One could build a dataset out of Github data, by analyzing KiCAD files. They are likely to be "completed" projects, so one would have to rip out all the traces and start from that. And the parts are also placed (in a way which makes routing feasible for a human), which is a large part of routing. So one could have another task setting where the parts also have to be placed, and then routed.
Such a dataset would likely represent simple to medium cases, as open designs are usually on the lower side of the complexity compared to industry. And it would be hard to automatically infer important constraints such as differential pair matching. That would require manual annotation, most likely. But if there indeed are no open datasets, then I think this would be a worthwhile contribution to the field.
If OP wants to approach this using machine learning instead of a deterministic algorithm, wouldn't this be exactly what they need?
Use the completed traces and part locations (complete with human post adjustments and all) as labels and the bare connectivity graph + "constraints" in some form as inputs.
Of course, as with all machine learning projects, the interface is deceptively simple but gives you no information how well the system can work or whether it can work at all...
This kind of dataset is useful also with a non-learned algorithm. Testing and benchmarking on realistic data is the best way to validate any solution to problems that are non-trivial and have a very diverse input space.
