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Creators/Authors contains: "Panta, Aashish"

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  1. ; ; ; ; (, IEEE Computer Society)
    The growing resolution and volume of climate data from remote sensing and simulations pose significant storage, processing, and computational challenges. Traditional compression or subsampling methods often compromise data fidelity, limiting scientific insights. We introduce a scalable ecosystem that integrates hierarchical multiresolution data management, intelligent transmission, and ML-assisted reconstruction to balance accuracy and efficiency. Our approach reduces storage and computational costs by 99%, lowering expenses from $100,000 to $24 while maintaining a Root Mean Square (RMS) error of 1.46 degrees Celsius. Our experimental results confirm that even with significant data reduction, essential features required for accurate climate analysis are preserved. Validated on petascale NASA climate datasets, this solution enables cost-effective, high-fidelity climate analysis for research and decision-making 
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    Free, publicly-accessible full text available May 22, 2026
  2. ; ; ; ; ; ; ; (, IEEE)
    Free, publicly-accessible full text available November 17, 2025
  3. ; ; ; ; (, ESS Open Archive)
    Free, publicly-accessible full text available November 23, 2025
  4. ; ; ; ; ; ; ; ; ; (, IEEE)
    Full Text Available 
  5. ; ; ; ; ; ; ; ; ; ; et al (, Computing in Science & Engineering)
    This perspective article presents the vision of combining findable, accessible, interoperable, and reusable (FAIR) Digital Objects with the National Science Data Fabric (NSDF) to enhance data accessibility, scientific discovery, and education. Integrating FAIR Digital Objects into the NSDF overcomes data access barriers and facilitates the extraction of machine-actionable metadata in alignment with FAIR principles. The article discusses examples of climate simulations and materials science workflows and establishes the groundwork for a dataflow design that prioritizes inclusivity, web-centricity, and a network-first approach to democratize data access and create opportunities for research and collaboration in the scientific community. 
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    Full Text Available