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Formatting is the problem??? Are you kidding me? That could have been a problem 15 years ago but any paper being submitted requires that they be properly formatted already. Developing a new format? Doesn't have to be perfect and the same format can be used by almost every journal. By the way that's basically what most large publishers do.
All I said is that universities already have most of the infrastructure and people necessary to do the necessary work. Just going the extra 100 m would cost them very little and would, eventually save perhaps more than 50% of library budgets.
Clearly you don't have any imagination at all and don't seem to realize the huge research support infrastructure that exists in universities and funding agencies. in many places, usually where public universities are common, funding agencies often are the ones responsible for journal subscriptions. They (and any large university really) spend millions of dollars every year on subscriptions. These same funding agencies have people that receive research grant proposals and distribute them for analysis. Does that remind you of anything???
The infrastructure and people are already there. And, by the way, pooling resources doesn't necessarily involve "expensive job of coordinating". For instance, some univerity or department within a university decides to be responsible for a journal. The only coordination necessary is for other universities to not create the same journal.
Pooling among different universities would drop the publication costs to nearly zero. Hell, if each university had one person doing this work and a single server to handle the work, there wouldn't be enough work to go around. And libraries would be saving a large percentage of their budgets.
The publishers are today middleman parasites.
I don't think that being easy is python's main advantage. Using a dynamic environment were you can type code that gets executed immediately and were you can explore the data is a really big help. On the other hand, the same could be done with R, Matlab, Octave or Scilab and it is done. In some ways these languages are better suited than python because they were designed to do math, or more specifically matrices/arrays very well and might have better syntax for that. But then doing anything else increasingly becomes a pain once the problem becomes larger or more complex and that's where, IMHO, python gains an advantage. Better module/OOP environment, better GUI,etc.
By the way, I work on scientific computing, using spectral element methods in computational fluid dynamics and I also work on a wind tunnel and I do lot's of data acquisition and processing. Right now I use C++ for lower level stuff (and bottlenecks) and R. I have been seriously considering switching to Python to have an easier environment to maintain.