Background: The Virtual Data Warehouse (VDW) was created as a mechanism for producing comparable data across sites for purposes of proposing and conducting research. It is “virtual” in the sense that the data remain at the local sites; there is no multi-site physical database at a centralized data coordinating center. At the core of the VDW are a series of standardized file definitions. Content areas and data elements that are commonly required for research studies are identified, and data dictionaries are created for each of the content areas, specifying a common format for each of the elements—variable name, label, description, code values, and value labels. Local site programmers have mapped the data elements from their HMO’s data systems into this standardized set of variable definitions, names, and codes, as well as onto standardized SAS file formats. This common structure of the VDW files enables a SAS analyst at one site to write one program to extract and/or analyze data at all participating sites. Methods: This poster demonstrates the wide range of data sources used at HealthPartners Research Foundation to feed information into our local implementation of the VDW datasets. Results: The HealthPartners Research Foundation local implementation of the VDW contains detailed medical information on HealthPartners members and patients. These files contain details on 69 million pharmacy dispensings (2000-2011), nearly 58 million unique medical encounters (2000-2011), including 14 million diagnoses, and 20 million procedures. We have some 9 million Vital Signs observations, and 26 million lab results. The VDW Enrollment and Demographic files are derived from several historical and current membership/patient files; the VDW Pharmacy and utilization files are derived from internal HealthPartners systems plus claims files; the VDW tumor data is retrieved from our owned Cancer Registry. Conclusions: The VDW at HealthPartners Research Foundation provides an easily employed unified central repository of data from all available source files. This resource enables the sharing of compatible data in multi-site studies, and also improves programming efficiency, accuracy, and completeness for local single site studies by expending resources to link these legacy systems only once.