Hacker News Reader

My lab practice is to publish the repo path and the full hash of the commit that gave the results in the paper. The repo might have subsequent bug fixes, new code or data, etc. But you can get and run the exact code & data that the claims are based on if you wish.

The published paper contains the hash printed in the text.

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.

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

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.

> workflow for modern scientific research

careful with the word modern, as the latest generation of students and postdocs is using Git, CI/CD etc, sometimes militantly but this amateurish approach reflects the incentive structure, where the goal isn't to produce a robust solution but to achieve notoriety and impact in one's domain. This impact factor plays into getting the next grant which creates a feedback loop that makes any shortcuts to the latest shiny thing obligatory.

Even when failures become apparent, scientists can play the feature-not-a-bug card very frequently to stay on top.

The journal I edit has a policy that the code and data has to be made available to the reviewers. I was surprised at the number of reviewers that dig into the code.

TBH, code (even when all you can do is just looking at it and understanding how it works) is probably the most useful thing to the reviewers themselves.

Imagine the equivalent for reviewing an experimental paper, if you were given a free instant travel to the place where they did the experiment and you could look at their setup and tinker with it and see how it works. I guarantee you a lot of reviewers would go for that.

I've learned that what you say is true. This is particularly the case for papers that use small datasets and not especially complicated empirical analysis.

Surprised that the reviewers do or don't look at the code?

Surprised that the reviewers do look at the code. I had assumed they wouldn't want to put the time in. In hindsight, I think it's often the case that it's faster to look at the code than to read an unclear methodological description.

Speaking as a reviewer, reviews do take an immense amount of time, especially just in trying to figure out what was done and how. Code would actually speed things up a lot.

Yes, but there needs to be a more complete solution to this. For this to happen, somebody should start funding one (relatively) professional developer per research group. Or, at least, there should be a way to fund "methodology/development" researchers/PhD students that have some domain knowledge, but mainly focus on aspects of publishing code, data, stats methodology, maintaining it, and ensuring that others can use it. Unfortunately, in many fields there does not seem to be a niche for that. But clearly this is a very important problem, possibly more important than funding a larger number of research ideas?

In most fields papers / articles are still the one critical metric crucial for career advancement. You will sit on data and code until you are ready to publish, sometimes for years.

Datasets and analytical code must be regarded as a scientific achievement on its on, albeit not as substitute for the "full" substantive analysis in a paper.

A social science PhD in Denmark, for example, must write two to four peer reviewed papers at the moment. The option to substitute one of the articles with a data set?

Things get better though: People nowadays at least understand my citations better, since I cite at least the explicitly included R-packages used in my work.

> The workflow for modern scientific research always seems so amateurish from the perspective of basic software development.

It's sad but it's true. Although there are fundamental pressures that come because academia is academia, I pretty firmly believe that this can be fixed culturally too.

Ultimately, I think people in some fields enjoy programming but view it to be either beneath them or beneath their paper. This is clearly not the case for Computer Science, but I noticed a few papers in the tire society journal which presented promising results but with absolutely no source code.

One slightly ugly truth is that a lot of academics don't really know how to program properly or how computer actually work (Again, outside CS for obvious reasons). I'm reminded of the Imperial Covid Model's bugs as a fairly easy example.

As to culture-question - the obvious solution is to start requiring disclosure of almost all materials and data used to construct a paper - for instance if Hendrik Schon had to publish the data behind the curves in his graphs his fraud could've been spotted almost immediately.

I've gotta say, I don't read conservative papers, but I follow news closely, and up to this day I didn't know about the bugs. The media landscape is crazy.

However, it looks like they've been updating the model (and are at version 10 on github). I have no idea what their results look like now.

I think the catch here is that people in academia who like cs/eng rapidly leave academia for the much better paying sde/data science roles. Then what you have left are the people who care less about the code, at least in aggregate.

> The proper solution would be to publish the code along with the paper so that others could directly review it. Ideally, the raw data would also be published,

Definitely. Some journals and conferences have badging for papers that also submit their digital artifacts, and also for experiments that have been independently reproduced.

Personally I'd like to see entire journals or conference sessions where the code and experimental data for every paper are available for review.

Sometimes authors can't submit their code for corporate IP reasons and data for privacy or other reasons, but the more digital artifacts that can be submitted, reviewed, and built upon, the better.

> The proper solution would be to publish the code along with the paper so that others could directly review it.

This is taking root! More CS conferences should get onboard.

https://www.artifact-eval.org/about.html

(certificate seems to be expired...)

Universities are trying, but the incentives for researchers just aren't there. https://deepblue.lib.umich.edu/

> The proper solution would be to publish the code along with the paper so that others could directly review it.

Most ML researchers write code to prove their theory works, not to check if it works.