Often things are found or worked out during the process, which can change week to week or month to month, iteratively. Experiments don't go to plan, data is cooked and can only be used in reduced ways etc. Researchers are meant to share their raw data anyway which should prevent this sort of stuff. Basic statistical analysis on datasets usually reveals tampering.

The issue is the insane academic standards and funding bodies (public grant $) which reward high volume and high 'impact' work. These incentives need re-evaluation and people should not be punished for years of low activity. Sometimes science and discovery just doesn't work the way you think it will, and that's okay. We need a system which acknowledges that which everyone in science knows.

[–] ebits21@lemmy.ca 2 points 9 months ago* (last edited 9 months ago)

All it would do is create an audit trail of your data to keep scientists honest. You can still iterate and change course but now you’re responsible for the record (if you look at the data at some point the data at that point could be recorded as is and a log keeps track when you check the data). Why did you change course and when? Was that appropriate? The data is verified when and if you decide to review it.

How science is done has a problem, just suggesting a solution. I know that’s not how it’s done.

All the data is a matter of record. It makes sure the raw data is ACTUALLY the raw data without bias. It makes sure you’re not ignoring negative results (a huge issue). Statistical detection of cheating will never be as good as reviewing the raw data and changes over time.

As for scooping data, it’s a matter of the record now. There’s data available showing that they scooped you. Currently there’s nothing. The data doesn’t have to be public until the study is published.

I think the main barrier would be scientists and the incentives inherent in the system (career, money, prestige) that creates the cheating in the first place.