| >What people don't realize is that reproducing from scratch the algorithm is also very very efficient. This is where we differ. Especially if the author shares neither the data or the code, because you can never truly be sure it's a software bug or a data anomaly or a bad method or outright fraud. So you can end up burning tremendous amounts of time investigating all those avenues. That statement (as well as others about how trivial replication is) makes me think you don't actually try to replicate anything yourself. >there is a contradiction in saying "people will study the code intensively" and "people will go faster because they don't have to write the code". I never said "people will go faster" because they don't have to write the code. Maybe you're confusing me with another poster. You were the one who said sharing code is worthless because people can "click on the button and you get the same result". My point, and maybe this is where we differ, is that for the ultimate goal is not to create the exact same results. The goal I'm after is to apply the methodology to something else useful. That's why we share the work. When it doesn't seem to work, I want to go back to the original work to figure out why. The way you talk about the publication process tells me you don't do very much of this. Maybe that's because of your work at CERN is limited in that regard, but when I read interesting research I want to apply it to different data that are relevant to the problems I'm trying to solve. This is the norm outside of those who aren't studying the replication crisis directly. >I say "bad paper that turns out to have errors (involuntary or not) are anecdotal" My answer was not conflating peer-review and code sharing and replication (although I do think they are related). My answer was to give you researchers who work in this area because their work shows it is far from anecdotal. My guess is you didn't bother to look it up because you've already made up your mind and can't be bothered. >I ask you to give example where the replication crisis was avoided by sharing the data, you talk about bad papers that turns out to have errors Because it's a bad question. A study that is replicated using the same data is "avoiding the replication crisis". Did you really want me to list studies that have been replicated? Go on Kaggle or Figshare or Genbank if you want example of datasets that have been used (and replicated), like CORD-19 or NIH-dbGaP or World Values Survey or any host of other datasets. You can find plenty of published studies that use that data and try to replicate them yourself. >how on hell CERN is not bursting with fire The referenced authors talk about how physics is generally the most replicable. This is largely because they have the most controlled experimental setups. Other domains that do much worse in terms of replicability are hampered by messier systems, ethical considerations, etc. that limit the scientific process. In the larger scheme of things, physics is more of an anomaly and not a good basis to extrapolate to the state of affairs for science as a whole. I tend to think you being in a bubble there has caused you to over-extrapolate and have too strong of a conclusion. (You should also review the HN guidelines that urge commenters to avoid using caps for emphasis) >"sharing the code...but it's not "the good practice"" I'm not sure if you think sharing a single unsourced quip is convincing but, your anecdotal discussion aside, lots of people disagree with you and your chemist friend. Enough so that it's become a more and more common practice (and even requirement in some journals) to share data and code. Maybe that's changed since your time at uni, and probably for the better. |
> Especially if the author shares neither the data or the code
What are you talking about. In this example, why do you invent they are not sharing the data? That's the whole point.
> A study that is replicated using the same data is "avoiding the replication crisis"
BULLSHIT. You can build confidence by redoing the experience with the same data, but it is just ONE PART and it is NOT ENOUGH. If there is a statistical fluctuation in the data, both studies will conclude something false.
I have of course reproduced a lot of algorithm myself, without having the code. It's not complicated, the paper explains what you need to do (and please, if your problem is that the paper does not explain, then the problem is not about sharing the code, it's about paper badly explaining).
And again, my argument is "nobody share data" (did you know that some study also shares code? Did you know that I have occasionally shared code? Because, as I've said before, it can be useful), but that "some don't share data and yet are still doing very good, both on performance, on fraud detection or on replication".
For the rest, you are just saying "my anecdotal observations are better than yours".
But meanwhile, even Terence Tao does not say what you pretend he says, so I'm sure you believe people agree with you, but it does not mean they do.