This paper explores bias in large-scale visual datasets, specifically in YFCC, CC, and DataComp. Researchers used a novel framework to analyze various transformations that isolate different types of visual attributes, such as semantic, structural, color, and frequency biases. They discovered that semantic bias plays a significant role in distinguishing the datasets, with distinct thematic focuses and object distributions contributing to this bias. Structural bias is also present, with object shapes and spatial configurations being strong indicators of dataset origin. Color bias exists across both high-frequency and low-frequency components, while frequency bias contributes to the visual distinctiveness of the datasets. The findings suggest that despite efforts to improve diversity, large-scale datasets still exhibit significant biases that can affect model generalizability and robustness. By applying transformations and analyzing their outputs, researchers and practitioners can gain insights into their single dataset's visual characteristics and potential biases.