Submission Date

8-27-2026

Document Type

Paper- Restricted to Campus Access

Department

Neuroscience

Faculty Mentor

Leslie New

Comments

Presented during the 28th Annual Summer Fellows Symposium, July 24, 2026 at Ursinus College.

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Project Description

Quantitative analysis of neuromast morphology generates large, complex datasets that present a significant computational challenge for neuroscience research. Each experimental fish contributes multiple sensory organ (neuromast) measurements across pre-synaptic (GFP) and post-synaptic (MAGUK) imaging datasets, in addition to treatment group and recovery-time metadata, resulting in thousands of files requiring organization, validation, and analysis. To address this challenge, a secure, read-only extract–transform–load (ETL) pipeline was developed to automate the identification, retrieval, and integration of experimental data from a structured cloud repository while preserving experimental metadata throughout the workflow. Automated quality-control procedures were incorporated to verify dataset completeness and ensure reproducibility prior to statistical analysis. Mixed-effects modeling and diagnostic testing were implemented to validate statistical assumptions and evaluate treatment and recovery-time effects, with complementary exploration from both classical and Bayesian perspectives. To further reduce manual processing, a machine learning classification model was developed to assist in automated neuromast identification, minimizing repetitive annotation while improving consistency and scalability. Together, these tools establish a reproducible computational framework that streamlines morphometric analysis, reduces opportunities for human error, and enables neuroscience researchers to devote more effort to experimental interpretation and biological discovery.

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