Worked on the UniversumX/Universum repository to enhance data visualization and documentation for analytics workflows. Developed UMAP-based dimension reduction visualizations with action-specific filtering, enabling users to isolate and analyze specific actions within EEG datasets. Addressed technical challenges by correcting epoch-to-action mapping and resolving EEG data path issues, which reduced the risk of misinterpretation in action-specific plots. Updated data collection documentation in Markdown to improve data provenance by recording new participant runs. Leveraged Python for data preprocessing and visualization, focusing on reproducibility and configurability. The work improved data governance and provided stakeholders with clearer, more actionable insights from machine learning analyses.