>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.