A question: How do biologists learn experimental design? Any suggestions for good textbooks or notable people responsible for current practices?

(I recently failed to convince some collaborators doing an experiment about data-visualization of the importance of a positive control.)

For example parquet is a high performance format, but writing was a bottleneck until I wrote to multiple files at once. The arrow library then makes it easy to read across multiple files at once.

Zarr is another format that seems to lean-in to this idea, but I don't have much experience with it.

This is my simplified version which I use to illustrate different types of error bars:

logarithmic.net/2017/dance/

Love this type of thing. I particularly like repeatedly sampling Confidence Intervals as a teaching tool. There's a web app that goes with a book caled "The New Statistics" where they call this the "Dance of the CIs".

esci.thenewstatistics.com

I should also note Shiny's own version of this, ExtendedTask, similarly allows an app to remain responsive.

If inputs to an ExtendedTask change while it is running, the new computation is delayed until the current one finishes. This isn't ideal for my application.

shiny.posit.co/r/articles/i...

The splats can also be used as weights for local model fitting.

*Grimes

If you've ever wondered why medical research has so many Chesterton's Fences, or what it would actually take to "do your own research", this would be a good starting point. (The target audience of the book is doctors seeking to use published medical research.)

I did get the AMS. Not quite sure what I'm doing with it. Maybe some interesting possibilities mixing soft and hard materials.

I've heard good things about marble PLA.

with apologies to grugbrain.dev