Additional Info: I designed a Python workflow to perform OCR on every xkcd comic, feed that text into a large language model, and ask the model whether this comic was about the category named in the title.
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xkcd comics published about research correlates with...
Variable | Correlation | Years | Has img? |
The number of phlebotomists in New Jersey | r=0.89 | 11yrs | No |
The number of computer user support specialists in Maine | r=0.85 | 11yrs | No |
Micron Technology's stock price (MU) | r=0.84 | 17yrs | Yes! |
The average number of likes on LockPickingLawyer YouTube videos | r=0.8 | 9yrs | No |
Popularity of the first name Betty | r=0.8 | 16yrs | Yes! |
The number of marriage therapists in North Carolina | r=0.78 | 16yrs | No |
The number of statisticians in Vermont | r=0.77 | 16yrs | No |
Google searches for 'who is elon musk' | r=0.76 | 17yrs | No |
Google searches for 'dr pepper vs mr pibb' | r=0.74 | 17yrs | No |
How insightful LEMMiNO YouTube video titles are | r=0.73 | 12yrs | No |
Total runs scored in the World Series | r=0.69 | 7yrs | No |
xkcd comics published about research also correlates with...
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You caught me! While it would be intuitive to sort only by "correlation," I have a big, weird database. If I sort only by correlation, often all the top results are from some one or two very large datasets (like the weather or labor statistics), and it overwhelms the page.
I can't show you *all* the correlations, because my database would get too large and this page would take a very long time to load. Instead I opt to show you a subset, and I sort them by a magic system score. It starts with the correlation, but penalizes variables that repeat from the same dataset. (It also gives a bonus to variables I happen to find interesting.)