www.fh-potsdam.de/fileadmin/user_dateien/1_informieren/D_Profil/a_Stellenanzeigen/20180305.aka.MA.Doerk.02_2018._D_.pdf
– English: https://www.fh-potsdam.de/fileadmin/user_dateien/1_informieren/D_Profil/a_Stellenanzeigen/20180305.aka.MA.Doerk.02_2018._E_.pdf
Observable is cute: https://beta.observablehq.com/d/50d988b38bcccb50
https://cscheid.net assistant prof at arizona computer science. data analysis, data vis. humans, data, scale
A social space for anyone in data, visualization, creative coding, and related arts and research. Share your work, discuss, critique, and ask for help! Come one, come all:
→ creative coders
→ data scientists and visualizers
→ generative artists
→ visual researchers, curators, and critics
→ anyone else data- or visualization-adjacent
If you are curious about the world and creativity, you are welcome here. Learn more.