[izacus]

I've spent plenty of time of my career doing exactly the type of replication you're talking about and easily the majority of CS papers weren't replicable with the methodology written down on the paper and on dataset that wasn't optimized and preselected by the papers author.

I didn't care about sharing code (it's not common), but independent implementation and comparison of ML and AI algorithms with purpose of independent comparison. So I'm not sure why you're getting so hung up on the code part: majority of papers were describing trash science even in their text in effort to get published and show results.

[cauch]

I'm sorry that the area you are exercising in is rotten and does not have the minimum scientific standard. But please, do not reach conclusion that are blatantly incorrect in areas you don't know.

The problem is not really "academia", it is that, in your area, the academic community is particularly poor. The problem is not really the "replication crisis", it is that, in your area, even before we reach the concept of replication crisis, the work is not even reaching the basic scientific standard.

Oh, I guess it is Occams Razor after all: "It's really strange seeing how many (academic) people will talk themselves into bizarre explanations for a simple phenomenon of widespread results hacking to generate required impact numbers". Occams Razor explanation: so many (academic) people will not talk about the malpractice because so many (academic) people work in an area where these malpractice are exceptional.

[bumby]

But what’s the point of the peer review process if it’s not sifting out poor academic work?

It reads as if your point is talking in circles. “Don’t blame academia when academia doesn’t police itself” is not a strong stance when they are portrayed as doing exactly that. Or, maybe more generously, you have a different definition of academia and it’s role.

I think sharing code can help because it’s part of the method. It wouldn’t be reasonable for omitting aspects of the methodology of a paper under the guise that replication should devise their own independent method. Explicitly sharing methods is the whole point of publication and sharing it is necessary for evaluating its soundness, generalizability, and limitations. izacus is right, a big part of the replication crisis is because there aren’t near as many incentives to replicating work and omitting parts of the method make this worse, not better.

[cauch]

Maybe for the audience here, it is useful to consider that peer review is a bit like scrum. It's a good idea, but it does not mean that everyone who say they do scrum does it properly. And when, in some situation, it does not work, it does not mean that scrum is useless or incorrect.

And, like "scrum", "academia" is just the sum of the actors, including the paper authors. It's even more obvious that peer review is done by other paper authors: you cannot really be a paper author and blame "academia" for not doing a good peer review, because you are one of the person in charge of the peer review yourself.

As for "sharing code is part of the method", it is where I strongly disagree. Reproducibility and complete description allowing reproducibility is part of the method, but keeping enough details blinded (a balance that can be subjective) is also part of the method. So, someone can argue that sharing code is in contradiction with some part of the method. I think one of the misunderstanding is that people cannot understand that "sharing methods" does not require "sharing code".

Again, the "replication crisis" can be amplified by sharing code: people don't replicate the experiment, they just re-run it and then pretend it was replicated. Replicating the experiment means re-proving the results in an independent way, sometimes even with an orthogonal setup (that's why CMS and ATLAS at CERN are using on purpose different technologies and that they are not allowed to share their code). Using the same code is strongly biased.

[bumby]

It seems you are conflating concepts, maybe because you take it personally which it shouldn’t be. The process can be broken, but that doesn’t mean the academic is bad, just that they are part of a broken process. Likewise if a scrum is a broken process, it will lead to bad results. If it isn’t “done properly” then we seem to be saying the same thing: the process isn’t working. As I and others have said, there are some misaligned incentives which can lead to a broken process. Just because it sometimes works doesn’t mean it’s a good process, anymore than a broken clock is still correct twice a day. It varies by discipline, but there seems to be quite a few domains where there is actually more bad publications than good. That signals a bad process.

As others have talked about here, sometimes it becomes impossible to replicate the results. Is it because of some error in the replication process, the data, the practioner, or is the original a sham? It's hard to deduce when there's a lot you can't chase down.

I also think you are applying an overly superficial rationalization as to why sharing code would amplify the replication issue. This is only true if people mindlessly re-run the code. The point of sharing it is so the code can be interrogated to see if there are quality issues. Your same argument could be made for sharing data; if people just blindly accept the data the replication issue would amplify. Yet we know that sharing the data is what led to uncovering some of the biggest issues in replication, and I don’t see many people defending hiding data as a contradiction in the publication process. I suspect it’s for the reasons others have already eluded to in this thread.

[cauch]

I'm not sure what you are saying. The peer review process works relatively well in the large majority of the scientific fields. There are problems but they are pretty anecdotal and are far from counterbalancing the advantages. The previous commenter was blaming the peer review process for "bad incentives that lead to bad science", but that is an incorrect analysis. The bad science in their field is mainly due to the fact that private interest and people with poor scientific culture are getting more easily into this field.

Also, let's not mix up "peer review" or "code sharing" and "bad publication" or "replication crisis".

I know people outside of science don't realise that, but publishing is only a very small element amongst the full science process. Scientists are talking together, exchanging all the time, at conferences, at workshops, ... This idea that a bad publication is fooling the domain experts does not correspond to reality. I can easily find a research paper mill and publish my made-up paper, but this would be 100% ignored by domain experts. Maybe one or two will have a look at the article, just in case, but it is totally wild to think that domain experts just randomly give a lot of credit to random unknown people rather than working with the groups of peers that they know well enough to know they are reliable. So, the percentage of "bad paper" is not a good metric: the percentage of bad papers is not at all representative of the percentage of bad papers that made it to the domain experts.

