The Freakonomics podcast covered this topic pretty nicely just recently. Would recommend a listen! It’s not just international or low impact journals that are having issues.
I feel like zero trust research could be a thing in the future in some areas.
So for example, the study would be pre registered with expected outcome as is starting to be done more often now. But also the third party has a private encryption key and the experiments data is encrypted somehow during collection with a public encryption key (obviously very much depends on the type of study).
The scientist could not snoop the data even if they wanted. The public key can encrypt data but only the private key can unlock it.
Then once uploaded to the third party they can unlock it with their private key. Then the data is public before any analysis.
This is the best summary I could come up with:
Tens of thousands of bogus research papers are being published in journals in an international scandal that is worsening every year, scientists have warned.
The practice has since spread to India, Iran, Russia, former Soviet Union states and eastern Europe, with paper mills supplying fabricated studies to more and more journals as increasing numbers of young scientists try to boost their careers by claiming false research experience.
The products of paper mills often look like regular articles but are based on templates in which names of genes or diseases are slotted in at random among fictitious tables and figures.
Others are more bizarre and include research unrelated to a journal’s field, making it clear that no peer review has taken place in relation to that article.
The spokesperson added that Wiley had now identified hundreds of fraudsters present in its portfolio of journals, as well as those who had held guest editorial roles.
“We have removed them from our systems and will continue to take a proactive … approach in our efforts to clean up the scholarly record, strengthen our integrity processes and contribute to cross-industry solutions.”
The original article contains 957 words, the summary contains 186 words. Saved 81%. I’m a bot and I’m open source!
Good bot!
https://www.youtube.com/watch?v=qlas3TOi_CQ
a scary science data story48 min long
Their video-description-linked text source: https://laskowskilab.faculty.ucdavis.edu/2020/01/29/retractions/
Knowing that our data were no longer trustworthy was a very difficult decision to reach, but it’s critical that we can stand behind the results of all our papers. I no longer stand behind the results of these three papers.
There has been some questions of why I (and others) didn’t catch these problems in the data sooner. This is a valid question. I teach a stats course (on mixed modeling) and even I harp on my students about how so many problems can be avoided by some decent data exploration. So let me be clear: I did data exploration. I even followed Alain Zuur’s “A protocol for data exploration to avoid common statistical problems“. I looked through the raw data, looking for obvious input errors and missing values. […]
Altogether, I was left with the conclusion that there was good variation in the data, no obvious outliers or weird groupings, and an excess of 600 values which was expected due to the study design. As a scientist, I know that I have a responsibility to ensure the integrity of our papers which is something I take very seriously, leading me to be all the more embarrassed (& furious) that my standard good practices failed to detect the problematic patterns in the data. Multiple folks have since looked at these data and came to the same conclusion that until you know what to look for, the patterns are not obvious.
Thank you for sharing. This is concerning indeed.
The image that some unserious magazines pushed the main part of the fake papers is not sattled by research as I have read long ago. Some scientometrists has checked out and find out that even many of the “small magazines” use regular peer review. Is actuall a small minority of magazines which let them be paid to publish “science spam”.
