The complexity of modern systems has made the use of simulations paramount, in order to test different scenarios in an affordable, ethical, and risk-free way. As such, simulations need to be validated, ensuring that the obtained results are meaningful. But validation apart from the computational difficulties, bears several other problems. The constant need for validation due to updates on the simulation software, the dependence on the validation experts to be always available for the new iterations and for presenting any new insights are just some of these problems. This paper proposes a framework, and applies it to two case studies, which is based on Web 3.0 technologies and the R statistical language as a mean to mitigate such problems.