This software repository contains a python package Aegis (Active Evaluator Germane Interactive Selector) package that allows us to evaluate machine learning systems's performance (according to a metric such as accuracy) by adaptively sampling trials to label from an unlabeled test set to minimize the number of labels needed. This includes sample (public) data as well as a simulation script that tests different label-selecting strategies on already labelled test sets. This software is configured so that users can add their own data and system outputs to test evaluation.

This software tool generates simulated radar signals and creates RF datasets. The datasets can be used to develop and test detection algorithms by utilizing machine learning/deep learning techniques for the 3.5 GHz Citizens Broadband Radio Service (CBRS) or similar bands. In these bands, the primary users of the band are federal incumbent radar systems. The software tool generates radar waveforms and randomizes the radar waveform parameters.

This dataset includes a Microsoft Excel spreadsheet (*.xlsx file) provided as supplemental material for the NIST Technical Note (TN-2099) publication titled "Quasi-Static Position Calibration of the Galvanometer Scanner on the Additive Manufacturing Metrology Testbed". The file contains two tabs, titled "Pre-Compensation" and "Post-Compensation", which provides example measurement data and calculations pertaining to the calibration procedures described in the publication.

Supplementary Data for the paper "Three-Dimensional Particle Shape Analysis Using X-ray Computed Tomography: Experimental Procedure and Analysis Algorithms for Metal Powders" by E.J. Garboczi and N. Hrabe. Contains all the data and programs described in this paper to generate the results discussed in the paper. Also contains all the results that are generated by the data and programs, with which the user can compare to check their results.

This data set consists of both measured and simulated optical intensities scattered off periodic line arrays, with simulations based upon an average geometric model for these lines. These data were generated in order to determine the average feature sizes based on optical scattering, which is an inverse problem for which solutions to the forward problem are calculated using electromagnetic simulations after a parameterization of the feature geometry.

Standard Reference Material-1721: Southern Oceanic Air. Ambient Nominal Amount of Substance Fraction for Carbon Dioxide, Methane & Nitrous Oxide. This Standard Reference Material (SRM) is a primary gas mixture for which the amount-of-substance fraction, expressed as concentration, may be related to secondary working standards. This SRM is intended for the calibration of instruments used for ambient carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) determinations and for other applications. This data is public in the Certificate of Analysis for this material.

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.

The Additive Manufacturing Benchmark Test Series (AM-Bench) is developing a continuing series of controlled benchmark tests, in conjunction with a conference series, with two initial goals: 1) to allow modelers to test their simulations against rigorous, highly controlled additive manufacturing benchmark test data, and 2) to encourage additive manufacturing practitioners to develop novel mitigation strategies for challenging build scenarios.

Using a modified Thomas-Fermi approximation, we model a reference semiconductor system comprising a quasi-1D nanowire with a series of five depletion gates whose voltages determine the number of quantum dots (QDs), the charges on each of the QDs, as well as the conductance through the wire. The original dataset, QFlow lite, consists of 1 001 idealized simulated measurements with gate configurations sampling over different realizations of the same type of device.

Hestia Project quantifies, simulates and visualizes greenhouse gases such as carbon dioxide emitted in urban regions. Los Angeles basin activity is provided at the 1 km grid spatial resolution, and at temporal resolutions of hourly or annually. Hestia-LA urban CO2 emissions inventory builds upon work conducted at the national scale (Vulcan Project) that includes various sectorial attributions. Hestia's high spatial and temporal resolution datasets are currently available for 2010 to 2015, in UTC or local time.