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Rather common question on Academia StackExchange for exactly the scary reasons mentioned. Almost every time, the rational is something close to "my professor will ruin my life if I don't do what they say."

My own research is creepy and I want to leave (except my professor will ruin me): https://academia.stackexchange.com/questions/67897/i-dont-wa...

My professor is using us to rig the IEEE elections (except afraid of losing scholarship and grad courses): https://academia.stackexchange.com/questions/69486/what-shou...

My professor is rigging nanotechnology data and plagiarizing (except I don't stand a chance against him): https://academia.stackexchange.com/questions/31265/my-profes...

Those are terrifying

What a mess.

After the "metrics" revolution we started to measure and incentivise everything. It has not worked well for science.

Measuring the wrong thing, even if it is highly correlated to what you really want to measure, often results not in a slightly suboptimal result, but in the worst possible result.

It's not a failed system ism.. But the sad truth is, we should go back to the 1800 were proof was travelling with others to the lab and watch the experiments happen life. Reproduction by the author under expert supervision and with a camera recording of the whole ordeal.

That and reputations trees. The tree that spawned this rotten apple, root to roof, has spawned many more.

The first one is odd, but far less scary than the other two.

That's unfortunate.

There are bad professors in academia just like there are bad bosses in industry.

Hopefully anyone with bad ones will find better ones.

The big problem in academia very often is that you cannot simply take your research and finish your PhD elsewhere. With jobs you mostly can switch without losing much. Obviously depends on the situation.

> With jobs you mostly can switch without losing much

Seniority, bonuses, equity vesting, resume damage

> Seniority, bonuses, equity vesting, resume damage

Relatively minor. Most senior people I know who leave with bad blood end up with equally senior roles, and often "fail upwards" to even more senior roles in another company.

Bonuses/Vesting: Very common to get good vesting at a new company to make up for what you're losing. Of the people I know who've moved, it's the exception that didn't get decent replacement for vesting.

Resume damage: What? Person spent 4 years at a company and left. What resume damage? Your experience still counts.

Contrast with a PhD who leaves in the middle of a program: The years he spent are 0. Generally, the experience/knowledge accumulated is not factored into it when he applies elsewhere for a PhD - unless he has some fantastic publication.

Still, this is the good outcome - I've often seen students leave an advisor or school and succeed elsewhere. The real danger is when you get your PhD and the advisor will not write letters of recommendation. Unless you've made some really special connections, it means your academic career is over. You won't get a post doc. You won't get an academic position. You won't get into a national lab. And depending on your field, you won't get a research position in industry either.

In the industry, no boss has that power.

> Contrast with a PhD who leaves in the middle of a program: The years he spent are 0. Generally, the experience/knowledge accumulated is not factored into it when he applies elsewhere for a PhD - unless he has some fantastic publication.

When I relised this kind of power inbalance during my PhD resulting docile behaviour of my collegues and partly myself, I even did not even wanted to chage my supervisour nor make a protest as the power inbalances are normalized. This awareness made my PhD easier as I did not put false hopes and did not invest my leisure time into becoming more specialised or come up with my own ideas which could latter be turned down to not allign with funded project as my collegues did. I found the remedy in fight for a better tooling and investing my leisure time in completely different field in which papers were still not been written by robots.

Those are a thing but probably not as bad. You may gain seniority. The second is unavoidable. The last can be dealt with and there are levels of ethics around that.

Yeah, that doesn't work.

Once you are in a PhD program you are locked in for 5+ years. It's almost as bad as the green card timeline.

I think it's much worse for a grad student. It may have to do with the cultures of industry and the academe.

In industry, competitive hiring and job-hopping are considered to be normal. Confidentiality is assumed, for the most part. There are some stock, face-saving reasons why you're leaving your previous job, that don't open cans of worms. There are lists of questions that you don't ask. And It's not weird if you're changing jobs after a couple of years. Your seniority and promotion clocks don't typically get reset to zero.

