> So, let's break down the fields of CS for which this should be applicable:
> So, is this focused on ML and ML-using code and experiments?
Your completely missing the point. Please look into 'Computational Science' (or Scientific Computing, or Numerical Analysis), that applies to 80%+ of all disciplines that exist today (e.g., computational physics, comp. biology, comp. economics, comp. aspects of engineering disciplines, the list goes on).
Yup, I can see it for computational experiments or "applied CS" fields. I realized this soon after I posted the comment, but I didn't bother to update my comment.
However, this still isn't clear in their website. I will give them the benefit of the doubt since they are early in their project, but I think it would behoove them to nail down their mission sooner rather than later.
This is probably what I get being in the CS bubble. =)
Well to their credit, they did mention "scientific research reproducibility" which is a very well known phrase in computational circles.
But I agree, it would help if they expand on this from the pure CS point of view. Especially if they mention things like containers, CS people would be interested in finding out what they're up to.
I guess you could also say that CS is one of those "applied math" fields :)
Seriously though, this kind platform is a critical component in scientific reproducibility. The dream is that we can have code, data, and the results of the composition of the two in the same revision control system. A minimal layer to allow the execution of linux software would support the use of legacy code and binaries in this new platform. Javascript has its advantages, but it's a waste to build a data RCS and require all functions on the data to be written in it.
And to go a bit further, it's not just for science. For example, you could write a HN clone in dat. I could fork it and get both your code and all the posts.
> So, is this focused on ML and ML-using code and experiments?
Your completely missing the point. Please look into 'Computational Science' (or Scientific Computing, or Numerical Analysis), that applies to 80%+ of all disciplines that exist today (e.g., computational physics, comp. biology, comp. economics, comp. aspects of engineering disciplines, the list goes on).