Python Toolbelt for Data Stewards in JupyterLab

Annotation: The overall aim is to provide a practical, Python-based toolkit for data stewards to turn incoming project data into a validated, well-documented, and handover- or deposit-ready package, using JupyterLab as the execution and reporting environment.

The session will cover:

• Data validation: defining and adjusting schemas, and producing actionable validation reports.
• Metadata readiness: creating and validating a minimum metadata record, and aligning outputs with common repository expectations.
• Packaging for handover/deposit: bundling data, documentation, and validation evidence.

Time permitting, we may also provide a brief overview of repository automation, including a short conceptual demo of where automation begins (e.g., Zenodo API for deposit workflows or GitHub).