People in the industry think that doctoral advisors are something like bosses in companies. They aren't. A Ph.D. is an all-or-nothing experience. When leaving on bad terms, you not only lose a few years of your life, but it is likely to ruin your career. With very competitive hires, an anti-recommendation might be a kiss of death.

Sometimes, this power balance pushes people over the edge. There is a case of a PhD student who, after a struggle with his PI wanting him to publish fraudulent results, had a breakdown and took his own life. https://huixiangvoice.medium.com/the-hidden-story-behind-the...

For foreign PhD students their kids can be ripped out of school and they and their family deported if they can't get another advisor within 30-60 days depending on the visa.

Nope. A student's ability to stay in the US (along with his dependents) is by remaining a student at the university (i.e. registering for a full time load each semester). They're not dependent on whether they have funding/assistantship, or whether they have an advisor.

If you are a doctoral student you need a new advisor to remain in status. This is playing out at my institution right now, a female PhD student had had enough of the sexual advancements of her advisor, who is a favourite of the departmental chair, so she filed a Title IX complaint and needed a new advisor. There was exactly one faculty member who was in a position to take her on, an established figure close to retirement age. US immigration law is fucked up beyond reasonable.

What is going on is this:

To remain in the US on a student visa, you need to be "in status". The government has rules on what that is, and mostly it is that you continue to register for a full load. I do not think the government has specified additional requirements.

However, each university has an international programs office and they are required to specify for each international student whether the person is compliant with the university's guidelines. In your university's case, that office may have decided that "Hey, if you're a PhD student, you can't go too long without an advisor, because that would mean you're not making progress towards a PhD." I've seen them say similar things if it's taking too long to get a degree (e.g. over 6 years for undergrad or 8 years for PhD). Or if the GPA is too low and you're on probation

Typically, these rules apply to all students, and not just international ones. It's essentially the university saying "You're about to be kicked out/expelled, so we can't tell the immigration folks that you are progressing towards your degree"

Still, I really doubt your university has a rule specifying only 30-60 days to find a new PhD advisor. To give you an idea, in many sciences, it's common that you don't have a thesis advisor until after your first year (you focus in the first year on passing your qualifying exams, and only once you pass it are professors interested in taking you on).

If indeed your university is enforcing a 30-60 day rule, it's a really crappy university.

TLDR: The law doesn't require it, but each university has its own policies.

It's essentially the university saying "You're about to be kicked out/expelled, so we can't tell the immigration folks that you are progressing towards your degree"

Yes, exactly that, it was something along the lines of "you are not sleeping with me, consequently you are not progressing to your degree".

Yep, when I was reading this article, as I kept hearing about what Dias was doing to his students, I was getting ready to throttle him.

The students here didn't really do anything wrong, but Dias used his position of authority to essentially blackmail the students into becoming complicit in fraud, and attempted to justify it essentially with an "everybody does it" excuse. Kudos to them for taking the initiative to retract their paper over the head of their PI! That takes a lot of courage, given the immense powers an advisor holds over their students.

> Soon after the CSH paper was published, Jorge Hirsch, a condensed-matter theorist at the University of California, San Diego, began pressing Dias to release the raw magnetic-susceptibility data, which were not included in the paper. More than a year later, Dias and Salamat finally made the raw data public.

Explain to me why Nature would publish a paper without having access to/requiring the data. Could it be Nature wanted to be the first to publish such a ground breaking discovery? Dishonest people are simply a fact of life. The entire point - supposedly - of peer review is to insure that scientist do not make unsubstantiated claims. Job well done Nature. Job well done.

Nature very specifically wants to be the venue where the most important and impactful discoveries are first reported, and they tolerate a very high false positive rate to achieve it. Quick, light peer review is part of that process.

We always joked that if you read it in Nature, it was certain to be wrong, but realistically, it meant that the topic was likely an important one with a small number of competitive groups who could all evaluate each other's papers and will loudly complain about every inconsistency or irreproducible detail.

