Rendering biological data with napari

Welcome to the online handouts for napari Workshop Datasets: Deciphering the Living Cell through Microscopy and Computation, November 16–17, 2026.

This three-hour workshop is written for biologists with little or no programming experience. Start with your operating system setup page before class. During class, the main notebook takes you from an image array to a deliberately styled view with supplied detections and tracks, then to a saved PNG or short movie.

Download the course files · Download offline HTML handouts

JupyterLab opens in a browser and runs the explanatory notebook. Napari opens in a separate desktop window on the same computer. Opening this website alone cannot launch that local Qt viewer.

The reference above is a real render of the prepared course data at frame index 10. A matching 2D projection supports the fallback route. These images were produced during Linux validation and do not claim a successful Windows or macOS platform test.

Download the reference time-series MP4 (30 acquisition frames, displayed at 10 playback frames per second; source data CC BY 4.0).

Scientific boundary

The points and tracks are precomputed. The workshop displays and interprets them; it does not run new detection or tracking. Coordinates remain source voxels and time remains a frame index because physical calibration and acquisition timing are not yet confirmed.

Begin

• Rendering biological data with napari
  • Start here
  • Materials
  • Reference view
  • What the data represent
  • Requirements and status
• Windows setup
  • 1. Open PowerShell
  • 2. Install uv
  • 3. Download and extract the workshop folder
  • 4. Move PowerShell into the folder
  • 5. Create the workshop environment
  • 6. Download the teaching data
  • 7. Check the setup
  • 8. Start JupyterLab
• macOS setup
  • 1. Identify your Mac and open Terminal
  • 2. Install uv
  • 3. Download and extract the workshop folder
  • 4. Move Terminal into the folder
  • 5. Create the workshop environment
  • 6. Download and check the data
  • 7. Start JupyterLab
• Download the data
  • Recommended: verified command-line download
  • Browser download
  • Five-minute classroom limit
  • Optional source data
  • What is already computed
• Troubleshooting
  • uv is not found
  • pyproject.toml or uv.lock is missing
  • The lock file is out of date
  • The kernel is absent or wrong
  • The workshop folder was moved or renamed
  • A notebook cell is busy or cells ran out of order
  • Napari does not appear
  • Qt, OpenGL, graphics, or a blank 3D canvas fails
  • The data folder is missing or nested twice
  • A download is slow or interrupted
  • A point or track does not align with the image
  • Saving is not exporting
  • Movie export fails
  • Memory use is high
  • An output folder already exists

Workshop notebooks

• Before the workshop: check your setup
  • Check graphics with a tiny practice object
  • Check movie encoding in this kernel
• Rendering biological data with napari
  • 1. Run your first cells
  • 2. Open a single image
  • 3. Choose a useful display
  • 4. Explore depth and time
  • 5. Compare a projection with a volume
  • 6. Add existing particle detections
  • 7. Add existing tracks
  • 8. Return to 3D with the same coordinates
  • 9. Save a screenshot
  • 10. Make a short movie
  • 11. Your independent challenge
• Optional: camera movement and acquired time
• Optional: downsampling and loading data only when needed
  • Inspect what downsampling changes

Notebook source files:

• Download the setup notebook

• Download the main workshop notebook

• Download the optional animation notebook

• Download the optional large-data notebook

Student handouts

• Workshop quick reference
  • Start and stop
  • Where things are
  • Notebook controls
  • Napari controls
  • Coordinates and meaning
  • Recovery
• Glossary
• Exercises
  • 1. Find the axes
  • 2. Display choices are interpretations
  • 3. Slice, projection, and 3D view
  • 4. Match a table row to a point
  • 5. Follow one supplied track
  • 6. Independent mini-challenge
  • Optional: prepared versus source resolution
  • Take-home reflection
• Use your own data
  • 1. Check whether your data fit the lesson
  • 2. Record units before plotting
  • 3. Prepare tables explicitly
  • 4. Use relative paths
  • 5. Estimate image memory
  • 6. Choose a loading route
  • 7. Keep images and overlays aligned
  • 8. Adapt a copy of the notebook
  • 2D fallback

Instructor materials

• Instructor teaching guide
  • Course promise and boundary
  • Before learners arrive
  • Room setup and roles
  • Exact 180-minute schedule
  • Demonstration notes and answers
  • Mini-challenge rubric
  • Common misconceptions to address aloud
  • Recovery routes
  • Exit ticket prompts and expected elements
  • After class
• Slide outline
  • Opening and orientation (0–20 minutes)
  • Images and display (20–90 minutes)
  • Supplied measurements (100–140 minutes)
  • Export and communication (140–165 minutes)
  • Challenge and close (165–180 minutes)
• Instructor solutions and discussion prompts
  • Suggested answers
• Validation record
  • Source and teaching data
  • Runtime and notebooks
  • Documentation and portability
  • Remaining instructor checks
  • v0.1.1: published teaching DOI

The teaching dataset and original source dataset have separate Zenodo records, both under CC BY 4.0. The main teaching package is a factor-4 spatial derivative that keeps all 30 frames and loses fine detail. Use Download the data for the verified course route.