Developed MRI quality control tooling and visualization features for the NA-MIC/ProjectWeek repository, focusing on enhancing data quality monitoring in medical imaging workflows. The work involved documenting quality control protocols for both clinical and MRI data, exploring anomaly detection concepts, and building a prototype web-based MRI visualization interface. Using Python and web technologies, the developer established a technical foundation for future machine learning model training aimed at robust MRI quality assessment. Updates to the README included a visual representation of the QC process, supporting clearer communication and faster QA workflows. The month’s efforts emphasized rigorous documentation and practical data science applications.