With advances in scientific workflow automation and high-performance computing, it has become increasingly facile to generate large datasets of molecules, 1 materials, 2 and reactions, as well as their computed and predicted properties. Such datasets, which commonly rely on atomistic simulations using some combination of classical or ab initio molecular dynamics, semi-empirical quantum chemistry, density functional theory (DFT), and wavefunction methods, have ushered in a new paradigm of data-driven chemical research, enabling indepth understanding of complex chemical domains and the discovery and design of new materials and molecules with desirable properties in various applications. Well-designed, systematic, and diverse datasets are also essential for virtually all machine learning (ML) tasks in chemistry and materials science. While the wealth of data available to researchers is a boon, not all data is created equal.