Could you provide examples of healthcare executives held personally liable for harm resulting from reckless decision-making? I have never heard of such a thing happening in healthcare so framing CEO responsibility as a solution to the problem sounds like a stretch to me.
Some examples: Elizabeth Holmes got canned for lying to investors, not harming patients. Purdue Pharma plead guilty to misleading regulators and giving doctors kickbacks, not causing some hundreds of thousands of opioid deaths, but no Sackler family members were personally tried.
Only through the first two paragraphs but a little turned off by the "everybody else is wrong, we are right and it's this one specific thing" attitude when it the topic is understanding something as complex and opaque as the global economy
I'd be really interested to know of anybody making money with those topics (and doesn't already have their own domain-specific practice for the problem)
This code is jibberish to me, but it appears the target is just parsing how many atoms are in a molecule string of some representation. That's cool, but to do just about anything useful in chemistry we need the bond graph (and often more - bond orders stereochemistry, plus much more for biopolymers).
I'm a little surprised to see you dismiss type-checkers disagreeing with each other - there are more than a few cases of mypy and pyright disagreeing and brushing them aside as rare enough to be irrelevant is in conflict with my experience over the years.
I'm happy you believe that the community can converge on unified standards - I wish I shared the optimism - but that's not where we're currently at and years-long efforts don't help me now. (For example, I'd love to use PEP 695 generics since I found a use case for them around 18 months ago but I can't until I can get away with not supporting 3.11, which is years out.) Maybe everything is perfect in 2028 or so - I'd be thrilled - but that doesn't help the pitch for annotations being a value add for people who are worried about the current jobs.
> as for the split in type checking tools, it is not as bad as you think
I'm surprised you already know it's not as bad as I think - have you been able to use it? Working on a team that mixes mypy and pyright is pretty frustrating since they don't agree on everything (i.e. when one changeset passes on one and fails on the other) and I see no reason to believe the inconsistencies will become more rare when the number of opinions goes from two to three
- there's already a major split in Python type-checking tools, if there's a third that doesn't agree with either of them it'll be a mess for projects to deal with
- astral has been hiring like mad recently and has yet to communicate that they can actually make money ($5 million doesn't last forever)
- does it actually exist? is this currently a closed-source codebase, or is "we're building" future tense?
Possibly in some medical or social science fields, I don't know. I know there is not such an issue in chemistry and materials science. There also may be some complications for collaborations with industry, but that's kinda a different situation. For people whose career development is not strongly tied to reproducibility of their work (a.k.a. everybody) it's just another step in the overly complex process of publishing in for-profit journals. Funding agencies generally aren't going to punish people for using this excuse and the watchdogs/groups concerned with reproducibility have no teeth.
Not an excuse, but journals don't make it easy to share files, as hard as that is to believe. Some will only take PDFs for supplemental information and many have garbage UIs, stupidly small file size limits, etc. Just uploading to a repo (or tagged release) on GitHub is common these days because there is much less friction.
> I wonder if there is any requirement for researchers to at least publish their data set for statistical analysis and further research.
Not generally, though the tide is slowly turning in the right direction. Unfortunately many laws/policies pushing for openness and transparency in research are sidestepped with the classic "data available upon request," a.k.a. "I promise I'll share the Excel files if you email me" (they will not).
I think there is truth to the general principle you refer to, but I don't think it accurately describes what I saw skimming experts' comments in the linked thread. I'm an outsider to medical research but have experience in other parts of STEM research at universities. Here I saw a plenty of nuance, documentation of historical skepticism, concern over broad perception, and plenty disagreement over technical points. Far from a unified kool-aid drinker sort of situation. And I think there has been plenty of changes of opinions in the Alzheimer's field in recent years given the number of failed drugs - which goes against the idea that these scientists are following their career over the evidence.
Hopefully so, but there will probably always be a back-and-forth between frauds and journalists in the same way security is always a competition. At least the easy frauds are more likely to be caught today.
Harboring skepticism of the work people did with a seemingly fraudulent researcher is a good idea. Dismissing everybody in a field whether or not their work is fraudulent is disrespectful approach (not to mention useless).
I'm not asking for people to turn off skepticism or blindly trust researchers. It's not disrespectful to be skeptical.
What is disrespectful is not bothering to read what people have to say before dismissing them as liars who are too vested in "the current research direction" and/or money for their perspectives to matter. It only takes reading a few comments to see that's not happening - for starters, people were skeptical of this group's work for a while now.
Some examples: Elizabeth Holmes got canned for lying to investors, not harming patients. Purdue Pharma plead guilty to misleading regulators and giving doctors kickbacks, not causing some hundreds of thousands of opioid deaths, but no Sackler family members were personally tried.