To sum up, we question the consistency of computational material science data with respect to building new machine learning models. Three examples of such data have certain features that are likely to compromise machine learning training: the energy above the convex hull is unsteady, and the electronic bandgap and formation energies values have discrepancies. Cathode voltage datasets that are obtained from density functional theory calculations share the unsteadiness of the energy above the convex hull. We suggest two guidelines for developing machine learning-friendly datasets: data must be fundamentally consistent, and the benchmarking baselines for machine learning models should be set with respect to experimental, rather than theoretical, data.