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Getting Started with JupyterHub at Würzburg University

JupyterHub provides a collaborative and user-friendly environment for data analysis, computational workflows, and notebook-based research, specifically tailored for the Bioinformatics team at Würzburg University. It runs directly on the department’s HPC environment and can also serve as a gateway to other high-performance resources.

1. Prerequisite: Connect to the VPN

Before accessing JupyterHub, you must first connect to the University of Würzburg VPN.

How to Connect:

  1. Visit the VPN Guide – Würzburg University
  2. Follow instructions for your system:
    • Windows / macOS / Linux / iOS / Android
  3. Log in using your university credentials.

2. Request Access to JupyterHub and HPC

JupyterHub runs on the Bioinformatics HPC system. Before using it or accessing the University’s central HPC (“Julia 2”), please contact:

For using the university-wide HPC “Julia 2”, visit:
🔗 HPC at Würzburg University

3. Log In to JupyterHub

Once VPN is connected and access is granted:

  1. Open your browser and go to:
    🔗 https://jupyterhub.uni-wuerzburg.de/
  2. Alternatively, access via Nextcloud:
    • Look for the JupyterHub icon listed as an external service.
  3. Or ask the Bioinformatics administrators for the internal IP if needed.
  4. Log in using your University of Würzburg credentials.

4. Tools, Notebooks, and Resources

A. Widgets & Helper Scripts

Helpful scripts for:

  • Mounting cloud data
  • Extracting metadata
  • Visualizing datasets

🔗 GitHub – CoreUnitRDM Tools

B. Pre-built Notebooks

Get started quickly with:

  • Data exploration workflows
  • Statistical analysis pipelines
  • Visualization templates

🗂️ Ask an administrator for the current shared notebook collection.

5. Best Practices for JupyterHub Users

  • Organize Your Workspace
    Use folders to keep each project clean and separated.

  • Document Code and Results
    Add Markdown cells and comments throughout your notebook.

  • Backup Frequently
    Download notebooks or sync to your Nextcloud folder.

  • Use Resources Considerately
    Avoid blocking shared memory or long-running processes unnecessarily.

6. Learn More: Official JupyterHub Resources

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