Bayesian Model Parameters for Rat Gait Errors in a Parkinson's Disease Study

A figshare dataset by Diego Lievano Parra, last updated in May 2026. It contains Bayesian model parameters for footfall errors and velocity in Wistar rats with partial dopaminergic lesions, assessed weekly over six weeks. The data supports research into early motor symptoms of Parkinson's disease.

Use Cases

• Fitting Bayesian models to rodent stepping accuracy data based on weekly error counts.
• Analyzing time-dependent locomotor skill alterations based on velocity and footfall error trends over six weeks.
• Investigating the relationship between tyrosine hydroxylase asymmetry and persistent motor impairments mentioned in the study.
• Comparing heterogeneous velocity changes between control and lesioned animal groups described in the findings.

Strengths

• Data is derived from a controlled longitudinal study with animals assessed weekly for six weeks.
• Parameters are based on markerless pose estimation with DeepLabCut, a modern tracking method.
• Dataset is openly shared under a CC-BY-4.0 license, facilitating reuse.

Limitations

• The dataset is very small at 9.5 KB, indicating limited scope.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment for certain analyses.

Provenance

Source

Diego Lievano Parra via figshare.

Collection Method

Data likely generated from statistical modeling of behavioral data collected from Wistar rats with unilateral 6-OHDA lesions.

Time Range

Study period covered six weeks of weekly assessments.

Freshness

Last updated 2026-05-21 17:31:54; freshness should be verified.