I think when people talk about "replicate" they mean something more than that.

The dataset could contain coding errors, and the analysis could contain incorrect formulas and bad modeling. Reproducing a bad analysis, successfully, provide no corrective feedback.

I know for one paper I could replicate the paper's results using the paper's own analysis, but I couldn't replicate the paper's results using my analysis.

> Would this require labs to give up whatever used to be secret sauce? That's. The. Point.

That seems to be a very different Point.

Newton famously published results made from using his secret sauce - calculus - by recasting them using more traditional methods.

In the extreme cas, I could publish the factors for RSA-1024 without publishing my factorization method. "I prayed to God for the answer and He gave them to me." You can verify that result without the secret sauce.

I mean, people use all sorts of methods to predict a protein structure, including manual tweaking guided by intuition and insight gained during a reverie or day-dream (à la Kekulé) which is clearly not reproducible. Yet that final model may be publishable, because it may provide new insight and testable predictions.