In 1998-1999, I was the organizer for a Bioinformatics course in Sao Paulo Brazil, funded in some indirect way by the National Academy of Sciences (as I recall). In addition to paying for the faculty (about 6 people) to spend a week teaching, the budget also paid to purchase an SGI server, which was a standard machine to run the bioinformatics software. The course ran for a week in January, 1999, and we had ordered (and paid for) the machine by July, 1998.
A group of us arrived in Sao Paulo on Thursday (before the course was scheduled to start on Monday) to begin configuring the machine -- downloading/transferring software and databases. We did not trust the internet, so we brought the stuff we needed on a laptop.
We got there thursday, to find out that despite a 6 month lead-time, the machine had not been delivered. Not only that, the weekend we planned to configure the machine, the power to the campus was scheduled to be shut off so that some of the 60+ year old infrastructure could be replaced (our host got a special dispensation to keep the power on in the building we were using). After many frantic calls, the machine was delivered around 5:00 pm on Friday. We worked frantically through the weekend, and managed to get a few things working, a few hours before our first computer lab. Fun times.
While it is true that departments often fund their graduate students for the first year (or possibly 2) out of their own budgets, their budgets are largely determined by the generosity of their Deans (who got the money from indirect costs from grants) or their own indirect costs. And they will not be admitting students if they do not see a clear path for them to be externally funded after their first year or two.
This is not true for PhD programs in top-ranked institutions. It may have been true 20+ years ago, but today it is very difficult to buy your way into a graduate program.
There are no un-funded graduate (PhD) students in the sciences and engineering at MIT (or any other top-ranked graduate program). The number of graduate student admissions is directly tied to the amount of external funding. If the faculty do not have the grants, their departments cannot admit students.
Amusingly, 15 min ago, the animation did not work on my MacOS Safari (Sequoia), but it was visible on Chrome. Now (1600 UTC-4), it is animated on Safari.
Apple Keychain has a number of old bugs that have caused me to have to resort to this strategy several times. The most common problem is having a secure note that you can open, but then immediately disappears (closes). Copying over an older keychain database can sometimes solve the problem.
>>> If I buy a can of soup and find glass in it, I have a valid claim against the manufacturer. It's a matter of holding someone accountable for fraud or negligence, not a matter of regulation. The proper route is a court, not a bureaucratic agency that preemptively dictates production methods on the assumption that every manufacturer is a potential prisoner.
This is a common conservative trope. We don't need regulation because customers can always sue. (Famous interview with Milton Friedman.) Good luck finding a lawyer who will sue because of some glass in your soup can, or, for more serious cases, who can out last (or match the spending of) a billion dollar corporation. Yes, sometimes the underdog wins. Rich people can sue, and may not need the governments regulatory help. For most people, there is absolutely no recourse, particular for technically complex things, like prescription drugs.
The idea that the legal system can consistently make better informed technical decisions than government scientists is not well supported by the evidence.
Re self-correcting science. In the area I am most familiar with (basic life sciences), correction happens pretty quickly. But I don’t worry about public policy much.
But I’m comfortable arguing that where science intersects with policy, fraud plays a very minor role. I suspect that most policy “mistakes” (policies that were adopted and then reversed) are more about the need for a policy in the absence of data (covid and masks), or subtle tradeoffs (covid and masks), or a policy choice that seems slightly better than an alternative (mammography) but also has poorly understood harms. Policy involves politics, and science unfortunately plays less of a role than one might like (and fraudulent science an even smaller role). This is not my field, but I cannot think of policies that were reversed because of discoveries of fraud (perhaps thalidomide and other drug approvals).
It is useful to distinguish between "effective" scientific fraud, where some set of fraudulent papers are published that drive a discipline in an unproductive direction, and "administrative" scientific fraud, where individuals use pseudo-scientific measures (H-index, rankings, etc) to make allocation decisions (grants, tenure, etc). This article suggests that administrative scientific fraud has become more accessible, but it is very unclear whether this is having a major impact on science as it is practiced.
Non-scientists often seem to think that if a paper is published, it is likely to be true. Most practicing scientists are much more skeptical. When I read a that paper sounds interesting in a high impact journal, I am constantly trying to figure out whether I should believe it. If it goes against a vast amount of science (e.g. bacteria that use arsenic rather than phosphorus in their DNA), I don't believe it (and can think of lots of ways to show that it is wrong). In lower impact journals, papers make claims that are not very surprising, so if they are fraudulent in some way, I don't care.
Science has to be reproducible, but more importantly, it must be possible to build on a set of results to extend them. Some results are hard to reproduce because the methods are technically challenging. But if results cannot be extended, they have little effect. Science really is self-correcting, and correction happens faster for results that matter. Not all fraud has the same impact. Most fraud is unfortunate, and should be reduced, but has a short lived impact.
It is perhaps worth noting that the 25,000 bit flips/out of 470,000 crashes (in a week) are probably not coming from all Firefox users. It would be useful to know how many of those crashes (and bit flips) are happening on the same machine. And whether the crashes/bit flips continue on the same machine continue from week to week.
I can certainly imagine that a very small fraction of Firefox users are generating these results, so that bit flips are not a problem generally.
Nice display, but it starts off with misleading measurements of DNA. The spacing between DNA base pairs is 0.34 nm, so the 10 base pairs pictured are in fact 3.4 nm. But the DNA in a single human cell is about 2 meters, and chromosome lengths vary from 2 to 10 cm. I am skeptical of the hemoglobin vs ribosome sizes as well; hemoglobin has a molecular weight of about 60,000, while ribosomes weigh more than 5 million.
Before Postscript, tektronix 4010 was the language of graphics, at least for biologists drawing phylogenetic trees or reassociation curves. The phylip phylogenetics package, introduced around 1980, still has the option to generate trees in tek4010 format.
Perhaps I need AI to use it.