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Vincent L. Ott
@vlott.bsky.social
I model memory & develop stats software. 📍Amsterdam vincentott.github.io
68 followers186 following14 posts
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Reposted by Vincent L. Ott
Ttterence.bsky.social

Mount Fuji in glass. #rayshader#rstats tale

A visualisation of Mount Fuji in glass
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VLvlott.bsky.social

@zeit.dewww.zeit.de/digital/mobi...#dataviz#linguistics#etymology

scatter plot published on 26.09.26 by zeit.de; for the series "nice and useful". the x-axis ranges from "lame" to "nice". the y-axis ranges from "useless" to "useful".
Merriam-Webster´s entry on the term "lame" states: "the disparaging uses, as in 'a lame excuse', 'he´s so lame', 'a lame party', and 'lame jokes', are occasionally considered to be offensive".
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VLvlott.bsky.social

Prompt was: "Is it possible to alter the box length in geom_box() for example that is stretches from lower to upper whisker instead from 25th to 75th percentile?"

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VLvlott.bsky.social

My R is currently running a simulation so I cant verify the following approach: ChatGPT recommended using geom_errorbar() in combination with geom_box(). And I have been successful with geom_errorbar() is the past for customizing boxes. So might work. Great data viz question, thanks for posting!

output from ChatGPT
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VLvlott.bsky.social

Stats Puzzle in The Guardian: www.theguardian.com/science/2024...#stats#rstats to come up with an answer.

Screenshot of the puzzle. Can be read via the link for free.
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VLvlott.bsky.social

so then alumnum/alumna would be the general, gender neutral member/members terms?

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Reposted by Vincent L. Ott
Smehr.nz

this is pretty amazing: @lucinauddin.bsky.socialwww.lieffcabraser.com/antitrust/ac... summary of the case:

The Publisher Defendants’ Scheme has three primary components. First, the Publisher Defendants agreed to not compensate scholars for their labor, in particular not to pay for their peer review services (the “Unpaid Peer Review Rule”). In other words, the Publisher Defendants agreed to fix the price of peer review services at zero. The Publisher Defendants also agreed to coerce scholars into providing their labor for nothing by expressly linking their unpaid labor with their ability to get their manuscripts published in the Publisher Defendants’ journals. In the “publish or perish” world of academia, the Publisher Defendants essentially agreed to hold the careers of scholars hostage so that the Publisher Defendants could force them to provide their valuable labor for free.
Second, the Publisher Defendants agreed not to compete with each other for manuscripts by requiring scholars to submit their manuscripts to only one journal at a time (the “Single Submission Rule”). The Single Submission Rule substantially reduces competition among the Publisher Defendants, substantially decreasing incentives to review manuscripts promptly and publish meritorious research quickly. The Single Submission Rule also robs scholars of negotiating leverage they otherwise would have had if more than one journal offered to publish their manuscripts. Thus, the Publisher Defendants know that if they offer to publish a manuscript, the submitting scholar has no viable alternative and the Publisher Defendant can then dictate the terms of publication.
Third, the Publisher Defendants agreed to prohibit scholars from freely sharing the scientific advancements described in submitted manuscripts while those manuscripts are under peer review, a process that often takes over a year (the “Gag Rule”). From the moment scholars submit manuscripts for publication, the Publisher Defendants behave as though the scientific advancements set forth in the manuscripts are their property, to be shared only if the Publisher Defendants grant permission. Moreover, when the Publisher Defendants select manuscripts for publication, the Publisher Defendants will often require scholars to sign away all intellectual property rights, in exchange for nothing. The manuscripts then become the actual property of the Publisher Defendants, and the Publisher Defendants charge the maximum the market will bear for access to that scientific knowledge.
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VLvlott.bsky.social

#stats

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VLvlott.bsky.social

@aaronlob.bsky.social there are not many others of us on bluesky yet, right? Do you know of more people to tag?

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VLvlott.bsky.social

"The data revealed considerable between-people variation in the degree of this same-side bias".

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VL
Vincent L. Ott
@vlott.bsky.social
I model memory & develop stats software. 📍Amsterdam vincentott.github.io
68 followers186 following14 posts
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