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> It seems to be that, in a collaboration between three graduate students, some background data which was foundational to the interpretation of other findings may have been fabricated.

This is a gross misinterpretation. The authors were forthcoming that they used a model of the background conditions instead of direct measurements, as is standard practice for this type of experiment, and they stand by their results.

They were not at all forthcoming. In the original paper they stated: " The background signal, determined from a non-superconducting C–S–H sample at 108 GPa, has been subtracted from the data." Then after questions were raised, they published https://arxiv.org/abs/2201.11883v1, where they say: "We note here that we did not use the measured voltage values of 108 GPa as the background." If the second statement is true then, at best, the first statement is highly misleading. Furthermore, they have not been at all forthcoming about how exactly that background subtraction was performed - despite repeated requests for clarification. I challenge anyone to understand the background subtraction methodology that they describe in the second paragraph of page 2 in the article linked above.

In my mind, what is at question here is not only the validity of the background subtraction, but the validity of the raw data itself. If the raw data is valid, then why are they unable to show how to go from raw data to published data?

Their explanation of the background subtraction methodology seems pretty clear to me, and I don't see how their statement in the first paper could be construed as misleading nonetheless deceitful.

They are able to show how they went from the raw data to the published. Unless you are claiming that the raw data itself is completely made up (in which case why not just make up data that gives the result they want with a different background subtraction method?) then I don't see how the validity of the raw data is in question.

Here is the text in question:

"We selected the background after carefully investigating the temperature dependence of the non-superconducting CSH sample at 108 GPa, the closest pressure prior to the superconducting transition. We note here that we did not use the measured voltage values of 108 GPa as the background. We use the temperature dependence of the measured voltage above and below the Tc of each pressure measurement and scale to determine a user defined background (Fig. 2a). The scaling is such that one achieves an approximately zero signal above the transition temperature; the subtracted background isolates the signal due to the sample."

I challenge you to actually repeat what they did using that description. It is not a complete description.

And no, they did not "show how they went from the raw data to the published". Just because they said they did, doesn't mean they did.

The raw data is in question because it's impossible to understand how subtracting two noisy data sets would produce data that is a combination of a spline and digitized data.

Disappointed but not surprised to see graduate students thrown under the bus like that. They're an easy target for blame: powerless and also by the time the retraction rolls around, not there anymore. Very easy to blame someone who can't hit back.

Other than more carefully noticing, “hmm this data looks like a polynomial curve, maybe it’s fabricated?” I’m not sure what you propose the professor do instead.

The professor is not going to duplicate all of the experimental measurements done by the students and it seems totally natural to me to trust the student is not committing fraud, until there is evidence otherwise.

Duplication of work prior to publication is standard in many experimental fields, like synthetic chemistry. In some cases the cost of an experiment is high enough to be a problem, but conductivity measurements are not really that exotic.

Duplicating everything is unnecessary. Duplicating the thing which is foundational to a groundbreaking discovery is necessary. Beyond fraud there could be just an honest mistake or some fluke confounding factor.

Maybe there should be a "reproducability risk" metric, calculated with the transitive closure of all published results that reference a paper that has yet to be reproduced. This could help researchers calibrate how much they should trust a result and indicate when a foundational paper really needs to be reproduced.

Taking the professor at the word that it's those graduate students' fault (who very conveniently can't defend themselves and haven't been at the University since 2020) and not the fault of the professor is a bit strange. It might after all be their fault, but it could have easily been some manipulation done or demanded by the PI who wanted a publication.

What I'm trying to say is, I'm not willing to take the professor at their word here when their word is so extremely convenient for them.

Just to clarify, the only person suggesting the graduate students were responsible is the OP. Neither the retraction notice nor the Science news article (https://www.science.org/content/article/something-seriously-...) point to the graduate students.

In fact the Science article points to a more senior member of the team as also being a co-author on a recently retracted article that was retracted for reasons related to the same kind of data.

Moreover, check out the quote from Eremets at the end of the Science article regarding principal investigator Dias' more recent, even more groundbreaking claims: "How is this possible? Everything he touches turns to gold."

To be clear, in either circumstance i'm not throwing the graduate students under the bus. I'm speaking about the broader structural problems which lead to this sort of pressure-induced-error(?). Fabrication in this case can be as far as invented, to applying the wrong background separation, but it fits/looks good, and no more rigor is applied for expedience.

Not unsolvable - just difficult. Systematic problems driven by social incentives are very difficult to solve. But they can be solved. Especially if the institutions in question get behind solving them.

In the case of science, a lot of the bad incentives are created by a combination of the institutions (university administrations and granting agencies) and by the scientists themselves. It'll take both working together to change the incentives and solve the problems. But it can be done.

Fabricating data is fraud. The reputation of the university is harmed. The public is harmed by potentially providing grants for follow up research. Other researchers are harmed by wasting their time. It seems like one potential fix would be to prosecute people who perpetrate such fraud.

Who should you prosecute in this case? The PI? The students?

Prosecuting the PI for fraud might be possible. I'm not able to google for cases where that has been done, but I'll bet it's occurred before. I'm doubtful that the student could be prosecuted.

One basis for a fraud claim would be the statement that accompanies most grant applications, generally of the form, "X is the PI of the proposed work, and has the responsibility to ensure the quality of the investigation and scientific results..." -- and the PI signs their name, and takes the money. (And so does a university official.)

One example: https://research.wustl.edu/about/roles-responsibilities/prin...