An interesting question, not least because it seems skew to NEURON's primary design goal: to serve as a tool for implementing and performing experiments on computational models of biological neurons and networks. Every computational model is only an expression, in computational form, of a conceptual model or hypothesis. "Expression in computational form" means "expression in a way that is equivalent to a set of equations". NEURON's role in computational modeling is to provide a convenient means for creating such expressions, and a computational engine for generating numerical solutions with sufficient speed and accuracy.
Given this background, a proper notion of "validation" of NEURON would mean one of three things:
1. validation of the ease with which users can implement their hypotheses in computational form
2. validation by the modeler that there is a close match between the conceptual model and the expression of that model in computational form
3. validation of the numerical methods that NEURON uses to generate simulations.
We could discuss how each of these can be addressed, but from your question it seems that your actual concern is not about NEURON itself, but about the validity of the models that NEURON users create. That's up to the users themselves, not NEURON or NEURON's developers.
It should also be remembered that the notion of "validation" is easily misapplied, and in some cases may not be valid. Consider this very common scenario in neuroscience research:
A scientist is interested in phenomenon A which has been observed in some biological system (a cell or network of cells). The scientist knows some of the properties P of the biological system, and imagines that a particular subset S might account for phenomenon A. However, the elements in S have their own complexities such that neither unaided intuition nor nonlinear dynamical analysis is sufficient to decide that S can account for A. The scientist uses NEURON (or MATLAB or C or whatever) to build a computational model that describes the behavior and interactions of the elements in S, and uses the model to perform some computational experiments designed to see if A happens. If A does happen, the scientist has a result that is roughly equivalent to an "existence proof." Where in this process is there a role for "validation" in the sense of your use of the term?
I cannot seem to find an overview of all potential validation data. Does such a thing exist?
"Model validation" being a primary responsibiliity of modelers themselves (and only in those cases where one has decided that validation is relevant), the best place to look for evidence of validation is in the work of modelers who have published a series of papers on one or more related models. Or perhaps work done in response to RFAs that explicitly require model validation (maybe some of the SPARC-funded stuff?).