Jack Dohrn enhanced data visualization and documentation workflows in the UniversumX/Universum repository, focusing on analytics reproducibility and data governance. He delivered UMAP-based dimension reduction visualizations with action-specific filtering, enabling users to isolate and analyze particular actions within EEG datasets. By correcting epoch-to-action mapping and fixing EEG data path issues, Jack reduced the risk of misinterpretation in action-specific plots. He also updated data collection documentation in Markdown to improve data provenance, adding detailed notes for new participant runs. His work leveraged Python for data preprocessing and visualization, resulting in more configurable, transparent, and reliable analytics for stakeholders and collaborators.