This! I struggled with this topic in university. I was studying pulsar astronomy, and there was only one or two common tools used at the lower levels of data processing, and had been the same tools used for a couple of decades.

The software was "reproducible" in that the same starting conditions produced the same output, but that didn't mean the _science_ was reproducible, as every study used the same software.

I repeatedly brought it up, but I wasn't advanced enough in my studies to be able to do anything about it. By the time I felt comfortable with that, I was on my way out of the field and into an non-academic career.

I have kept up with the field to a certain extent, and there is now a project in progress to create a fully independent replacement for that original code that should help shed some light (in progress for a few years now, and still going strong).

> The software was "reproducible" in that the same starting conditions produced the same output, but that didn't mean the _science_ was reproducible, as every study used the same software.

This is the difference between reproducibility and replicability [1]. Reproducibility is the ability to run the same software on the same input data to get the same output; replication would be analyzing the same input data (or new, replicated data following the original collection protocol) with new software and getting the same result.

I've experienced the same lack of interest with established researchers in my field, but I can at least ensure that all my studies are both reproducible and replicable by sharing my code and data.

[1] Plesser HE. Reproducibility vs. Replicability: A Brief History of a Confused Terminology. Front Neuroinform. 2018;11:76.

This is almost an argument for not publishing code. If you publish all the equations, then everybody has to write their own implementation from that.

Something like this is the norm in some more mathematical fields, where only the polished final version is published, as if done by pure thought. To build that, first you have to reproduce it, invariably by building your own code -- perhaps equally awful, but independent.

Maybe gate release of the code by some number of attempted replications.

Should this be surprising? I'm not saying it is correct, but it is similar to the response many managers give concerning a badly needed rewrite of business software. Doing so is very risky and the benefits aren't always easy to quantify. Also, nobody wants to pay you to do that. Research is highly competitive, so no researcher is going to want to spend valuable time making a new tool that already exists even if needed if no other researchers are doing that.

Conversely though, it is often impossible to obtain the original code to replay and identify differences once that step is reached without some sort of strong incentive or mandate for researchers to publish it. When the only copy is lost in the now-inaccessible home folder of some former grad student's old lab machine, there is a strong disincentive to try replicating at all because one has little to consult on whether/how close the replicated methods are to the original ones.

And so we find ourselves in the same situation as the rest of the scientific process, throughout history. When I try to replicate your published paper and I fail, it's completely unclear whether it's "your fault" or "my fault" or pure happenstance, and there's a lot of picking apart that needs to be done with usually no access to the original experimental apparatus and sometimes no access to the original experimenters.

The fact that we can have that option is an amazing opportunity that a confluence of attributes of software (specificity, replayability, easy of copying) afford us. Where we are not exploiting this like we could be, it is a failure of our institutions! But it is different-in-kind from traditional reproducibility.

Of course, but the flip side is that same confluence of attributes has also exacerbated issues of reproducibility. Just as science and the methods/mediums by which we conduct/disseminate it have changed, so too should the standard of what is considered acceptable to reproduce. This is especially relevant given how much broader the societal and policy implications have become.

More concretely, it is 100% fair (and I might argue necessary) to demand more of our institutions and work to improve their failures. I'm sure many researchers have encountered publications of the form "we applied <proprietary model (TM)> (not explained) to <proprietary data> (partially explained) after <two sentence description of preprocessing> and obtained SOTA results!" in a reputable venue. Sure, this might be even less reproducible 200 years ago than now, but the authors would also be less likely to be competing with you for limited funding! Debating about the traditional definition of reproducibility has its place, but we should also be doing as much as possible to give reviewers and replicators a leg up. This is often flies in the face of many incentives the research community faces, but shifting blame to institutions by default (not saying you're doing this, but I've seen many who do) is taking the easy road out and does little to help the imbalanced ratio of discussion:progress.

This. I absolutely agree there needs to be more transparency, and scientific code should be as open as possible. But this should not replace replication.

But "rerunning reproducability" is mostly a neccessary requirement for independent reproducability. If you can't even run the original calculations against the original data again how can you be sure that you are not comparing apples to oranges?

In some simulations, each rerun produces different results as you’re simulating random events (like lightning formation) or using a non-deterministic algorithm (like Monte Carlo sampling). Just “saving the random seed” might not be sufficient to make it deterministic either, as if you do parallelized or concurrent actions in your code (common in scientific code) the same pseudorandom numbers may be used in different orders each time you run it.

But repeating the simulation a large number of times, with different random seeds, should produce statistically similar output if the code is rigorous. So even if each simulation is not reproducible, as long as the statistical distribution of outputs is reproducible, that should be sufficient.

Very interesting. I was thinking of software as most similar to apparatus, and secondarily to procedure. You raise a third possible comparison: calculations, which IIUC would be expected to be included in the paper.

There are some kinds of code (a script that controls a sensor or an actuator) where I think that doesn't match up well at all. There are plenty of kinds of code where they are, in fact, simply crunching numbers produced earlier. For the latter, I'm honestly not sure the best way to treat it, except to say that we should be sure that enough information is included in some form that replication should be possible, and that we keep in mind the idea that replication should involve human interaction.

This is not clear at all. It depends on the "result" in question. If I wrote a paper describing a super numerical algorithm for inverting matrices, and no one is able to replicate the superior performance of my algorithm despite following the recipe in my paper, then whether they can run my code or not doesn't seem to be of the highest priority.

Edit: more careful phrasing.

> whether they can run my code or not doesn't seem to be of the highest priority.

On the contrary; in that case there are four possibilities:

a: your algorithm doesn't work at all, and your observations are a artifact of convenient inputs or inept measurements.

b: your algorithm works, but the description in the paper is wrong or incomplete

c: your algorithm works as described, but the replicater implemented it incorrectly

d: other

Having the original implementation code is necessary to distinguish between cases a and b versus case c, and if the former, the code for the test harness is likely to help distinguish a versus b. (Case d is of course still a problem, but that doesn't mean it's reasonable to just give up.)

I agree with the case analysis, but disagree with the implication that the code needs to be runnable (which is seems to be the point of the discussion at hand). In many cases having source code, even if it no longer runs, should be sufficient.

I complete agree with your case analysis, but disagree with the conclusion that the code needs to be runnable for it to be useful -- I thought this was the point of the discussion at hand? In most situations, having source code, even if it no longer runs, would be sufficient to conduct the analysis you describe.

I'm all for more transparency, and this includes making codes and data public as much as is reasonable. But the real test is if someone can independently replicate the result, and how to incentivize replication studies (in both computational and experimental science) is also important, and in my view should not be divorced from discussions of reproducibility.

Edit: rewrote to clarify my position.

I would also place as a requirement that the code be comprehensible to someone familiar with the domain - .i.e. a "peer".