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research seeks to address these challenges through an integrated approach combining high-speed X-ray diffraction (XRD) and other synchrotron-based scattering experiments and advanced data analysis. Central
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shocks and stressors). This research effort relies extensively on modeling and optimization, with consideration for field data collection, and statistical and geospatial data analysis. Informed by
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algorithms to improve methods for peptide identification from raw mass spectral data. The use of orthogonal information such as multi-enzyme digestions, to verify the presence of a peptide using different
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the rigorous data quality metrics for accurate diagnostics, prognostics, medical biomarkers, and for untangling the mechanisms of disease. The development and delivery of solution-enabling metrology tools
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property data are available for only a few dozen fluids. A better understanding of fundamental fluid behavior would allow the accurate prediction of properties for those fluids lacking good data and thus
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a premier tool for probing atomic dynamics, yet extracting physical insights from experimental data remains a significant computational challenge. Traditional methods—Empirical Force Fields and
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and novel data-processing tools need to be developed to embrace these new techniques and further elevate their capabilities. Instrumentation available for this research includes ion trap, Orbitrap, and
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, and complete products of combustion) will be performed. Comprehensive data sets of this type have not been previously reported for full-scale enclosure fires. The chemical data will be augmented by
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. Sriram sriram@nist.gov 301.975.3507 Description Systems biology involves gathering comprehensive sets of data that define and quantify the elements of a particular biological system and computationally
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metabolomics. Our studies focus on developing new mass spectral data analysis algorithms (e.g., clustering) to better solve the common key persistent problems arising from factors such as mass shift and peak