JupyterLab + AI assistant as data steward workbench
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16 June 2026
1:30 PM – 5:00 PM
This course is part of the Summer School for Data Stewards 2026.
Annotation: The overall aim is to introduce data stewards to JupyterLab as a potential workbench for practical and reproducible workflows supporting a range of tasks (e.g., data intake and exploration, documentation, and DMP drafting), enhanced by an integrated and supportive AI assistant.
The session will cover:
- JupyterLab essentials for stewardship work: workspace organisation, Jupyter notebooks versus files, kernels, and exporting/shareable outputs.
- AI assistant within JupyterLab: how to use the AI assistant for drafting and structuring stewardship artefacts, as well as for code generation and explanation.
- Hands-on example workflow: based on the actual needs of participants, this may include dataset intake and exploration, visualisation, documentation, and creating “starter text” for a Data Management Plan.
Time permitting, we may also provide a brief overview of Python and R side by side, demonstrating the same workflows in two notebooks (Python and R) within JupyterLab, with R support provided via IRkernel. Please note: AI assistant capabilities in JupyterLab are not fully supported when using R.
Language: English
Prerequisites: An e-INFRA account and a laptop prepared according to the lecturer’s instructions.
Tutor: Matthias Täschner
Matthias Täschner
Matthias Täschner is a Research Associate at the Data Science Center ScaDS.AI at the University of Leipzig, working in the area of Service & Transfer. His research focuses on topics related to data analysis and integration, artificial intelligence, and visualization. He manages projects in application development and research in collaboration with partners from industry, academia, and public administration. He is also responsible for planning and conducting training sessions on data science and artificial intelligence, as well as coordinating the IT infrastructure at ScaDS.AI.
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