aqsalose (scholar?) @aqsalose@scholar.social

Finally, an explanation: https://www.smbc-comics.com/comic/robins

#comics #batman #smbc

Disney's multiplane camera:

https://twitter.com/RaminNasibov/status/973625853165916161

#animation #history

https://www.wired.com/story/real-heroes-have-the-guts-to-admit-theyre-wrong/

"Bolnick investigated his original work and, in a horrified instant, recognized his mistake: a single miswritten line of computer code. [...]
Why do I recount this story? Because I think society ought to give Bolnick some sort of a prize. We need moral examples of people who can admit when they’re wrong. We need more Heroes of Retraction."

I don't know about "Heroes of Retraction", but I do find the expressed sentiment inspiring.

(via https://twitter.com/yletiede/status/963773322017296384 )

A Norwegian University Student Used a Spy Camera in This Amazing Example of 19th Century Street Photography

http://www.thisiscolossal.com/2018/01/19th-century-norwegian-street-photography/ https://scholar.social/media/1Cp8-HPUfnXeW0yadaM https://scholar.social/media/qJaegUjoPW_YyT8XG-M

"What Could Have Entered the Public Domain on January 1, 2018?
Under the law that existed until 1978 . . . Works from 1961"

https://law.duke.edu/cspd/publicdomainday/2018/pre-1976/

via https://news.ycombinator.com/item?id=16045852

#ifttt2tweet

Can ppl boost this until somebody who knows #stats or #dataviz etc sees it?

I have a time series of numbers, sampled irregularly, typically between 1 and 6 samples a day. On a line graph, there's considerable jitter. If I want to smooth jitter and see trends on e.g. a 36-hour granularity level, but also not lose outliers in the smoothing, what's the best approach?

My idea: average samples in arbitrary 12-hour periods (am/pm), then apply a 36-hour moving average. But I don't know this stuff.