Olympus provides 23 additional datasets adapted from the chemistry and material science literature. Datasets (referred to by the Olympus kind keyword) and specifications thereof are listed in Table . We provide datasets from a diverse range of scientific disciplines, including chemical reaction optimization, materials manufacturing, organic photovoltaics, and energy storage. New datasets consist of between 3-10 parameters and 1-3 objectives. Several datasets feature exclusively categorical parameters (denoted by "cat" in the param types column). For these datasets, Olympus returns by default noisy measurements whose mean values are the discrete dataset values themselves. Individual measurements are sampled from Gaussian distributions around these values parameterized by standard deviations derived from Bayesian neural networks trained on these datasets. This approach constitutes a heteroscedastic noise model which intends to replicate the aleatoric uncertainty (inherent data noise) in the original measurements. More information on the noise model for fully-categorical datasets is given in SI Sec. S.4. In the fully-categorical cases, the Dataset and Emualtor objects are interchangeable, and can both be queried for measurements using their respective run() methods. For example, consider the fullycategorical dataset perovskites, which reports DFT computed bandgaps for hybrid organic-inorganic perovskite materials. Several datasets, including perovskites, ship with descriptors for categorical variables. Descriptors can be used to introduce ordering between categorical variable options. While descriptors have been shown to increase the optimization rate of experiment planners, 67 not all experiment planning algorithms in Olympus are compatible with descriptor-based representations. Several of the new datasets contain continuous-valued parameters, and therefore require an emulator to return virtual measurements at all possible parameter settings. Among these datasets, suzuki a through suzuki d also comprise multiple objectives. We extended the Emulator module in Olympus to be compatible with multi-output regression such that one neural network model provides virtual measurements for all objectives. More information on the implementation and performance of new emulators is provided in SI Sec. S.3.