Developing pore-scale transport predictive models in subsurface research is relevant to, among others, contaminant and colloidal transport, non-aqueous phase liquid dissolution, and deep sequestration of CO2. Currently, several independent research groups are developing numerical simulations of multiphase flow and reactive transport at the pore scale without experimental data sets to test, verify, and validate the models. Despite the numerous numerical pore-scale studies evaluating solute mixing available in the literature, there has only been limited testing against experimental data, primarily due to measurement limitations. The numerical models use a variety of computational approaches, and each has strengths in areas such as accuracy, computational speed, or scalability. Because of different numerical approaches, there is a strong benefit in benchmarking these models against common experimental data sets. Such a benchmark challenge would highlight relative strengths of each approach and serve to accelerate progress in the field. At the Pore-scale Modeling Challenge/Workshop held at the EMSL on August 9 and 10, 2011, attendants indicated that a benchmarking effort should include definition of learning sets of mutually beneficial pore-scale experimental studies to calibrate numerical models and challenge sets designed to numerically predict results without knowing the experimental results. Based on the recommendations of the workshop participants, a total of five micromodel experimental sets have been designed to develop the necessary data to test and verify simulators in their ability to model pore-scale transverse mixing. This proposal describes these five data sets.