Peer review is not designed to detect fraud. It is meant to improve the quality of work, by giving a publication a fresh set of eyes who might identify gaps in the original research.

> Peer review is not designed to detect fraud.

Maybe it should be. "Peer reviewed" is taken as a sign of reliability in many contexts, including public policy. If it isn't actually such a sign, then either it needs to change so it is, or we need to stop taking it as a sign of something it isn't.

I am not sure you appreciate the scope of distrusting submissions. Does this mean the reviewer has to clean-slate replicate everything? What if the author spent three years breeding a particular mouse model? Purified 8pg of an antibody extracted from flea tears? Spent six months of HPC time to simulate molecular dynamics? Some science is hard and incredibly difficult and expensive to do.

Even just reevaluating a submitted dataset is no guarantee. Someone committing an elaborate fraud could have faked the raw datasets as well.

> I am not sure you appreciate the scope of distrusting submissions.

What you're saying is, the first option I gave, to fix the peer review process so it is a good indicator of the reliability of research, is not feasible.

If that's the case, that just means we have to pick the second option: stop treating peer reviewed research as if it is reliable.

> If that's the case, that just means we have to pick the second option: stop treating peer reviewed research as if it is reliable.

It is the case, and that second option is exactly what we do. One article is nice, but replication is key before some new finding is accepted. That is exactly what happened with LK-99, for example, which is interesting because it was very public.

> that second option is exactly what we do

I don't think so. I see claims made all the time about science being "settled" purely on the basis of it being peer reviewed.

> replication is key before some new finding is accepted

If only that were true. In many cases it isn't.

That's actually exactly correct. Peer review is supposed to characterize the research in terms of quality, not validate it. Otherwise, those peers who validate it would practically be co-authors.

It's a system built upon honesty. A dishonest person is obviously going to take advantage of it. Really blatant things are caught in peer review, but sneaky fraud will definitely slip through.

> Peer review is supposed to characterize the research in terms of quality

Fraudulent research would obviously not be of the desired quality, wouldn't it?

> It's a system built upon honesty.

But if the purpose is to characterize the research in terms of quality, this can't be right. Since fraud is known to happen, and there are known to be incentives for fraud, any process that claims to judge the quality of research would have to have some way of checking for fraud.

I think you're misunderstanding what I mean by "quality" in this context. It's not quality as in something like meat, it's quality as in "a quality education". Does it go above and beyond vs. do the bare minimum to address the scientific question proposed by the authors.

Something can appear to be of very high quality and yet still be completely fraudulent. This occurs in countless fields; art, counterfeit money, deepfakes, etc.

Scientists are attempting to address fraudulent research in other ways, but peer review is not the best means for it and it should not be thought of in that way.

> Something can appear to be of very high quality and yet still be completely fraudulent.

So in other words, peer review does not judge actual quality. It only judges apparent quality. Which is not what anyone actually wants to know.

Not having data is quite a significant gap.

BTW: I am not criticizing the actual reviewer of the paper. Data availability should be a policy of the journal and required for publication. How can you improve quality without access to the data? In this instance requiring data would have likely prevented the paper from even reaching peer review and wasting a busy individuals time.

> Not having data is quite a significant gap.

Requiring (raw) data is relatively new, and is only in a few fields. For most of the history of peer review data was not required. In fact, none of the PIs I worked in during grad school would ever agree to releasing the raw data. I'm pretty sure they're still not in most of physics.

IMO, it's reasonable to require it, but understand that this is a relatively new requirement and not part of peer review culture.

> Requiring (raw) data is relatively new, and is only in a few fields.

That on its own is scandalous

I was reading of the "reproducibility crisis", in computing, in the nineties

Science still wants to be the warm friendly club, whilst playing competitively for high personal stakes.

