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Comment by CJefferson | Hacker News Reader

I partially blame software infrastructure here.

I've tried to do reproducible research, but just setting something up when I get the exact version of GCC, python and python packages I want, in such a way I can get the same versions 2 years later, is a collosal pain.

Just dumping a pile of code people won't be able to run isn't super useful, and becomes a source of continous complaints and requests for fixes.

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You might be interested in using Guix [1] for reproducible research, as is reported earlier (for genomics analysis) [2] and still actively developed [3].

As a bonus, Guix and Nix [4] allow for modular and composable pipelines, where you can swap different components (such as to upgrade Python from 3.8 to 3.9, or to enable certain optimizations for some Python libraries, or to run on MacOS and Linux while keeping the whole stack more or less the same), which would be more difficult to do reproducibly with other tools such as Docker (composing Guix or Nix functions are easier than composing DOCKERFILE or Docker Compose).

[1]: https://ambrevar.xyz/guix-advance/

[2]: https://academic.oup.com/gigascience/article/7/12/giy123/511...

[3]: https://hpc.guix.info/blog/2020/11/hpc-reproducible-research...

[4]: https://builtwithnix.org/

When you publish the code, the chances of someone getting it to run increases from 0% to a much much higher number. Maybe 50% of those who really try can get it done. That it is hard to get to 100% does not at all invalidate this value.

It is fine to not do much support and bugfixing on such a codebase - but one should make sure to state it clearly in the documentation.

I try out published research code (in machine learning) all the time. Almost every time I have to debug and fix things. But in a couple of hours or days I can usually get it fixed, normally without any help. That is a shorter time than it take (in the best case) to even ask for access to code from a researcher.

Reduce barriers to entry. Make it easy for others to build on your work. Your chances of impact and value created from your work will be higher.

I have over a dozen open source projects, many where I am the major author :) Therefore, I also get significant numbers of complaining emails.

For a while my e-mail address was in a header in gcc's C++ standard library, in retrospect that was a seriously bad choice.

You could make a container with the pipeline and data set up to go. Also, ideally, you'd publicly host the Dockerfile or w/e build time config, too.

Downvoters: explain? Maybe this suggestion is aimed too much at a specific piece of technology (Docker). But there are plenty of tools out there to create reproducible builds, which I think is the overall point of this response

Speculation: many downvoters are people who have repeatedly had a frustrating user experiences and are just exasperated at being repeatedly told how thier tools are good enough.

There is so much on the plate of the academic that to learn yet another piece of software — like docker which can be trying at the best of times — is yet another straw that can break the back.

I didn't downvote but...

I'm skeptical that specific tools and "formalities" like requiring Docker containers will do a lot to improve the quality of scientific research. Obviously, being able to re-run an old analysis is better than not being able to do so but that wouldn't have helped (much) with this issue

The root cause here was a conceptual problem, not a library version mismatch. They used the same data to a) group data points into bins and b) test for differences between those bins. Having a perfectly-engineered, smoothly-running pipeline might have made it easier to confirm the result, but I don't think it would help detect it. The things that would have things like rewarding collaboration (more eyes make bugs shallower) instead of rewarding mostly lead authorship, time and support to carefully mull over findings instead of a helter-skelter rush to publish, and a publication culture that doesn't avoid ambiguity. Those are much harder to achieve though....

Because, as far as I can tell, docker doesn't make easy reproducible images.

How do I make a docker file which will create the same image, bit for bit (at least version for version) in 2 years time? I find usually it's required to update packages, which is good for security, bad for not breaking things.

> Also, ideally, you'd publicly host the Dockerfile

Docker doesn't make it easy to produce a reproducible config. People commonly write Dockerfiles with stuff like this in them:

RUN apt-get install -y my-favourite-package

And then which version of my-favourite-package you get depends on what is the latest one on the update servers at a time. Maybe a new version is released tomorrow with a regression bug that breaks everything.

There are ways to get around this problem, but the default ways of doing things in Docker doesn't encourage reproducibility.

The solution is to put the actual package into the Dockerfile instead of the reference? Asking because I don't actually know the answer.

The solution is to publish the created docker container for examination and reuse in recreating the results. It is possible to create reproducible docker containers, but not with docker.

In one company I worked we hosted our own repo for npm and mirrored all packages we used there. For our own stuff we stored all old versions in it too. The software itself always installed specific version rather than "latest"

Another option is to store the whole docker images, granted this will not give you the ability to always rebuild it, but at least you have a "binary" you will be able to run later.

This is true, although the built Docker image itself (rather than Dockerfile) wouldn’t have these problems. I think in situations where reproducibility is paramount, it would make sense to offer a tarball of the image (`docker save`) along with the Dockerfile itself.

After making Dockerfile you can additionally push your built docker image to docker hub or other public facing repo, People can then directly pull your docker image in case any issue with Dockerfile.

I look at this whole idea another way: if your results are so fragile that they need a docker container (with all the caveats others are mentioning about versions etc), it's not really "reproducible" just because someone can re-run your code. Real reproducibility would come from having discovered some objective fact that shows itself again when similar work is attempted.

Reproducibility =/= generality which is a much more important aspect of research.

While I agree that science should aim for replicability (same result for similar methods on the same scientific question) instead of reproducibility (same methods) [1], it does not hurt to let others to reuse part of your research pipeline.

I think we can in fact test and improve replicability if the whole pipeline is published. We should aim for allowing others to change certain parameters or data in the pipeline. That is, the result should be robust with respect to minor changes in the pipeline, which could be more easily tested by others with a published, fully specified pipeline.

Referring to my other comment [2], Guix or Nix really shine in allowing others to change part of the pipelines, due to their functional approach to composing components.

[1]: https://phys.org/news/2019-05-replicability-science.html

[2]: https://news.ycombinator.com/item?id=25866651

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