How is Biology Research handling new Tech?

>Submit paper on ML algorithm for spatial transcriptomics :marseysnappy:

>One reviewer insists we benchmark it against his two year old methodology with like 2 citations that no one knows or cares about :marseysmug2:

>Since scientists can't make repeatable or documented code the entire thing is hard coded to his one training dataset meaning I have to rewrite it :marseygigaretard:

>Rather then provide a GUIX archive or pack or a docker image they just have a list of pip packages :marseyboardcode:

>they are all so outdated they refuse to install the right version :marseytabletired:

:marseyeyelidpulling: I ofc have to finish this in 30 days while I also have two class projects due in 20 days plus studying for my finals. I love having to do the same work as PHD and post grad students for less pay :marseywagie:

TLDR; !linuxchads !fosstards !biology Despite having cowtools to create instantly deployable exact decency loaders via docker or GUIX scientists can apparently pass unreplicatable and hardcoded code past reviewers. :marseythumbsup:

Pip3 download speeds are prob butt (like 600 kb/s) because there is one gorillion AWS and Azure vms for ML trying to set up at the same time and amazon and Microsoft cant arsed to use their billions of dollars to make their own mirrors so they just leach off FOSS projects

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I know a couple CS professors, at their school every single person involved went straight into academia when they finished their own education. One of them didn't finish his PhD untill he was already teaching. None of them have a single lick of real world experience, they don't know what's actually useful or being used these days. Anything slightly newish like Docker is not only foreign and unknown to them but totally uninterests them. Lack of curiosity defines their computer use. They don't really seem to know jack shit about linux and 90% of their work appears to be done in Visual Basic via a VM from a MacBook. I can't imagine the situation where computers meet applied sciences can be good.

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I do all my work on a macbook ssh'd into a linux enterprise machine and code everything in nano (no syntax highlighting) :marseybigbrain:. Comp sci cels cant even provide the version of python they used at minimum so I have to guess what exact version works with the unearthly dependency chain.

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code everything in nano

https://media.tenor.com/KjJTBQ9lftsAAAAx/why-huh.webp

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code everything in nano

:marseygigaretard:

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Please use vscode remote instead

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That's the fricking problem I always had with LISP-y languages. They always came from the fricking ivory towers of academia and lacked so many real world utilities.

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This is why I love stata. All of its models and commands are designed by professors (economists) who painstakingly write out the documentation. The problem with python cowtools is that you don't know which method and which underlying assumptions are at play (because the !fosstards who wrote it have no idea either). Must be heck using it for anything involving serious statistics.

!codecels, bring out the pythonistas. I want to wring your necks!

:#marseyfrozenchosenchokespal:

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I unironically love Python

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The sooner D replaces python the better

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frick you

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I love Haskell, I think it hits an amazing mix of functional purity and a deeply expressive type system.

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