Random insight of the night: every couple years, someone stands up and bemoans the fact that programming is still primarily done through the medium of text. And surely with all the power of modern graphical systems there must be a better way. But consider:
* the most powerful tool we have as humans for handling abstract concepts is language
* our brains have several hundred millenia of optimizations for processing language
* we have about 5 millenia of experimenting with ways to represent language outside our heads, using media (paper, parchment, clay, cave walls) that don't prejudice any particular form of representation at least in two dimensions
* the most wildly successful and enduring scheme we have stuck with over all that time is linear strings of symbols. Which is text.
So it is no great surprise that text is well adapted to our latest adventure in encoding and manipulating abstract concepts.
@rafial Both accurate and also misses the fact that Excel is REGULARLY misused for scientific calculations and near-programming level things since its GUI is so intuitive for doing math on things.
Like, GUI programming is HERE, we just don't want to admit it due to how embarrassing it is.
@rafial Now what we need to do is make a cheap, easy to use version of it that is designed for what scientists are using it for it. Column labels, semantic labels, faster calculations, better dealing with mid-sized data (tens of thousands of data point range), etc
@Canageek I'm wondering, given your professional leanings if you can comment on the use of "notebook" style programming systems such as Jupyter and of course Mathematica. Do you have experience with those? And if so how do they address those needs?
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.
@Canageek @rafial Well, I don't personally think this kind of dynamic documentation is going to fully replace static documentation. Static webpages haven't been fully replaced by dynamic webpages either.
However, the growing notebook ecosystem does address issues that didn't have well-formed norms around them before.
In addition to the obvious CS/Math applications, there's a lot of areas where complicated statistical work gets done with code, and then how do you publish that?
Jupyter handles the software dependencies for you as part of the kernel selection.
As these norms become better established over time, the tools to deal with these issues automatically will just be there.
Just dropping some source code on someone is putting the burden of replication on them and not on the platform.
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.
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