The paper was posted on the 18th of June. I would guess that means they started writing the first draft in the winter of 2026, if not earlier. 2026 data obviously wouldn't be available then, or even now---we're only in September!

In fact, most data products take a few months to compile and distribute. The US will release its Q2 estimate of GDP at the end of September, for example; Statistics Canada is on a pretty similar schedule. Thus, 2025 data might not have been available when they started writing either. It's certainly possible to estimate things on your own, but now you need to show that a) your method of estimating GDP (energy prices, whatever) is valid and b) any effects you see aren't simply due to using different metrics.

In short, it just takes a while to do and write up research.

However, the in-country processing takes 6 months while the flagpole option (If you’re eligible) takes 15 minutes.

I know this computer has more RAM than reported and it's definitely not on a 4G network either.

You build a model that describes how likely people are to get diagnosed with Alzhimer's as a function of sex: maybe 12 women out of 100 get it, while 8/100 men are diagnosed. You can do the same thing for age: almost nobody is diagnosed before 30, it's very rare before 40, and sadly common (~1 in 10) after 65 years of age. There are all sorts of mathematical tricks to include multiple variables, account for the fact that you can only be diagnosed once, or that data is "censored" (i.e., missing) at some ages because people have already died.

Based on that, you can then ask if the prevalence of Alzheimer's Disease among cab drivers is surprising, given their demographics. For example, if we know that they skew male and younger, we'd expect that number to be a bit lower than a naive estimate of 10%. In fact, we can calculate that number and then see if it's unexpected given the number we actually see. In practice, you'd actually do this by fitting two models: one containing job and one that doesn't, and see which one best describes the data and how, specifically, the job factor affects the outcome.

However, how well these adjustments work depends on the quality of your data and your modelling. Your model could be missing important factors or have the wrong structure: (e.g., you assume risk is directly proportional to age, but it actually increases more rapidly as you get older). Your data could have problems too: maybe women are more likely to go to the doctor (and thus get diagnosed), even if the actual prevalence is the same.

FWIW, my comment may have come off as more dismissive than I meant--it's a cool approach. I'd just never seen a visualization quite that like and then ran into two in the same week.

If you want to take the patent agent exam, your CS degree has to come from an ABET-accredited program, though many of the very good ones aren't (Stanford, CMU).

Likewise, federal jobs also sometimes seem to want accreditation but not always.

It looks strikingly like the LLM explainer someone posted a few days ago.

[0] I ask this bit in the "About" makes me wonder: "No build step, no dependencies, no network calls. Every shape on screen is drawn from plain polygons in a canvas."

And of course, that raises the issue of what do you want to do about ties....

https://en.wikipedia.org/wiki/Sandy_Island,_New_Caledonia

It does feel like it’d be “better” if good behavior came from the company’s intrinsic behavior but laws like this help (re)establish norms that encourage those sorts of actions.

Likewise, it’d be better if kids say please and thank you because of a genuine sense of gratitude, rather than just to avoid parental nagging—-but that nagging helps develop appreciation for others’ actions.

Any replacement system needs to somehow serve as a token for people who can't/won't actually read your papers.

There’s also a lot of path dependency. SHA256 makes a lot of sense once you already have lots of digital data but you’d probably do something else entirely in a more analog world.

PLOS One does publish pretty much anything, but that was always meant to be the point: "here's some data, make of it what you will. "