The absolute laser absorption was measured simultaneously with X-ray imaging during laser melting of Ti-6Al-4V solid metal. The data included here are the time-resolved absolute absorbed power and the Xray images acquired at the same time, along with timing data for synchronization. Also included is information about the experimental configuration including applied laser power, laser beam spatial profile, and the experimental setup. A text document is included that describes all files.

This software is a Python module for estimating uncertainty in predictions of machine learning models. It is a Python package that calculates uncertainties in machine learning models using bootstrapping and residual bootstrapping. It is intended to interface with scikit-learn but any Python package that uses a similar interface should work.

This dataset was an initial test harness infrastructure test for the TrojAI program. It should not be used for research. Please use the more refined datasets generated for the other rounds. The data being generated and disseminated is training, validation, and test data used to construct trojan detection software solutions. This data, generated at NIST, consists of human level AIs trained to perform a variety of tasks (image classification, natural language processing, etc.).

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.

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.