@Canageek I'm very interested in that space, in so far as it seems to intersect with the ideas of Don Knuth's literate programming. But I also admit to be slightly unclear as to what domains it is best for. I think it's big with the data science crowd?

@rafial Yeah, I'm a chemist who works with very small data, no statistical analysis or anything like that. About the most I have to do is "Adjust this data to account for the lamp response on that day" or "Normalize and scale these two spectra against one another"

@Canageek if I had to guess, I would say if your domain involved exploration of data sets, with visualization as a key component of that, the notebook things might well be a killer app.

@rafial @Canageek You absolutely need a beefy server, fast connection (local if possible), and fast front-end CPU to make notebooks usable. They're a massive increase in resources needed, but they're one of the few "this is better than what we did before" technologies I've seen in 40 years.

@mdhughes @Canageek interesting to hear. And Julia is definitely on my stack of "stuff I need to learn more about."

@rafial @Canageek There's annoyances with #Julia: Startup time is many (5-30) seconds, indexing starts at 1 unless you do crazy things to "fix it", docs can be confusing/nonexistent.

But it has a great method dispatch/object model which is not just "static Smalltalk" like most, it's super fast at runtime, and like I say the REPL is amazing.

@mdhughes I mean for those who, unlike us, didn't come up through C and "indexing is really pointer multiplication", index base 1 kinda makes sense, nah?

@rafial
"Should array indices start at 0 or 1? My compromise of 0.5 was rejected without, I thought, proper consideration."
—Stan Kelly-Bootle

@urusan @rafial Whomever wrote this is an idiot that has never worked in actual physical sciences where you have actual physical experiments, just FYI. Like 10% of my labs works involves computers at all. But sure, science now moves at the speed of software.

@urusan @rafial It totally ignores the advantages of PDF though, like the fact there are a stack of independent implementations that can view it, which means we will still be able to read these files in 50 years, unlike whatever format they are using, and we can even print them out on paper to edit them (for example, how my boss and I do it, as he doesn't know LaTeX, which is what I write in).

Or the fact that like, 90% of scientists don't know how to program and are unlikely to learn.

@urusan @rafial In my lab right now... I can program a little, one post doc has spent some time doing python tutorials, and I think that is it, out of ten people.

Organic labs might have less then that, as there is a reputation at least that organic chemists and programming are like oil and water: if you are good at one, you are likely terrible at the other.

@urusan @rafial You public the source files as ESI the way we do with input files in crystallography?

@urusan @rafial (I might be giving opinions different then normal due to spending two days trying to get this figure to compile and dealing with moving to lualatex from pdflatex as a result)

@urusan @rafial Right but then you are stuck with a system 0 people understand instead of 1 person.

If I wanted to use GNUplot as it was built I could use the script a former grad student passed on, but I want it in LaTeX so the fonts and such match.

(Honestly, if I could go into campus it would be done, but the software we have that makes these graphs is trapped on one computer, and not worth me going into campus during a pandemic to get, at least not yet)

Honestly, if you are going to try and replicate any of my work you aren't going to go to my data: You are going to synthesize the compounds yourself and take the measurements on your equipment against your standards so that it doesn't turn out to be some dumb different between how I set up my experiments and how you do, or some defect in my hardware, etc etc.

@clacke @urusan @rafial Well yeah, but most scientists don't do statistics. Most chemists, most biologists, geologists, etc.

Like, there is a reason computational is a subfeild of every discipline.

I think it is going up, due to more stats and computations being used, but I also think we are way to reliant on stats these days and use them instead of getting good data.

@urusan @clacke @rafial Right, I've been frustrated with this in science for decades. We should do half as many studies and put twice or more as much funding into each one so we have actually decent stats.

For example, lately you have to justify the minimum number of rats for ethics committees for any experiment. Fuck that, use 4 times as many so we can be confident in our work instead of justifying it to heck and back.

@urusan @clacke @rafial (This is part of why I picked a field where all the stats were baked into software and validated before my Dad was born, and the sample sizes for everything else is around 10^26 (ie the number of atoms in solution)

@urusan @clacke @rafial Nope, its counted as bad, but justifiable so you have to minimize the number you use, at least as I understand it.

Likewise, academic human studies are typically very underfunded, which is why there is such a bias towards small sample sizes and all the participants being undergrads found on campus.

> Listen, my grandpa was a mason, from Borlänge, Gösta was his name, construction worker, he used to say this:
> "In my day, we made an honor out of building houses as strong as possible, so they would last as long as possible. But now, they have computers that calculate how weak they can build a joint without it falling in on itself."
> *ding*
> Ain't it weird?

@clacke @urusan @rafial I've heard from engineers that anyone can build a bridge. And engineer can build a bridge that only JUST carries the required load to make it not cost a fortune

Thanks @urusan, I found the article interesting, and it touched on the issue how to balance the coherence of a centrally designed tool with the need for something open, inspectable, non-gatekept, and universally accessible.

PDF started its life tied to what was once a very expensive, proprietary tool set. The outside implementations that @Canageek refers to were crucial in it becoming a universally accepted format.

I think the core idea of the computational notebook is a strong one. The question for me remains if we can arrive at a point where a notebook created 5, 10, 20 or more years ago can still be read and executed without resorting to software archeology. Even old PDFs sometimes break when viewed through new apps.

