Here we question the datasets that are routinely used to assess the accuracy of ML models, particularly focussing on three popular data targets: energy above the convex hull, E h , the electronic bandgap E g , and the formation energy E f . E h is one of the key properties that machine learning models are trained to predict for crystalline materials. E h has a special status: it is the ultimate indicator for the thermodynamic stability of new hypothetical structures. That is, it is an essential guide for validating theoretical material discovery efforts, as well as motivating the synthesis of yet-unknown materials. For a given crystal structure, E h is computed by performing first principles computation of the total energy of the structure, most commonly using DFT, as well as the total energies of the known stable materials that lie along the convex hull of the chemical space to which the structure belongs. There is an increasing number of reports on using E h as a target for machine learning training, and it is one of the training targets in the JARVIS leaderboard. The predicted E h was used in several works to establish the thermodynamic stability of unknown materials. However, as will be explained below, E h is an unsteady quantity; it is not dependent on the input structure alone, but also dependent on the accumulation of material discovery efforts. Cathode voltage data is infected with the same unsteadiness of E h . E g and E f data do not suffer from unsteadiness, but they have different problems. We show the inconsistency between the values of E g in materials datasets that are computed using the density of states (DOS), and the values computed using the band structure. Such inconsistencies compromise the accuracy of ML regression training exercise, as well as classification of materials into metals and non-metals. E f , which is commonly reported as the formation energy per atom within the structure's unit cell, is the most immediate DFT quantity that is computed for a structure with optimised electronic structure. In MP, the reported E f is decided after a series of structure optimisations for a given structure with various combination of parameters, particularly various k-point meshes. We show that some structures in MP might miss k-point meshes that would yield lower E f than that deposited in the database. Additionally, we point out the problem with using GGA data for layered materials.