The Operation Blended Warrior (OBW) integration demonstration at the Interservice/Industry Training, Simulation and Education Conference 2017 (I/ITSEC'17) provided the U.S. Army an opportunity to evaluate the shareability of the SE Core Master Database by providing OBW participants with source data and correlated terrain datasets. SE Core and OBW'17 leadership selected a 200x300km area of the Pacific Northwest (PacNW) that met the land, sea and air requirements. SE Core prepared the PacNW dataset to meet the “DISTRIBUTION Câ€� classification requested. During the first OBW planning meeting, the ground scenario group expressed the desire for a small inset representing a Dense Urban Terrain (DUT) area with unlimited distribution. A small 10x10 kilometer area of Seattle Washington, labeled the Emerald City Inset (ECI), was selected, and SE Core prepared this dataset to meet the â€�DISTRIBUTION Aâ€� request of the OBW leadership. After providing the initial production of a source dataset to all interested OBW participates, SE Core produced several run-time terrain formats to enable more participates to become part of the OBW ground scenario. SE Core provided both the PacNW and ECI source data to eighteen Government, Industry, and Academic organizations, while simultaneously generating multiple other formats including OpenFlight®1, OneSAF, Virtual Battlespace 3 (VBS®23), and Virtual Reality Scene Generator (VRSGâ„¢3) used during the OBW'17 demonstration events. Once the availability of an open-distribution SE Core DUT proliferated, many other Modeling and Simulation industry members outside of OBW'17 requested a copy for experimentation and demonstration at the I/ITSEC'17 conference. This paper describes the production processes and license coordination required to deliver the Distribution-C PacNW and Distribution-A ECI source dataset, along with the associated runtime databases. Additionally, this paper provides important lessons learned from the coordination across the various OBW'17 and general industry consumers and provides insightful feedback on what would have made the SE Core data more easily consumed by their respective products and processes.