napari Workshop Teaching Dataset: Spatially Downsampled Images, Detections, and Tracks.
Year: 2026
Type: dataset
Abstract: This record provides a compact teaching derivative of napari Workshop Datasets: Deciphering the Living Cell through Microscopy and Computation (2026), contributed by Reto Fiolka and Kevin Dean (source DOI: https://doi.org/10.5281/zenodo.22694247). The dataset supports practical instruction in visualizing microscopy images, particle detections, and tracks using napari and Jupyter. It contains two image channels, labeled DNA and Mitochondria in the source workflow, and preserves all 30 acquisition frames. Each spatial dimension was downsampled by a factor of four using Gaussian filtering followed by subsampling, reducing individual volumes from 118 × 664 × 860 to 30 × 166 × 215 voxels in z,y,x order. The approximately 42.8 MB archive includes 60 TIFF volumes, metadata and checksums, 28,105 precomputed detections, and 25,076 track observations representing 2,058 track IDs. Detection and track tables retain their original rows and source-voxel coordinates. No new detection or tracking was performed. This derivative reduces download and memory requirements for teaching 2D and 3D navigation, visualization settings, overlays, and movie export. Downsampling removes fine detail and changes voxel intensities; the images are intended for visualization instruction rather than quantitatively interchangeable use with the originals. Physical calibration and acquisition timing remain unconfirmed, so the accompanying lessons use source voxels and frame indices. Workshop instructions, notebooks, and preparation code: https://thedeanlab.github.io/napari-workshop/ Distributed under Creative Commons Attribution 4.0 International (CC BY 4.0).
Authors: Fiolka, Reto, Dean, Kevin
Institution: The University of Texas Southwestern Medical Center
Subfield: Biophysics
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7212130219
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