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The project focuses on developing innovative tools to extract and analyze the available observational and modeling data to enable new physics-based and machine-learning approaches for understanding and predicting solar activity and its influence on the geospace and Earth systems. Numerous space and ground-based observatories produce several terabytes of multi-wavelength data, from radio waves to gamma rays, every day. Finding and processing the relevant information for specific space weather applications is currently a difficult task. The Team has developed and implemented interactive databases of solar flares and coronal holes that provide a synergy of ground-based and space observations, taking advantage of big datasets from a wide range of instruments. The databases and analysis tools allow the larger research community to significantly speed up investigations of flare events, perform a broad range of new statistical and case studies, and test and validate theoretical and computational models. The databases store, integrate, and present physical descriptors of solar flares and provide automatic real-time machine-learning identification and characterization of solar coronal holes, which are sources of open magnetic flux and fast solar wind.