The Dark Matter Data Commons project addresses a pressing need in experimental astrophysics by creating an open, reusable, and accessible data infrastructure to support discovery in dark matter research. There has been an important investment in dark matter detection experiments, but the field still lacks standardized mechanisms for sharing data and analysis workflows. This gap hinders transparency, reproducibility, and innovation in science. By aligning with FAIR principles (i.e., Findable, Accessible, Interoperable, and Reusable), the project builds a data platform that broadens access to high-value experimental datasets, enabling participation of students, early career researchers, and institutions with limited infrastructure. The project advances scientific progress and national research capacity through this data infrastructure while supporting educational development and the research community. It contributes to national interest by accelerating discovery in a federally prioritized scientific area and fostering access to cyberinfrastructure for data-intensive science. The project establishes an end-to-end infrastructure for curating, storing, accessing, and analyzing dark matter experiment data. It delivers a FAIR-compliant repository enriched with standardized metadata, integrated with scalable AI and machine learning (AI/ML) workflows, and accessible through user-friendly command-line and Python interfaces. Built atop NSF-funded resources, the commons begins with