This doesn't seem very feasible given the current level of privacy afforded to companies.
I don't think there's a way to distinguish between studies that have lost funding because there are genuinely less funds to go around, and foo experiment just happened to be one of the ones that didn't make the cut, and studies that have had the funding pulled for harmful reasons (Like negative results).
Mainly because it's devilishly easy to mask; you'd just reduce funding to research and redistribute it into something plausible like marketing. Of course this isn't feasible for lots of studies producing negative results.
Of course, one way to counter that is to insist that the data is published regardless, but I'm sure that the data could be hidden with clever use of NDAs and court arguments along the lines of "We need to prohibit the distribution of company assets".
> Of course, one way to counter that is to insist that the data is published regardless, but I'm sure that the data could be hidden with clever use of NDAs and court arguments along the lines of "We need to prohibit the distribution of company assets".
I find myself more and more treating complexity as a proxy for dishonesty. Companies that are trustworthy seem to have a relatively simple business model that they don't try to hide. The more convoluted it is, the more likely it is someone is scamming you. Wonder if that could be used as an effective metric - the more complex the reason someone weasels away from publishing the data, the more their "reputability" score goes down?
What about starting by implementing these measures in public universities? That seems very feasible, and could have a snowball effect on how studies are broadly analyzed and perceived.