Live, virtual, constructive, and gaming (LVC-G) integrated training environments bring challenges to data providers with increased target formats for geospatial environment data and visual models. New sharing points along the processing pipeline support consumer expectations of both managed correlation for interoperability and fair fight and optimized runtime content. Sharing points now include cleaned source, intensified source, confederate differentiated source, and runtime formats providing benefit of increased interoperability and fair fight through managed, well-defined levels of correlation. While datasets available from a single provider have increased, so has the number of providers bringing differing standards and conventions. With this increased sharing comes the complexity of managing the number and variety of datasets and providing efficient search and retrieval by data consumers. Oftentimes consumers ask for data from one sharing point without fully realizing the intended use for that share, resulting in poor reuse performance and consumer frustration. Maximum reuse requires incorporating externally developed, value-added data submitted with a variety of formats, data models, dictionaries, fidelity, and specialization levels. Provider reuse policies must balance between accepting un-validated data, risking contaminating their repository and full data validation which may be as costly as using raw source data. Effective data sharing across this vast set of available data possesses potential for improved approaches to managing the acquisition of geospatial environment data for the M&S community. Multiple initiatives have been established or proposed to address the standardization of metadata, exchange protocols, and data product formats toward improved interoperability both between sharing sites and with consumers. This paper describes how some of those efforts are converging to support improved human and machine discovery and selection and interoperability between providers. We describe real world experiences solving these problems from the perspective of a large data provider and propose future direction for effective data sharing.