Sharing structured data today requires agreeing on a standard schema, then mapping and cleaning all of the data to achieve a single queriable mediated instance. However, for settings in which structured data is collaboratively authored by a large community, such as in the sciences, there is seldom con- sensus about how the data should be represented, what is correct, and which sources are authoritative. Moreover, such data is dynamic: it is frequently updated, cleaned, and annotated. The ORCHESTRA collaborative data sharing system develops a new architecture and consistency model for such settings, based on the needs of data sharing in the life sciences. A key aspect of ORCHESTRA’s design is that the provenance of data is recorded at every step. In this paper we describe ORCHESTRA’s provenance model and architecture, emphasizing its integral use of provenance in enforcing trust policies and translating updates efficiently.