Cannot be both. Fraud, deliberately cynical fraud and hopeful naive fraud, has been rampant for decades

The journals benefit directly and seem to be utterly complicit. The system is dreadfully broken

Nobody should wonder why people do not trust science

Science is a method.

Science isn't a religion, nor is it a paper, thesis, or journal. It's a method for understanding the world around us through rigorous application of testing and retesting.

Science can be trusted. Measurements and the testing and retesting can be trusted. It's people that can't be trusted.

We call people in the field of Science, Scientists. Perhaps we should stop labeling fallible and untrustworthy humans as such. Science is trustworthy. Humans are not.

> Science can be trusted. Measurements and the testing and retesting can be trusted. It's people that can't be trusted.

This is an important point and I think people should make it more often.

A human scientist who is committing fraud is misrepresenting facts about the world. And the way you would accurately describe those facts is with science. It's not that science is in the wrong here, it's that the human is lying about what the science says.

You want to be able to say "objectively, this guy lied about his research." But if you want to say that, then you need to be able to accurately describe both the world and the research so you can compute the diff between them. So if you throw out the science with the human scientist, then you've thrown out any objective means by which you were going to say the human scientist did anything wrong in the first place.

I strongly agree that there is a difference between science and "science".

>Science can be trusted. Measurements and the testing and retesting can be trusted.

I still wouldn't go so far. Science doesn't provide a pure understanding of the world it produces at best a model based on observations and axioms. These both can be wrong without politics,money, or personal bias. Our senses are fallible and sometimes you measured the wrong thing with the wrong tool. You shouldn't trust science, you scrutinize it and use what you find works until it doesn't (and then publish that).

> Nobody should wonder why people do not trust science

Taking an iterative approach where you make a prediction, test the prediction, and adjust course in response to the test (AKA science) is literally the only possible thing that can work to discover true things about the world. So if someone distrusts science they're basically giving up on a belief in objective reality.

But I agree it's not surprising that people don't trust science, any more than it's surprising that people believe in astrology, lizard people, or ESP. Human minds don't have a special science sensor that lights up when they read true things. It's pretty much garbage in garbage out.

Don't miss the story here, which is that the study failed to replicate and the fraud was detected. In many human endeavors there are frauds that have persisted for centuries.

> That on its own is scandalous

Yeah it is, I agree. Providing data should be mandatory to the reviewers. It should IMO also be provided publicly if feasible (at least upon request if it's financially burdensome to host) if the work is publicly funded.

That was one particular dataset being requested. There are near infinite ways to characterize a material, and even the best lab is only going to have the time, resources, and expertise to collect and present some of them.

I am not in the field, so I cannot comment on the specific relevance of the request, but in my area, there are absolutely some mandatory data inclusions which should block a paper from being published. Presumably this was not on that list.

There are numerous ways to embargo data and make it available to reviewers before publication and to the public after publication. [1] This isn't a new idea. They simply did not do it.

Nature is a "premier journal" known for publishing cutting edge research. Any data - in this case a bunch of numbers - used to support the conclusion should be made available. I am sure there are disciplines where the evidence would be difficult to provide directly - fossils, physical items etc. Maybe this PI did the experiment himself - thought he won a Noble - only to find he could not in fact reproduce the results after publication - oops. IMO Nature did not do its job. Just pointing that out.

[1] https://plos.org/resource/how-to-store-and-manage-your-data/

I would like it noted that I had Jorge Hirsch as a professor many years ago, and his grading was extremely harsh - you needed to be flawless at showing your work, not just getting the right answer. I remember getting a 14% on one of his tests, the worst score I ever got on any test in my life. It turned out to be a B+ after the curve was applied.

Hence, it doesn't surprise me at all to see that he was the one to call out Dias. Some things never change!

It happens in all fields, at all levels. You cannot trust any papers without reproducing them. The intentional fraud is rampant. There is too much reward for publishing junk as quickly as possible and too little punishment for misbehavior.