Datasets:

canns-team
/

data-analysis-datasets

Description: Grid cell spike data with position information
Format: NumPy archive containing spike times, positions
Keys: spike (spike times), t (time), x, y (positions)
Usage: 2D CANN analysis, topological data analysis, circular coordinate decoding
Example: Primary dataset for 2D CANN tutorials

grid_2.npz (4.5 MB)

Description: Second grid cell dataset for comparison studies
Format: NumPy archive with spike and position data
Usage: Comparative analysis, validation studies

Left_Right_data_of(604 MB)

pretty_name: "ASA-format MEC grid-cell dataset (Open Field only)" tags:

neuroscience
grid-cells
MEC
open-field
continuous-attractor-neural-network
topological-data-analysis
ASA-format

Dataset Summary

This dataset contains ASA-format conversions of MEC recordings restricted to Open Field (OF) sessions only.
Each session is provided as a NumPy .npz file (full session + optional module subsets) with a lightweight JSON manifest for indexing.

Original Source

EBRAINS dataset instance: https://search.kg.ebrains.eu/instances/4080b78d-edc5-4ae4-8144-7f6de79930ea

Files Overview

*_ASA_mec_full_cm.npz

Description: ASA-format Open Field (OF) MEC session data (all units included)
Format: NumPy archive (.npz) following the ASA schema
Keys: spike (neural spikes/activity), t (time), x, y (positions), meta (session metadata)
Usage: Full-session analysis with ASA (CANN / TDA / decoding), baseline for module comparisons
Example: Primary input file to run end-to-end OF analysis in the ASA pipeline

*_ASA_mec_gridModuleXX_nN_cm.npz

Description: ASA-format subset files containing grid cells from a specific module (Module XX, n=N cells) for the same OF session
Format: NumPy archive (.npz), same ASA schema as the full file, but restricted to one module’s grid cells
Keys: spike, t, x, y, meta
Usage: Faster experiments, module-wise topology/decoding analysis, comparing manifolds across modules
Example: Run TDA on Module 01 vs Module 02 to compare torus quality and decoding stability

*_ASA_manifest.json

Description: Per-session manifest summarizing the source .mat, generated ASA outputs, unit counts, and module splits
Format: JSON metadata index
Usage: Batch processing, programmatic iteration over sessions, auto-generating documentation without loading large .npz
Example: Parse manifests to list all sessions and locate their corresponding full/module .npz files

Reference

Please refer to the EBRAINS instance above for the original dataset description and citation requirements.

Usage

Install the CANNS package and use the datasets module:

from canns import datasets
from canns.analyzer import data_analysis

# Automatic dataset download and setup
datasets.quick_setup()

# Load specific datasets
roi_data = datasets.load_roi_data()
grid_data = datasets.load_grid_data("grid_1")

# Use with analysis tools
analyzer = data_analysis.CANNDataAnalyzer()
spikes, x, y, t = analyzer.load_spike_data(datasets.get_dataset_path("grid_1"))

Examples

See the CANNS examples for complete tutorials:

data_analysis_demo.py: Command-line demo
cann_data_analysis_tutorial.ipynb: Jupyter notebook tutorial

Citation

These datasets are derived from the CANN-data-analysis repository. Please cite:

@software{cann_data_analysis,
  title = {CANN Data Analysis},
  url = {https://github.com/Airs702/CANN-data-analysis},
  author = {Airs702},
  year = {2024}
}

License

Please refer to the original CANN-data-analysis repository for license information.

Generated automatically for the CANNS package datasets.