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).

Isn't this almost certainly by design?

I feel like I purchased a year's worth of problem-free phone experience for nearly $CAD900. To me, that's not great value.

For some, it was probably one of THE things that kept them using R over something else. Yes, definitely worth it.

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.

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?

There will be (are in fact) one-click, cloud-hosted solutions for analysis, but given how quickly tools have evolved in this space, there will always be groups wanting to run on their own hardware so as to experiment with the latest new developments.

1. As mentioned by others, there really is very little data on HD failure rates.

2. When you first published your blog on failure rates across HD brands/models and SMART attributes many, myself included, suggested it might be more illuminating as a predictive modelling exercise. This data allows others to do that now, which is great!