This chapter delves into the role of dimensions in cultural data visualization and analysis, with a focus on employing advanced techniques such as t-SNE for dimensionality reduction and plotting. By exploring numerical features from metadata or extracting image-based attributes, this chapter demonstrates how to visualize complex datasets in lower-dimensional spaces. Practical examples include using gganimate to track changes in photography dimensions over time and applying t-SNE to cluster objects based on shared characteristics.