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Intel Edison wireless latency and reliability computing code
Data provided by National Institute of Standards and Technology
This software provides a framework to generate events with both application payload identification and timestamps. Events information is logged at each producer and consumer. The logs can be used to derive latency and reliability metrics for cyber-physical systems experiments in which wireless communication is used for messaging.
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MCMLpar: A parallel version of the MCML code in C++
Data provided by National Institute of Standards and Technology
C++ code for Monte Carlo calculation of optical scattering in multi-layer material. Described in RH Streater, A-MR Lieberson, AL Pintar. and ZH Levine, "A parallel version of MCML and an Inverse Monte Carlo Algorithm to Calculate Optical Scattering Parameters," J. Res. NIST, https://doi.org/10.6028/jresnist.122.038. See also the main article RH Streater, A-MR Lieberson, AL Pintar, CC Cooksey, and P Lemaillet, unpublished.
Modified:
Source: https://github.com/usnistgov/MCMLpar
MCSLinv: An inverse Monte Carlo code to calculate optical scattering parameters in C++
Data provided by National Institute of Standards and Technology
C++ code for inverse solution of Monte Carlo calculation of optical scattering in single-layer material, i.e., determination of optical scattering parameters from the Angle-Resolved Scattering Described in RH Streater, A-MR Lieberson, AL Pintar. and ZH Levine, "A parallel version of MCML and an Inverse Monte Carlo Algorithm to Calculate Optical Scattering Parameters," J. Res. NIST, https://doi.org/10.6028/jresnist.122.038.
Modified:
Source: https://github.com/usnistgov/MCSLinv
mumpcepy: A Python implementation of the Method of Uncertainty Minimization using Polynomial Chaos Expansions
Data provided by National Institute of Standards and Technology
The Method of Uncertainty Minimization using Polynomial Chaos Expansions (MUM-PCE) was developed as a software tool to constrain physical models against experimental measurements. These models contain parameters that cannot be easily determined from first principles and so must be measured, and some which cannot even be easily measured. In such cases, the models are validated and tuned against a set of global experiments which may depend on the underlying physical parameters in a complex way. The measurement uncertainty will affect the uncertainty in the parameter values.
Modified:
Source: https://github.com/usnistgov/mumpce_py
Temperature measurements of experiments to characterize the influence of walls, corners and enclosures on fire plumes.
Data provided by National Institute of Standards and Technology
The data is from two series of compartment fire experiments in which a natural gas burner is positioned in a corner, or against a wall, or inside a steel cabinet, to assess the effects on the plume and compartment temperatures. The measurements consist of one dimensional vertical thermocouple arrays to measure the hot gas layer temperature and height, and a three dimensional thermocouple array to measure the temperature of the fire plume as the burner is moved away from the corner or wall.
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Thermographic measurements of single and multiple scan tracks on nickel alloy 625 substrates with and without a powder layer in a commercial laser powder bed fusion process (an additive manufacturing technology)
Data provided by National Institute of Standards and Technology
This dataset contains thermographic measurements acquired during single and multiple track scans on bare substrates and on single layers of powder. The substrates and powder are nickel alloy 625 and the experiments are performed inside a commercial laser powder bed fusion machine. There are four experiment cases: 1) a single scan track on a bare substrate, 2) a single scan track on a single hand-spread layer of powder, 3) multiple (39) scan tracks covering an area on a bare substrate, and 4) multiple (39) scan tracks solidifying a single hand-spread layer of powder.
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The NIST Extensible Resource Data Model (NERDm): JSON schemas for rich description of data resources
Data provided by National Institute of Standards and Technology
The NIST Extensible Resource Data Model (NERDm) is a set of schemas for encoding in JSON format metadata
that describe digital resources. The variety of digital resources it can describe includes not only
digital data sets and collections, but also software, digital services, web sites and portals, and
digital twins. It was created to serve as the internal metadata format used by the NIST Public Data
Repository and Science Portal to drive rich presentations on the web and to enable discovery; however, it
Modified:
Source: https://github.com/usnistgov/oar-metadata/tree/integration/model
NIST Standard Reference Simulation Website - SRD 173
Data provided by National Institute of Standards and Technology
The Standard Reference Simulation Website is an ongoing project whose aim is to provide well-documented simulation results for a variety of systems and from various simulation techniques. The data include raw canonical potential energy, macrostate probability distributions, metadata explaining the simulation parameters and constraints, and thermophysical properties generated by processing the raw simulation output, including pressure, phase coexistence properties, self-diffusivity, and excess entropy.
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Source: https://www.nist.gov/programs-projects/nist-standard-reference-simulation-website
Federal Spectrum Inventory
Data provided by National Telecommunications and Information Administration
Count of Federal frequency assignments in the 225 to 5000 MHz bands by agency, band, and radio service.
Modified:
Certified values of iodine in CRMs of food matrices in mg/kg units. The uncertainties are expanded uncertainties at approximately 95% confidence.
Data provided by National Institute of Standards and Technology
A comparison of the expanded uncertainty for certified values of iodine in food matrix CRMs. The CRMS were found by searching The European Virtual Institute for Speciation Analysis (EVISA) database using the terms "Iodine" and "Certified" in the Material category. The uncertainties are expanded uncertainties at approximately 95% confidence.
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