LTER has a wealth of carbon cycling data from many biomes, and its diversity sometimes hampers effective discovery for synthesis or reuse. The semantic web offers mechanisms to attach rich content to our datasets that includes specific context and extended descriptions which can be used to enhance discovery. However, these mechanisms require a significant effort that is generally beyond the scope and resources of a program the size of LTER. A large data federation project, DataONE, has a component focused on using semantic structures, and is applying those to LTER carbon cycling-related datasets. The benefits of this partnership are 2-fold: with LTER data, DataONE has a complex use case for applying and vetting semantic technologies. LTER gains semantic structures and related software which will enhance a user's ability to understand and reuse its data. In addition to semantic search, DataONE also is implementing a system to enhance reproducibility by storing and indexing provenance trace information. The use case for this provenance work is the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP), a comparison of carbon flux model results and observations, whose overall goal is to provide feedback to the terrestrial biospheric modeling community to improve the diagnosis and attribution of carbon sources and sinks. MsTMIP’s central measurement is Net Ecosystem Exchange (NEE), and MsTMIP has identified contributing measurements which are comparable to many LTER measurements, e.g., NPP.