Mouse Spontaneous Behavior Syllables Across Estrous Cycle and Sex

Behavioral syllable data from female and male mice in open field tests, collected longitudinally across different estrous cycle phases. The dataset was created by Dana Rubi Levy of Harvard University for the 2023 study 'Mouse spontaneous behavior reflects individual variation rather than estrous state'. It includes separate dataframes for female and male subjects, as well as control groups.

Use Cases

• Training models to identify individual mice based on characteristic behavioral patterns mentioned in the description.
• Analyzing the stability of exploratory behavior in female mice versus male mice across sessions.
• Investigating the negligible impact of estrous state on spontaneous behavior using unsupervised machine learning-derived syllables.
• Comparing within-individual and between-individual behavioral variability between sexes.

Strengths

• Data is derived from a longitudinal study design, tracking mice across multiple experimental sessions.
• Dataset includes separate, clearly labeled dataframes for female, male, and control groups.
• Data is associated with a peer-reviewed publication (Levy et al. 2023) and related analysis code is publicly available.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment for specific modeling tasks.
• Last update date is unknown; freshness unverified.

Provenance

Source

Harvard University (Dattalab)

Collection Method

Longitudinal open field behavior recording of mice, analyzed with unsupervised machine learning (MoSeq) to decompose behavior into syllables.