Introducing data stewards to JupyterLab as a potential workbench for practical, reproducible workflows supporting a range of tasks (e.g. data intake and exploration, documentation, and DMP drafting), with an integrated AI assistant.
Starting from the basics, we set up a reproducible environment, learn our way around Jupyter
notebooks, and then meet the bia-bob AI assistant - first for everyday coding help, then
tailored into a data steward assistant that helps explore a real (raw) dataset and draft a
plan, all while we read and verify what it produces.
You will learn to: work confidently in JupyterLab and Jupyter notebooks, understand why
projects use isolated environments (uv / virtual environments), use the bia-bob assistant to
generate and fix code, shape it with a custom system prompt, and apply it to explore data and
draft documentation.
Notebooks
1_notebook-basics- JupyterLab and notebooks: cells, Markdown, running code, common pitfalls2_ai-assistant-basics- thebia-bobassistant: prompting, generating and fixing code3_ai-assistant-advanced- system prompts and a reusable Data Steward assistant4_ds-workflow- a steward workflow: explore a dataset and draft a plan with the assistant
Leads into Session 2: Python Toolbelt for Data Stewards in JupyterLab.