Data curation enables data discovery and retrieval, maintains data quality, adds value, and provides for re-use over time. However the skills and expertise required to curate data (to prepare, arrange, describe, and test data for optimal reuse) cannot be fully automated nor reasonably provided by a few experts siloed at a single institution. Multiple data curation experts are needed to effectively curate the diverse data types a repository typically receives. The Data Curation Network (DCN) will establish a cross-institutional pool of expert data curators that institutional and disciplinary partner institutions can draw from for a wide variety of data types (e.g., GIS, tabular spreadsheets, statistical survey, video and audio, software code, etc.) and discipline-specific data sets (e.g., genomic sequence, chemical spectra, qualitative surveys, etc.). The DCN model provides a solution for partners of all sizes to develop or to supplement local curation expertise with the expertise of a resilient, distributed network that will allow them to supplement at peak times, access specialized capacity when infrequently-curated types arise, and to stabilize service levels to account for local staff transition, such as during turn-over periods. Starting in 2018, the DCN model will be piloted across nine partner data curation services at the University of Minnesota, Cornell University, Dryad Data Repository, Duke University, Johns Hopkins University, Penn State University, the University of Illinois, the University of Michigan, and Washington University in St. Louis. This presentation will explore our approach for this pilot phase, our assessment plan, and our long-term goals to strengthen collaboration between libraries and disciplinary projects and significantly enhance the future reusability of research data.