Materials discovery and development necessarily begins with the preparation and identification of product phase(s). Crystalline compounds can be identified by their characteristic diffraction patterns using X-rays, neutrons, and or electrons. An estimated 20,000 X-ray diffractometers and a comparable number of electron microscopes are used daily in materials research and development laboratories for this purpose.

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 tool calculates metrics for investment analysis documented in NIST Advanced manufacturing Series 200-5. It calculates net present value, internal rate of return, and payback period along with executing sensitivity analysis using Monte Carlo techniques. The tool helps to identify the most economical projects/investments. For instance, it could be used to identify the most economical heating and cooling system or it might be used to rank a set of potential investments.

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.

SRM 635a Portland Cement (Blended with Slag) - This Standard Reference Material (SRM) is intended primarily for use in validation of chemical and instrumental methods of analysis of cements and materials of similar matrix for elemental contents. It can be used to validate value assignment of in-house reference materials. This data is public in the Certificate of Analysis for this material.

SRM 690 Iron Ore (Canada) is for use in checking chemical methods of analysis and in calibration with instrumental methods of analysis. This data is public in the Certificate of Analysis for this material.

SRM 691 Iron Oxide, Reduced is intended primarily for use in evaluating chemical methods and in calibrations associated with optical emission and x-ray spectrometric methods of analysis. This data is public in the Certificate of Analysis for this material.

SRM 692 iron Ore (Labrador) is intended primarily for use in checking chemical methods of analysis and in calibration with instrumental methods of analysis. This data is public in the Certificate of Analysis for this material.