In the case of vaccines specifically, there is just a surprising amount of things we still don't understand.
Vaccination is positively ancient technology (Smallpox circa 1796), but it is still difficult to say why e.g., some vaccines are broadly effective at producing immunity and some aren't (and what the factors at play are in the recipients), or why we can produce successful vaccines to some diseases and not others.
A specific example: the efficacy of BCG vaccination (tuberculosis) varies by manufacturer and the reasons for why that is are still unclear.
There are a number of large NIH-funded initiatives that aim at understanding the complex cellular and molecular interactions that result in immunity as a result of vaccination under the Human Immunology Project Consortium (a bunch of high profile papers in Cell, etc. over the past few years).
I bought directly and was passed around by Google and Huawei support for months (and multiple calls; hours of my time wasted - why can't anyone just do support by email anymore?) before ultimately giving up on getting the constant battery issues addressed. Initial contact was precisely 3 weeks outside of the warranty period for the now well-documented phone shuts off when battery reaches [15|30|50]%.
I feel like I purchased a year's worth of problem-free phone experience for nearly $CAD900. To me, that's not great value.
Overemphasis on commercialization is a common complaint of the current funding situation in medical research, so I don't think we're failing for lack of interest in commercial exits. In fact, many scientist are serial entrepreneurs.
Moreover, many discoveries that went on to make a lot of people a lot of money had no obvious commercial application when they were first being researched... Predicting future markets is hard.
I think this is true, but it's also the case that businesses that cater to the very rich are doing very well. My wife works for a luxury retailer who's Vancouver location is seemingly supporting the entire company with it's sales. Revenue has basically tripled over the past 4-5 years.
Any chance you might be able to expand on this? I don't dislike OneDrive, but I've started to run into more and more issues with various machines falling out of sync unless I restart the OneDrive service to force changes to be detected... Dropbox has the same issue on those machines, so I'm not really sure what 'causes it.
Many (most) of the things we're interested in measuring in blood are bound to some sort large macro-molecular assembly... be it cells, lipid vesicles, large protein agglomerates, etc.
Faster because it isn't (currently) using compression (which rds uses by default) or faster period?
Either way, the idea of mixed Python/R pipelines with feather file intermediates input/outputs is pretty sweet. Learn in scikit, save to feather, plot in ggplot2... using Make to tie the pieces together?