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 report describes the test equipment, process, analysis, and data file formats for the Numisheet 2020 tensile and tension/compression testing of the four materials associated with the Numisheet 2020 conference benchmarks. The four materials include two steel alloys, DP980 for Benchmark 1 and DP1180 for Benchmark 2, and two 6000 series aluminum alloys, AA6xxx-T4 for Benchmark 1 and AA6xxx-T81 for Benchmark 2, that will be referred to here as BM1-DP980, BM2-DP1180, BM1-6xxx-T4, and BM2-6xxx-T81, respectively.

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 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 Standard Reference Material (SRM) is in the form of chips sized between 0.50 and 1.18 mm sieve openings (35 and 16 mesh). It is intended for use primarily in chemical methods of analysis. Similar material for use in spectrometric methods of analysis is available as SRM 1297. This data is public in the Certificate of Analysis for this material.

Between 1922 and 1940, the National Bureau of Standards (NBS) conducted a long-term investigation into the corrosion of bare steel and iron, pipes buried underground at 47 sites representing different soil types in the United States. Following the passage of the 2004 Pipeline Safety Improvement Act, the Department of Transportation's Office of Pipeline Safety requested that NIST review and reanalyze the data from the NBS study using modern statistical analysis tools. For this analysis, NIST compiled an updated database from the data in the publications by K. H. Logan (i.e.

The National Institute of Standards and Technology, as part of its report on the collapse of the World Trade Center, characterized many important steels recovered from the buildings to provide stress-strain models to analyze the impact, fires, and resulting collapse. Those tests represent a large additional data set that can be used for modeling the response of steel structures to fire.

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.

There is often a large amount of maintenance data already available for use in Smart Manufacturing systems, but in a currently-unusable form: service tickets and maintenance work orders (MWOs). Nestor is a toolkit for using Natural Language Processing (NLP) with efficient user-interaction to perform structured data extraction with minimal annotation time-cost.