@rafial @urusan Aim for longer then that. I can compile TeX documents from the 80s, and I could run ShelX files from the 60s if I wantd to.

@Canageek @urusan oh hey, I'm all for longer, eventually we'll need centuries. But I've also seen the hair pulling it can take to revive archived source code from even a couple years back, and realizing we've got to start by getting to the point where even a decade is a reliable and expected thing.

@rafial @urusan Fair, though I'd say source code is pointless and what we need is more focus on good, easy access to the raw data.

If you can't reproduce what was done from what is in the paper, you haven't described what you've done well enough, and redoing it is better then just rerunning code as a bug might have been removed between software versions, you might notice something not seen in the original, etc.

@urusan @rafial That would be my favoured approach. Raw data plus a good set of standard statistical tools. More basic analysis = less care if the EXACT toolchain is lost.

If you can't redo the analysis in alternatives then it isn't good science in the first place.

@urusan @rafial No, they've kept updating the software since then so it can use the same input files and data files. I'm reprocessing the data using the newest version of the software using the same list of reflections that was measured using optical data from wayyyy back.

The code has been through two major rewrites in that time, so I don't know how much of the original Fortran is the same, but it doesn't matter? I'm doing the calculations on the same raw data as was measured in the 60s.

There is rarely a POINT to doing so rather then growing a new crystal but I know someone that has done it (he used Crystals rather then Shelx, but he could do that as the modern input file converter works on old data just fine)

@urusan @Canageek one other thing to keep in mind is that data formats are in some ways only relevant if there is code that consumes it. Even with a standard, at the end of the day a valid PDF document is by de-facto definition, one that can be rendered by extent software. Similar with ShelX scripts. To keep the data alive, one must also keep the code alive.

@rafial @urusan No, what you need is a good description of how the data was gathered. Analysis is just processing and modeling and can be redone whenever. As long as you know enough about the data.

There are *six* programs I can think of that can process hkl data and model it (shelx, crystals, GSAS-II, Jana, olex2) so it doesn't REALLY matter which you use or if any of them are around in ten years as long as there is *A* program that can do the same type or better modeling (reading the same input file is a really good idea as well as it makes thing easy)

If a solution is physically relevant any program should be able to do the same thing.

@rafial @urusan Standardized data formats are more important then software.

Simple, standardized analysis is better then fancy, complicated work.

@rafial @urusan @Canageek And this is why all software should be written in FORTRAN-77 or COBOL.

@mdhughes @rafial @urusan I mean, that is why Shelx first major version came out in 1965 and the most recent one in 2013 (last minor revision was 2018)

I mean, modern versions of Fortran aren't any harder to write them C, which is still one of the most used programming languages in the planet, I don't see why everyone makes fun of it.

@Canageek @rafial @urusan I'm kind of not making fun of Fortran, though the last time I saw any in production it was still F-77, because F-90 changed something they relied on and was too slow; I last worked on some F-77 for the same reason ~30 years ago.

I am indeed making fun of COBOL, but it'll outlive us by thousands of years as well.

Stable languages are good… but also fossilize practices that we've improved on slightly in the many decades since.

@mdhughes @rafial @urusan Isn't Fortran-90 like three versions old now? I know I used it in 2005 because you could talk to F77 with it and we had certified hydrodynamics code in Fortran 77 that was never going to be updated due to the expense of recertifying a new piece of code

@Canageek @rafial @urusan Yes, newer Fortrans are actually useful for multithreading (F-77 can only be parallel on matrix operations, IIRC). And yet I expect F-77 to be the one that lasts forever.

> SHELX is developed by George M. Sheldrick since the late 1960s. Important releases are SHELX76 and SHELX97. It is still developed but releases are usually after ten years of testing.

@clacke @mdhughes @urusan @rafial yeah, the big worry is that George Sheldrick is getting very, very old and there are wonders if anyone will take over maintaining and improving the software when he dies. luckily it's largest competitor does have two people working on it the original author and a younger professor so it has a clear succession path.

@urusan @clacke @mdhughes @rafial See, this is a lot of focus on getting the exact same results, which for science I think is a mistake.

You don't want the same results, you want the *best* results. If newer versions of the code use 128-bit floating point numbers instead of 64-bit, GREAT. Less rounding errors.

Its like, I can create this model in Shelx or Crystals. They don't implement things EXACTLY the same, but a good, physically relevant model should be able to be created in either. If I try and do the same thing in two sets of (reliable) software and it doesn't work in one, perhaps I'm trying to do something without physical meaning?

Like, it shouldn't matter if i use the exact same Fourier transform or do analysis in R, SAS, or Python. It should give the same results. Stop focusing on code preservation and focus on making analysis platform agnostic.

@urusan @clacke @mdhughes @rafial If you can only do your analysis on ONE specific piece of code one ONE platform how do you know your results aren't due to a bug?

Also it is going to be *helllll* for someone in 20 years. I know a grad student in physics who has to revisit some code his prof wrote when he was in grad school. On the upside it is apparently well documented. On the downside, the documentation is all in Polish as that is the profs first language and where he went to grad school, whereas the grad student only speaks English.

Now nuclear physics is a bit of an exception, but asdfljk that sounds like hell.