3,561 karma · joined May 21, 2013
That said, we evaluated TileDB for genomics recently and found it lacking for our use case.
I totally agree that simulations of the most likely events would have been nice. The show could have benefited from more strict adherence to the scientific method. Alas, that probably would have made the show less appealing for a large segment of the audience.
I was also never brave enough to have my autograders submit grades directly to Canvas after the first time I did it. I confidently had it directly submit grades the first week during the first year I implemented autograders. Half the class forgot a shebang on their shell script, and the autograder happily gave them a 0.
For bonus fun times: Gitlab's runner timeout is pretty much nonexistent if you're using the shell runner. It just won't kill them if they take too long, so I had to write our own management system for runners. Silly university IT not support containers :(
Though it does require a bit of between the line reading
A vanishingly small number of people are paying for their own PhD in the US. You are expected to be _funded_ though. Most programs give you 1-2 years of guaranteed funding (either through research or teaching assistantships) to give you time to find a PI if you didn't start the program with one and apply to grants. Having to secure grants is much different than paying your own way.
Most PhD students are either directly out of a bachelor's program or worked, maybe 2 years, in the field they want to do a PhD in. They're still most definitely in their early to mid-twenties. Are you thinking post-docs?
Length of stay is really dependent on your field of study and your specific work. I have friends who took 3 years to do computationally focused doctorates and some who took 6 years to do biology focused ones. You simply cannot make living things grow faster through sheer force of will.
I guess my last note would be that there's a reason US biology/biochem/bioME PhDs are paid a premium over their European colleagues internationally. And that's probably related to time spent getting their degrees and the depth and breadth of their experiences in the process
1. Private networks -> keep classes isolated 2. Shared drives -> mount read-only class data 3. Create machine images -> build out the 'base' class image and spawn new instances from it 4. An API for spinning up and down
It seems like you are positioning yourselves as more for individuals and less for companies. It's not clear fo me from the landing page, but does Vagon require you download and install a client? Or is there a browser-only option? If we were to spin up, say 35 VMs, and then destroyed them a week later, is that $7.99/month charged for each machine or just once? Is it pro-rated if you don't use the whole month?
It just so happens that when they do share your data it may be with a Pharmaceutical company
But if I had to guess, $1000 is probably in the 95th+ percentile of spending for teams <20 (or any sufficiently small group) -> thus this is letting startups spend on Github resources without worrying about running out of credits for two years
Not linked on the current home page for whatever be the reason
Cue the deep state conspiracy theories, I suppose....
Those two costs are what makeup the $1000/human genome stat, we don't factor in the human and consumables components
[1]: https://www.genome.gov/about-genomics/fact-sheets/DNA-Sequen... [2]: https://assets.thermofisher.com/TFS-Assets/GSD/brochures/rap...