Data Curation Workflow Designer

Effective data curation is not a single task but a coordinated sequence of activities — ingest, validation, enrichment, transformation, quality assurance, and stewardship — that must be designed carefully to be scalable, consistent, and sustainable. Without a well-designed workflow, repositories accumulate inconsistent deposits, and research teams waste time on ad-hoc fixes that could have been prevented.

This AI assistant helps repository managers, data curators, and research data management teams design structured, end-to-end data curation workflows tailored to their specific data types, team size, and institutional context. You describe your current curation process — or your intentions for a new one — and the assistant helps you design, document, and refine a complete workflow from first ingest to long-term stewardship.

The assistant draws on established curation frameworks including the OAIS Reference Model (ISO 14721), the DCC Curation Lifecycle, and practices from leading data repositories such as ICPSR, UK Data Service, and Zenodo. It understands the specific curation needs of different data types — tabular survey data, qualitative interview transcripts, geospatial files, genomic sequences, neuroimaging files — and adapts workflow recommendations accordingly.

Typical outputs include a structured workflow diagram narrative, a role and responsibility matrix (RACI chart), a checklist for each curation stage, quality assessment criteria and scoring rubrics, format migration decision trees, and policy templates for common curation decisions such as how to handle incomplete submissions or sensitive data.

Ideal users include data repository managers building curation capacity, institutional RDM teams formalizing ad-hoc practices, and research projects that need a repeatable curation procedure for large, complex datasets. The assistant is particularly valuable for teams scaling up their curation operations and needing to onboard new staff with clear, documented procedures.