Session

Description The integration of artificial intelligence (AI), machine learning (ML), and automated instrumentation is transforming research through Cloud Labs and Self-Driving Labs (SDLs). Positioned strategically, these labs enhance accessibility and integrate computing and data analysis, enabling researchers to leverage cutting-edge technology globally. A national network of Cloud Labs and SDLs is envisioned to promote synergy in AI, ML, computing, and data analysis. The minisymposium prioritizes efficiency, reproducibility, inclusivity, collaboration, and cost-effectiveness, fostering a robust ecosystem. Addressing challenges, it targets reproducibility in Cloud Labs and SDLs, emphasizing parallels between computing and experimental workflows. Pursuing replicable results in virtual environments and automated SDL procedures builds trust in scientific validity. Standardized practices, rigorous documentation, and transparent methodologies, whether in digital Cloud Labs or physical SDLs, are crucial for advancing scientific inquiry and bolstering trust in research outcomes. The minisymposium aims to unite experts, researchers, and industry professionals to showcase innovations, discuss challenges, explore funding opportunities, and share success stories in integrating computing and data analysis within research paradigms.