For many historical reasons, much biodiversity data is cataloged and aggregated by type: occurrence, gene sequence, literature, etc. While this has advantages, interesting and new scientific questions require information from more than one source but the varied structure and axes of existing datasets don't make it easy to simply join them up.

In this symposium we will have presentations from researchers on techniques used to join, extract, and infer information across traditional occurrence, taxonomy, trait, phenology, genetic, environment, literature, image, climate, and other boundaries. We also will have limited presentations from data providers on the work they are doing and challenges they face in building and exposing linkages among their datasets. Our goal is to provide a forum to expose working techniques for addressing the disjoint nature of our current data ecosystem.