I don't think prosecution is the right tool but if we were going down that road material misrepresentations only would fit with anti-fraud standard for companies. Just drawing dumb, unpopular, or 'biased' conclusions shouldn't be a crime but data tampering would fall into the scope. Not a great idea as it would add a chilling effect, lawyer-friction and expenses and still be hard to enforce for little direct gain.
I personally favor requirements which call for bundling raw datasets with the "papers". The data storage and transmission is very cheap now so there isn't a need to restrict ourselves to just texts. We should still be able to check all of the thrown out "outliers" from the datasets. An aim should be to make the tricks for massaging data nonviable. Even if you found your first data set was full of embarassing screw ups due to doing it hungover and mixing up step order it could be helpful to get a collection of "known errors" to analyze. Optimistically it could also uncover phenomenon scientests thought was them screwing up like say cosmic background radiation being taken as just noise and not really there.
Paper reviewing is already a problem but adding some transparency should help.
Leveraging the prestigious papers to win grant proposals is where they need to get them. Citations aren't what gets you a job or tenure at a R1 research school, it's the grants that the high-impact papers help you win.
You don't have to convict people for full-on fraud. If you are caught using an obvious mistake in your favor or using a weak statistical approach, the punishment can be you are not allowed to apply for grants with a supervisor/co-PI/etc who's role is to prevent you from following that "dumb" process in the future.
I personally favor requirements which call for bundling raw datasets with the "papers". The data storage and transmission is very cheap now so there isn't a need to restrict ourselves to just texts. We should still be able to check all of the thrown out "outliers" from the datasets. An aim should be to make the tricks for massaging data nonviable. Even if you found your first data set was full of embarassing screw ups due to doing it hungover and mixing up step order it could be helpful to get a collection of "known errors" to analyze. Optimistically it could also uncover phenomenon scientests thought was them screwing up like say cosmic background radiation being taken as just noise and not really there.
Paper reviewing is already a problem but adding some transparency should help.