JupyterLab + AI assistant as Data Steward Workbench
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16 June 2026
12:30 PM – 4:00 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, reproducible workflows supporting a range of tasks (e.g. data intake and exploration, documentation, and DMP drafting), with an integrated AI assistant.
The session will cover JupyterLab essentials for data stewardship work, including workspace organisation, notebooks versus files, kernels, and exporting or sharing outputs. It will also introduce the AI assistant within JupyterLab, focusing on how it can support the drafting and structuring of stewardship artefacts, as well as code generation and explanation. In addition, a hands-on example workflow will be included, based on participants’ needs; this may cover dataset intake and exploration, visualisation, documentation, and creating “starter text” for a data management plan.
Depending on the audience and environment, we may also explore Python and R side by side by running equivalent workflows in separate notebooks within JupyterLab, with R support provided via IRkernel. Note: AI assistant capabilities in JupyterLab are not yet fully supported when using R.
Language: English
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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