> Every result is instantly reproducible. When you read a paper claiming that a new drug reduces symptoms by 30%, you click a link and watch the exact analysis run in your browser. The data processing, statistical tests, and visualizations execute in seconds using the same environment the authors used—preserved perfectly through reproducible containers.
Particularly the "data availability" and "Availability and peer review of computer code and algorithm".
However, in my limited experience, of trying to reproduce certain scRNA-seq processing pipelines, in practice it's never available as just a Github link. I can understand that some/many researcher's code is not in good shape, so I think it'll be quite a stretch to have this available.
I do think it's laudable though, to try and make it available. It would certainly have been very useful for me in the past.
I try to do something like this with my publications, and encourage others to. My goal is to have the pipeline from raw data to complete figures and manuscript in a repository, with cached data for computationally expensive analysis and for stochastic simulation results, and the option for the user to just use those or run the full pipeline, with or without the same random seeds. I just make clear that the code was run-once code and is going to be messy compared to code refined over time and diverse uses. I generally use Zenodo to a GitHub repo, however, in case GitHub decides to do something bad in the future. Making sure things run far in the future can also be a challenge. Sure, you can use a container: will the base of that container be available in 30 years?
And with that said, for experimental work, this approach does not make things fully reproducible; it only makes the analysis reproducible. There are always factors that influence experiments: research is by definition at the edge of our understanding, and reality has countless variables, including ones no one has thought of, known about or thought important.
Caveat: I am not a researcher (yet), I am moving from coding into science via a new degree, and along the way I am helping troubleshoot bioinformatics pipelines for scientists.
I see a lot of reliance on containers to make code always available, and I have the same misgivings as you do. It'll work a few years into the future, but what happens once packages aren't compatible with each other/the base container is upgraded/etc.
I've already seen this with older bioinformatics code, which is on old repositories that aren't running anymore, or are very unreliable (but weren't at the time that the code was written). And I'm talking about code that's "only" 15 years old; people will be going to these papers for implementation details long after that point.
Zenodo seems like a good step in the right direction. It should be available as long as CERN is going, shouldn't it? And by that stage it should be "too big to fail".
My first computer was a Microbee, a Z80-based Australian computer made by Applied Technology. I loved that thing to death, and thank my Mum and Dad for buying it for me, and getting me started in a field that I loved.
My school had a VIC-20, later an Apple II, and an enterprising teacher brought in a Commodore PET.
Later on, my friends had ZX Spectrums, Amstrad CPCs (not sure of the model), and lots and lots of C64s. A few Tandy 1000s and Cocos. I was always envious of the great games that they had.
I'm what you'd probably call a "90s kid" but I lived in a rural area. Around 1994, while the kids in more 'normal' places were using Mac LC-IIs with color screens, and I'm assuming others were using Windows 3.1, I was reading the manual cover to cover to learn how to use MS-DOS on a Tandy 1000 and teaching myself Applesoft Basic on a Franklin ACE (Apple II clone). I'm actually really grateful that thanks to lack of money I basically had a "Gen-X" computer childhood, allowing me to learn the command line, and, I think, to understand how computers worked better than those whose first computer experiences were Macs or Windows.
Nope. Alphafold 1 & 2 can be used for whatever you want. Also this has resulted in them being used to train better models than Alphafold 3.
> And Isomorphic are their commercial arm, and they partner with the big drug companies.
Yep. And given how Pharma works, being the "descendants" of Nobel-prize winning research I'm sad to say this will probably work and pay pretty well (not by Google standards, but by anyone else's standard, pretty well), despite the fact that there are better models.
Mostly the specification was just poorly written with many ambiguities. But also the design was complex, weird, invasive and IIRC not backwards compatible with standard RISC-V.
I re-took Computer Architecture recently, and we used their earlier book "Digital Design and Computer Architecture: ARM edition", and it was also excellent.
At least some journals have this as a stipulation e.g. https://www.nature.com/nature-portfolio/editorial-policies/r...
Particularly the "data availability" and "Availability and peer review of computer code and algorithm".
However, in my limited experience, of trying to reproduce certain scRNA-seq processing pipelines, in practice it's never available as just a Github link. I can understand that some/many researcher's code is not in good shape, so I think it'll be quite a stretch to have this available.
I do think it's laudable though, to try and make it available. It would certainly have been very useful for me in the past.
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