Highlights from the community white paper “Enhancing US fusion science with data-centric technologies”

The continued growth of data size and complexity from fusion experiments brings new opportunities and challenges for fusion science. Modern analysis activities like machine learning, automated analysis, and real time control can transform large, complex datasets into new scientific insight. At the same time, data-intensive applications depend on a foundation of enabling technologies like workflow managers, metadata tools, and data frameworks that promote access, collaboration, comprehension, and productivity. Analysis techniques that leverage large data volumes and enabling technologies that facilitate scientific productivity are complementary aspects of modern data science. Here, we present highlights from the community white paper “Enhancing US fusion science with data-centric technologies” which assesses the challenges and opportunities for data science in experimental fusion research. Investments in data science efforts can boost the scientific productivity of experimental data, and transforming data into actionable scientific insight in a timely and economical manner is a priority for all stakeholders.