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they are all so outdated they refuse to install the right version :marseytabletired: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 only worked with bioinformatics once and they had done their work in julia. That was ~10 years ago, I hate them so much.

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Juilia was basically killed when python got faster. Only positive of Julia is that it can work with gpus without the mess that is cuda Nvidia slop.

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