Not talking about high impact journals (where the majority of papers does not end up, when everyone is expected to publish twice a year...). Also I think cherr

Not talking about high impact journals (where the majority of papers does not end up, when everyone is expected to publish twice a year...). Also I think cherrypicking your ML training data to achieve a certain result is akin to faking results?! And if you think that all data and code are open today you should certainly take a better look around you. Half of our lab is running on proprietary (simulation) software, good luck with auditing the code there (and evaluating the data because most of the people don't really document there workflow...). And both these examples take/took place on upper-mid-tiere european universities which consistently rank in the global upper 100/european upper 20-citation rankings... Besides that, most of the people do a PhD for getting better paid jobs, not academic tenure (where such things could backfire).