Dataset for: Moreno-Gomez, M., et al. (2026). Off-target effects of DREADD ligands revealed by an anaesthesia emergence paradigm in mice.

This repository contains the behavioral tracking dataset associated with the study of off-target effects of DREADD (Designer Receptors Exclusively Improved by Designer Drugs) ligands. The data was collected using an anaesthesia emergence paradigm in a mouse model, specifically designed to evaluate the physiological and behavioral impact of common actuators (e.g., CNO, C21) independently of DREADD expression.

Experimental Design

Mice were subjected to a standardized anaesthesia protocol followed by a recovery period in an open-field arena. Behavioral monitoring was performed to record the latency and quality of emergence.

Subjects: Adult mice (see manuscript for strain and sex details).

Conditions: Multiple sessions across different days including baseline and ligand administration.

Apparatus: Open-field arena with overhead/lateral video recording.

Data Content & Format

The dataset consists of pose estimation results generated via DeepLabCut.

Format: .h5 (Hierarchical Data Format 5).

Data Structure: Each file contains the x, y coordinates and confidence likelihood for multiple body parts tracked at high temporal resolution.

Organization:

The data is organized into folders by experimental day: DayX.X_YYYY.MM.DD.

Each folder contains the specific .h5 tracking files corresponding to the subjects and trials of that session.

Data Provenance (Software Output)

The files provided here are the direct output of automated pose estimation software.

Input: Raw video recordings of mice during emergence from anaesthesia.

Processing: Videos were analyzed using DeepLabCut (ResNet-50).

Output: The resulting .h5 files contained in this dataset store the predicted trajectories, body part coordinates, and confidence scores ($p\text{-likelihood}$) for each frame.

Note: By providing the .h5 files, we offer the processed kinematic data, allowing researchers to skip the computationally expensive video-processing step and proceed directly to behavioral analysis and quantification.

Usage Notes

These .h5 files can be loaded directly into Python (using pandas or h5py) or MATLAB for further kinematic analysis, heatmaps, or behavioral segmentation.

To analyze these files, we provide a dedicated suite of MATLAB scripts. This software allows for the extraction of kinematic parameters, latency to emergence, and behavioral segmentation directly from the .h5 files.

Analysis Software: Available on GitHub at AnesthesiaDataAnalysis_MorenoGomez2026.

Workflow: Users can clone the repository and use the provided functions to process the dataset contained in this Zenodo record.