Summary
Artificial intelligence (AI) is becoming essential to scientific discovery in areas, such as biomedical research, environmental modeling, and genomics. However, the reliability of AI systems depends on the quality and integrity of the data used to train them. Scientific datasets are often collected from multiple sources, including laboratory instruments, simulations, and collaborative institutions. This variability makes it difficult to verify how data were generated, processed, or applied. This project supports the NSF's mission to advance trustworthy computing by developing an infrastructure