You seem to not understand the "replication crisis". The replication does not happens because the replicators are bad or the initial authors are cheating. There is a lot of causes, from the fact that science happens to the technology edge and that the technology edge is more tricky to reach, that the number of publications has increased a lot, that there is more and more economical interest trying to bias the system, to the stupid "publish or perish" + "publish only the good result" that everyone in the academic sector agree is stupid but exist because of non-academic people. If you publish scientifically interesting result that says "we have explored this way but found nothing", you have a lot of pressure from the non-academic people who are stupid enough to say that you have wasted money.

You seems to say "I saw a broken clock once, so it means that all clocks are broken and if you pretend it is not the case, it is just because a broken clock is still correct twice a day".

> This is only true if people mindlessly re-run the code. The point of sharing it is so the code can be interrogated to see if there are quality issues.

"Mindlessly re-running the code" is one extreme. "reviewing the code perfectly" is another one. Then there are all the scenario in the middle from "reviewing almost perfectly" to "reviewing superficially but having a false feeling of security". Something very interesting to mention is that in good practices, code review is part of software development, and yet, it does not mean that software have 0 bugs. Sure, it helps, and sharing the code will help too (I've said that already), but the question is "does it help more than the problem it may create". That's my point in this discussion: too many people here just don't understand that sharing the code create biases.

> Yet we know that sharing the data is what led to uncovering some of the biggest issues in replication,

What? What are your example of "replication crisis" where the problem "uncovered" by sharing the data? Do you mix up "replication crisis" and "fraud"? Even for "fraud", sharing the data is not really the solution, people who are caught are just being reckless and they could have easily faked their data in more subtle ways. On top of that, rerunning on the same data does not help if the conclusion is incorrect because of a statistical fluctuation in the data (at 95% confidence level, 5% of the paper can be wrong while they have 0 bugs, the data is indeed telling them that the most sensible conclusion is the one they have reached, and yet these conclusions are incorrect). On the other hand, rerunning on independent data is ALWAYS exposing a fraudster.

> and I don’t see many people defending hiding data as a contradiction in the publication process.

What do you mean? At CERN, sharing the data of your newly published paper with another collaboration is strictly forbidden. Only specific samples are allowed to be shared, after a lengthy approval procedure. But the point is that a paper should provide enough information that you don't need the data to discover if the methodology is sound or not.

[bumby]

>I'm not sure what you are saying.

I'm saying the peer review process is largely broken, both in the quality and quantity of publications. You have taken a somewhat condescending tone a couple times now to indicate you think you are talking to an audience unfamiliar with the peer review process, but you should know that the HN crowd goes far beyond professional coders. I am well aware of the peer review process, and publish and referee papers regularly.

>There are problems but they are pretty anecdotal

This makes me think you may not be familiar with the actual work in this area. It varies, but some domains show the majority (as many as 2/3rds) of studies have replication issues. The replication rates are lowest in complex systems, with 11% in biomedical being the lowest I'm aware of. Other domains have better rates, but not trivial and not anecdotal. Brian Nosek was one of the first that I'm aware of to systematically study this, but there are others. Data Colada focuses on this problem, and even they only talk about the studies that are generally (previously) highly regarded/cited. They don't even bother to raise alarms about the less consequential work they find problems with. So, no, this is not about me extrapolating from seeing "a broken clock once."

>it does not mean that software have 0 bugs

Anyone who regularly works with code knows this. But I think you're misunderstanding the intent of the code. It's not just for the referees, but the people trying to replicate it for their own purposes. As numerous people in this thread have said, replicating can be very hard. Good professors will often assign well-regarded papers to students to show them the results are often impossible to reproduce. Sharing code helps troubleshoot.

>So, the percentage of "bad paper" is not a good metric: the percentage of bad papers is not at all representative of the percentage of bad papers that made it to the domain experts.

This is a unnecessary moving of the goalposts. The thrust of the discussion is about the peer-review and publication process. Remember the title is "one of my papers got declined today" And now you seemingly admit that the publication process is broken, but it doesn't matter because experts won't be fooled. Except we have examples of Nobel laureates making mistakes with data (Daniel Kahneman), or high-caliber researchers sharing their own anecdotes (Tao and Grant) as well as fraudulent publications impacting millions of dollars of subsequent work (Alzheimers). My claim is that a good process should catch both low quality research and outright fraud. Your position is like an assembly line saying they don't have a problem when 70% of their widgets have to be thrown out because people at the end of the line can spot the bad widgets (even when they can't).

>What are your example of "replication crisis" where the problem "uncovered" by sharing the data?

Early examples would be dermatology studies for melanoma where simple bad practices were not followed, like balanced datasets. Or criminal justice studies that amplified racial biases or showed the authors didn't realize the temporal data was sorted by criminal severity. And yes, the most egregious examples are fraud, like the Dan Ariely case. That wasn't found until people went to the data source directly, rather than the researchers. But there are countless examples of p-hacking that could be found by sharing data. If your counter is that these are examples of people cheating recklessly and they could have been more careful, that doesn't make your case that the peer-review process works. It just means it's even worse.

>sharing the data of your newly published paper with another collaboration is strictly forbidden

Yup, and I'm aware of other domains that hide behind the confidentiality of their data as a way to obfuscate bad practices. But, in general, people assume sharing data is a good thing, just like sharing code should be.

>But the point is that a paper should provide enough information that you don't need the data to discover if the methodology is sound or not.

Again (this has been said before) the point in sharing is to aid in troubleshooting. Since we already said replication is hard, people need an ability to understand why the results differed. Is it because the replicator made a mistake? Shenanigans in the data? A bug in the original code? P-hacking? Is the method actually broken? Or is the method not as generalizable as the original authors led the reader to believe? Many of those are impossible to rule out unless the authors share their code and data.

You bring up CERN so consistently that I tend to believe you are looking at this problem through a straw and missing the larger context of rest of the scientific world. Yours reads as a perspective of someone inside a bubble.