I think the original blog post [0] or Andrew Gelman's discussion of it [1] are both better sources for technical details and some historical context.
In particular, this is not the first such issue for Dan Ariely, as Gelman points out, he has a history of sketchy scientific ethics like doing media tours for studies that he knows failed to replicate.
The datacolada post makes the very reasonable request that all data should be released, and scientists should make that a standard thing to do by doing it themselves and requesting others do it.
It feels like this could be applied retroactively too.
In this case the 2012 authors still had the data that they released in 2020 which is how the analysis got done that showed evidence of fraud. Might be worth just asking a whole bunch of people to release data they previously hadn't and collectively putting some time and effort into that.
It’s not possible to release data in all circumstances. If you work with health data (I have worked with birth certificates, EMRs, inpatient discharge abstracts, drug prescription histories and other data) you can’t post it publicly. You have to promise not to include a table in the paper with a cell size of fewer than ten individuals!
For what it’s worth, the Trump administration attempted to make issuing new health and environmental regs harder by requiring public data disclosure. They did this entirely because they knew that much of the data could not be disclosed. So if you were studying, eg, the effects of some pollutant on a health outcome using private data, you wouldn’t be able to rely on that study in a regulatory context bc the data could not be published.
It’s a worthy idea, but there are exceptions for good reasons.
One amusing point is that much of Gelman's post has an error itself (that someone pointed out in the comments a couple weeks ago): the NPR interview was in 2017, so "Ariely, as a coauthor of this article, had to have known for at least half a year before the NPR story that this finding didn’t replicate." is incorrect. Maybe Gelman should retract that part :).
Annoyingly, the NPR transcript at [1] only has a small note "(SOUNDBITE OF ARCHIVED NPR BROADCAST)" at the top with no indication of when (the audio doesn't seem to have a date either). The podcast show notes are apparently the only recordation of the date. [2]
It feels like this could be applied retroactively too.
In this case the 2012 authors still had the data that they released in 2020 which is how the analysis got done that showed evidence of fraud. Might be worth just asking a whole bunch of people to release data they previously hadn't and collectively putting some time and effort into that.