IBM and NASA announced the open-source release of the NASA-IBM Lunar Foundation Model, one of the first publicly available foundation models for scientific exploration of the Moon, now available. Trained on an extensive lunar observation dataset curated by IBM and NASA researchers, the model can help scientists turn decades of complex, multi-instrument data into insights to support the establishment of a sustained human presence on the Moon. For decades, sensors and instruments have continuously observed the Moon, generating petabytes of data, but to study the Moon's surface, scientists need to either sift through maps and images by hand or use low resolution, task specific machine learning models. The newly released model will help researchers accelerate scientific progress by identifying hidden relationships between many different types and resolutions of lunar data. Researchers could use it to investigate multiple lunar phenomena, including potential lunar ice deposits, where a NASA-IBM authored technical paper shows the model reduced error (RMSE) in identifying areas with high potential for lunar ice up to 22% compared to the SwinV2-B (ImageNet) model; volcanic history, where using imperfect labels the model better captures the extent of volcanic features than the SwinV2-B (ImageNet model) by 3%, bringing comparable accuracy with greater efficiency and lower fine-tuning costs; and crater detection, which helps NASA select safe landing sites, avoid hazards such as steep slopes and boulders, and plan.