This repository consolidates resources on data-centric AI, an approach that prioritizes improving data quality over model architecture. It collects papers, blog posts, and research progress in techniques such as data labeling, curation, and validation that help ML practitioners achieve better real-world results. The project originated from Stanford’s HazyResearch group and includes contributions from the broader data-centric AI community.