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 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 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 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 tutorial provides mechanical drawings, electrical schematics, parts lists, STL files for 3D-printed parts, IGS files, and instructions for automated machining necessary for construction of a dual-protease, subzero, liquid chromatography system for hydrogen-deuterium exchange mass spectrometry. Electrical schematics for construction of a multi-zone temperature controller that regulate to ±0.1 oC are also included in this tutorial.

The spreadsheet file reported herein provides centroid data, descriptive of deuterium uptake, for the Fab Fragment of NISTmAb (PDB: 5K8A) reference material, as measured by the bottom-up hydrogen-deuterium exchange mass spectrometry (HDX-MS) method. The protein sample was incubated in deuterium-rich solutions under uniform pH and salt concentrations between 3.6 degrees C and 25.4 degrees C for seven intervals ranging (0 to 14,400) s plus a control sample that simulates a Fab Fragment immersed for infinite time in D2O.

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.

The Micronutrients Measurement Quality Assurance Program (MMQAP) was coordinated by the Chemical Sciences Division and supported measurement technology for selected fat- and water-soluble vitamins and carotenoids in human serum. This program was initiated in 1984 by the National Cancer Institute Division of Cancer Prevention and Control to ensure the long-term reliability of the measurements made while studying the possible cancer chemoprevention roles of these compounds.