(Preprint) Data Curation Network: A Cross-Institutional Staffing Model for Curating Research Data

Abstract

The Data Curation Network (DCN) addresses the challenge of scaling domain-specific data curation services collaboratively across a network of multiple institutions and digital repositories in order to provide expert data curation services in disciplines and domains beyond what any single institution might offer alone. The DCN will serve as the “human layer” in a local data repository stack that provides expert services, incentives for collaboration, normalized curation practices, and professional development training for an emerging data curator community. Data curation enables data discovery and retrieval, maintains data quality, adds value, and provides for re-use over time through activities including authentication, archiving, management, preservation, and representation. Data curation requires a specialized skill set that spans a wide variety of data types (e.g., spatial/GIS, tabular, database, etc.) and discipline-specific data formats (e.g., chemical spectra, 3D images, genomic sequence, etc.). Our model networks these skills so that data curation services might be provided at scale for institutions of all sizes. The report includes a summary of the research done by the planning phase team to develop the model, which included focus groups with faculty researchers, controlled data curation pilots, and a survey of the library curator community to understand existing support and plans for future services in these areas. Our DCN model included detailed staffing roles, institutional participation levels, ingest workflows and curation procedures, and an implementation and sustainability plan that is grounded in one year’s assessment of the measurable metrics and observed demand for data curation services across our six planning phase institutions.