The term reproducible research refers to the idea that the ultimate product of academic research is the paper along with the laboratory notebooks [12] and full computational environment used to produce the results in the paper such as the code, data, etc. that can be used to reproduce the results and create new work based on the research.[13][14][15][16][17] Typical examples of reproducible research comprise compendia of data, code and text files, often organised around an R Markdown source document[18] or a Jupyter notebook.[19]
> abstract numerical disciplines
Could you give an example of a non-numerical science?
Yes of course I know what the Wikipedia definition is. Have you ever tried to 'replicate' a paper? Let's say someone did a survey of something. How are you going to 'replicate' that? Or used a particular piece of equipment, or did some measurements in a field? Sure, it's easy to restrict yourself to the data files that go into some software, and say 'oh I used the same data set and software version, my research is 'replicatable'.' My point is that there are a lot of aspects to most research that aren't easy (or even at all) to replicate, even if one is put in all the effort required to document every minute circumstance. There is more to 'science' than CS algos and lab bench biomed.
"Could you give an example of a non-numerical science?"
This is the point you're probably going to argue about what is or is not 'science', so let me use 'scholarship' instead from the fields I work in: law, environmental science and geography. There is a lot of research that is not purely numerical in nature. Note that this doesn't mean that it doesn't use numbers; you misread what I wrote - I didn't say 'numerical disciplines', I said 'abstract numerical disciplines', by which I meant disciplines that require at least some judgement of qualitative properties or inexact measurements of things along the way, somewhere.
No, but I had spent a whole year at university trying to replicate experiments in physics lab. I was studying CS but they had too many physics teachers so we had many of the same courses :)
The equipment was abused by decades of students, you had 45 minutes to do the experiment, and the results usually weren't even the correct order of magnitude :) It was stuff like measuring speed of sound, atmospheric pressure, gravity constant, etc.
Still - at least I knew it's the problem with equipment or with me, not with some obscure details that weren't mentioned and that I can't replicate.
> My point is that there are a lot of aspects to most research that aren't easy (or even at all) to replicate
And my point is that it makes publishing everything that can be adjusted that much more important.
Another question - when there's economic and proffesional incentive to fudge the numbers, and no way to check if the numbers were fudged - how do you trust the results?
I think you too his question too literally: I think he meant "what does it even mean pragmatically and why do you expect funding to go to less novel but more easily reproducible results instead of more novel and harder to reproduce papers?"
Well, reproduced research is inherently more valuable than novel but unsupported research. Otherwise why fund research at all? Just hire alchemists or poets, at least you’ll be entertained.
The current state of research seems to reflect that there is a balance: almost none of the most cited papers go really far out of theirs way to be trivially reproducible, even in computing where doing so is unusually much easier.
If a paper is very novel and impactful but difficult to reproduce, it doesn't seem to matter much for citation counts as long as people just believe it to be true.
And yet there's no revolution among funding sources to send money away from those and instead towards researchers who spend more time on it.
funding should go to more reproducible studies because those are the ones that are usefull. What use is a study saying that X will cure cancer Y if noone can reproduce that and verify?
