Document Type

Paper- Restricted to Campus Access

Publication Date

4-23-2025

Faculty Mentor

Stephen C. Kolwicz Jr.

Abstract

Data preprocessing is a crucial step in behavioral research, ensuring accuracy and efficiency in analysis. This project optimizes the data cleaning and manipulation for a dataset tracking wheel-running activity in multiple cages of mice over a 24-hour period for a total of 10 weeks (60 days) and includes a total of 8,640 data points. Initially processed in Excel, the dataset was transitioned to R to streamline and automate the workflow. Data processing was significantly accelerated by developing a reproducible R-based pipeline, reducing manual errors and improving consistency. The overarching aim of this research is to investigate the effects of Prozac on mice activity levels, with wheel revolutions serving as a key behavioral metric. This study demonstrates the advantages of computational approaches in data preprocessing, allowing for more efficient and scalable analysis in behavioral pharmacology research.

Comments

Presented as part of the Ursinus College Celebration of Student Achievement (CoSA) held April 23, 2025.

The downloadable file is a poster.

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