Published November 9, 2025 | Version 1.0

A device-restricted C. elegans whole-brain recording dataset for ASCENT: annotation-free neuron tracking benchmark

  • 1. ROR icon Georgia Institute of Technology

Description

This dataset accompanies the manuscript “Annotation-free whole-brain neuron tracking via transformer-based self-supervised learning” by Han et al. The manuscript introduces ASCENT, an Annotation-free Self-supervised Contrastive Embeddings for 3D Neuron Tracking method.

It provides the in-house whole-brain (head ganglion) recording of C. elegans used for benchmarking and analysis in the ASCENT study, along with the corresponding ground-truth and predicted neuron detections and tracks.

The recording captures neuronal activity across the head ganglion of a C. elegans (strain ZM9624, expressing pan-neuronal GCaMP6 and mNeptune) confined within a microfluidic device that permits controlled chemical stimulation. Worms were subjected to repeated cycles of 60s buffer followed by 30s E. coli OP50 supernatant stimulus. Imaging was performed on a Bruker Opterra II swept-field confocal microscope using a 40× 0.75 NA air objective and a Photometrics Evolve 512 Delta EM-CCD camera. The dataset consists of 1,100 volumetric frames (2 × 9 × 512 × 512; channels × z × y × x) acquired at 3.3 Hz with a spatial resolution of 1.5 μm (z) and 0.243 μm (xy).

Ground-truth neuron tracks were generated by manually proofreading and correcting an initial set of tracks generated by ZephIR (https://github.com/venkatachalamlab/ZephIR). These ground-truth and ASCENT-predicted detections and tracks are provided in CSV format compatible with Napari and standard cell-tracking frameworks.

 

Files included

  1. InHouse_Opterra_CH_RG_Day1_OP50Stim_1100frames.h5
    • Raw volumetric imaging data
    • Structure:
      root/
        ├── t0/c0  (Z × Y × X)
        ├── t1/c0
        ...
    • c0: mNeptune, c1: GCaMP6
  2. InHouse_Opterra_coords_GT.csv
    • Ground-truth neuron detections.
    • Columns: object_id,t,z,y,x
  3. InHouse_Opterra_coords_PRED.csv
    • ASCENT-predicted neuron detections.
    • Columns: object_id,t,z,y,x
  4. InHouse_Opterra_tracks_GT.csv
    • Ground-truth neuron tracks (Napari-compatible).
    • Columns: TrackID,ObjectID,t,z,y,x
  5. InHouse_Opterra_tracks_PRED.csv
    • ASCENT-predicted neuron tracks (Napari-compatible).
    • Columns: TrackID,ObjectID,t,z,y,x

 

Usage notes

  • The dataset can be opened using standard HDF5 libraries (e.g., h5py in Python) or visualized in Napari with the napari-ndtiffs or napari-hdf5 plugins.
  • Detection and tracking CSVs can be loaded directly as points and tracks layers in Napari.
  • The dataset follows the ASCENT data I/O conventions described in the associated manuscript and GitHub repository: https://github.com/lu-lab/ascent.
  • Coordinates are in voxel units consistent with the recorded voxel size.

Files

InHouse_Opterra_coords_GT.csv

Files (6.3 GB)

Name Size
md5:9581892c969bb0e7f3d84eba84988133
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md5:37bd1234033d360dfd888b46daa369b1
2.1 MB Preview Download
md5:2e90f28f008b7b4c5ff556e9cb065ce0
9.0 MB Preview Download
md5:5a7c414b95365bb13cc2674df03d5277
2.5 MB Preview Download
md5:149de39424a2cc1f48507b5e5813c279
3.1 MB Preview Download

Additional details

Dates

Submitted
2025-11-09