Title: Learning Dictionaries over Datasets through Wasserstein Barycenters. Abstract: Dictionary learning consists of trying to represent objects in terms of basic elements (atoms) weighted by an importance factor (representation). Non-linear dictionary learning using optimal transport as a metric has been previously studied for normalized non-negative data on a fixed grid. We propose a new framework by using Wasserstein Dictionary Learning on datasets understood as empirical distributions. We leverage Wasserstein barycenters for learning a dictionary of virtual datasets and embeddings in a simplex. We apply our method for unsupervised domain adaptation, improving the state-of-the-art over 1.96% and 2.70%, respectively, and manifold learning of Gaussian distributions and color histograms.