This Standard Reference Material (SRM) is intended for use as a primary calibration standard for the quantitative determination of silver. A unit of SRM 3151 consists of five 10 mL sealed borosilicate glass ampoules of an acidified aqueous solution prepared gravimetrically to contain a known mass fraction of silver. The solution contains nitric acid at a mass fraction of approximately 14 %. This data is public in the Certificate of Analysis for this material.

This Standard Reference Material (SRM) is intended for use as a primary calibration standard for the quantitative determination of thallium. A unit of SRM 3158 consists of five 10 mL sealed borosilicate glass ampoules of an acidified aqueous solution prepared gravimetrically to contain a known mass fraction of thallium. The solution contains nitric acid at a volume fraction of approximately 10 %. This data is public in the Certificate of Analysis for this material.

This Standard Reference Material (SRM) is intended for use as a primary calibration standard for the quantitative determination of vanadium. A unit of SRM 3165 consists of five 10 mL sealed borosilicate glass ampoules of an acidified aqueous solution prepared gravimetrically to contain a known mass fraction of vanadium. The solution contains nitric acid at an amount of substance concentration (molarity) of approximately 1.6 mol/L. This data is public in the Certificate of Analysis for this material.

This Standard Reference Material (SRM) is intended for use as a primary calibration standard for the quantitative determination of antimony. A unit of SRM 3102a consists of 50 mL of an acidified aqueous solution prepared gravimetrically to contain a known mass fraction of antimony in a high-density polyethylene bottle sealed in an aluminized bag.

This Standard Reference Material (SRM) is intended for use as a primary calibration standard for the quantitative determination of tellurium. A unit of SRM 3156 consists of five 10 mL sealed borosilicate glass ampoules of an acidified aqueous solution prepared gravimetrically to contain a known mass fraction of tellurium. The solution contains hydrochloric acid at a volume fraction of approximately 10 %, which is equivalent to an amount of substance concentration (molarity) of approximately 1.2 mol/L. This data is public in the Certificate of Analysis for this material.

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.

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 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.