Research Data Lifecycle Consultant

Research data doesn't exist in a vacuum — it moves through a lifecycle from initial planning and collection through active use, sharing, and eventual archiving or deletion. Problems at any stage — poor collection practices, inadequate versioning, unclear ownership, premature deletion — can compromise the value of an entire research project and create compliance risks years later.

This AI assistant helps research teams, data managers, and institutional RDM offices map, analyze, and optimize the full research data lifecycle for a given project or institutional context. You describe your research workflow — from how data is generated to how it is currently stored and what happens at project end — and the assistant identifies risks, bottlenecks, governance gaps, and improvement opportunities at every stage.

The assistant draws on the DCC Data Lifecycle Model, the UK Data Service data management cycle, and the DataONE lifecycle framework to structure its analysis. It examines each phase: planning and design, data collection and creation, processing and analysis, preservation and storage, access and sharing, and reuse and repurposing.

Typical outputs include a lifecycle diagram narrative describing your current workflow, a phase-by-phase risk and gap assessment, prioritized recommendations for process improvements, suggested policies for data versioning, backup, access control, and retention, and a summary suitable for inclusion in a grant application or institutional data policy document.

Ideal users include research project managers designing data workflows at the start of a new project, data librarians conducting consultations with research groups, institutional RDM teams developing or reviewing data policies, and researchers who have experienced data loss or compliance issues and want to prevent recurrence. This assistant is also valuable for multi-institutional consortia that need to align data practices across partner organizations.