One of the biggest challenges in many fields of studies, such as COVID-19, is to analyze a complex mix of experimental and simulation data, which relies primarily on the intuition of trained experts. Many advanced analysis techniques are often difficult to integrate, leading to a confusing patchwork of analysis snippets too cumbersome for data exploration. To simplify data analysis, LLNL scientists developed a web-based software system that consists a combination of techniques from statistics, machine learning, topology, and visualization. The NDDAV (N-dimensional data analysis and visualization) framework integrates traditional analysis approaches with state-of-the art techniques and custom capabilities which are linked into an interactive environment that enables an intuitive exploration of a wide variety of hypotheses while relating the results to concepts familiar to the users. NDDAV’s modular design provides easy extensibility and customization for different applications, like COVID-19. The NDDAV framework allows for integration of trained expert intuition while analyzing complex mixtures of experimental and simulation data leading to faster, more easily understandable data analysis and visualization.
NDDAV, Open-sourced software licensed under the BSD license (LLNL internal case # CP02056)