We describe our proposed demonstration of GeoDeepDive, a system that helps geoscientists discover information and knowledge buried in the text, tables, and figures of geology journal articles. This requires solving a host of classical data management challenges including data acquisition (e.g., from scanned documents), data extraction, and data integration. SIGMOD attendees will see demonstrations of three aspects of our system: (1) an end-to-end system that is of a high enough quality to perform novel geological science, but is written by a small enough team so that each aspect can be manageably explained; (2) a simple feature engineering system that allows a user to write in familiar SQL or Python; and (3) the effect of different sources of feedback on result quality including expert labeling, distant supervision, traditional rules, and crowd-sourced data.