The project that produced these data involved the analysis and modeling of atmospheric Asian dust particles and background marine air particles collected in Hawaii, USA at the Mauna Loa Observatory (MLO) of the National Oceanic and Atmospheric Administration. Individual heterogeneous dust particles were analyzed with focused ion-beam scanning electron microscopy and energy-dispersive X-ray spectroscopy.

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 consists of tensile tests performed by directly gripping single UHMMPE fibers. The tests were conducted at 11 different temperatures, and at both quasi-static and dynamic loading rates. The testing procedures are described in NISTIR 8265.

The purpose of the MATS project is to develop a NIST-based multi-spectrum fitting tool for spectroscopic data that allows the flexibility to test and adapt to experimental/data-driven needs. This software allows for the use of several commonly used spectroscopic line profiles (Voigt, Nelkin-Ghatak, speed-dependent Voigt, speed-dependent Nelkin-Ghatak, and Hartmann-Tran) and allows for pressure, temperature, and composition constraints to be imposed on solutions.

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.

Values of second dielectric and refractivity virial coefficients computed, with rigorous accounting for quantum effects, from state-of-the-art pair potentials and interaction polarizabilities. Values given at 1 K intervals (finer intervals below 10 K) for helium (both mass 3 and 4 isotopes), neon (both mass 20 and 22 isotopes), and argon (mass 40 isotope). Upper temperature limit is 2000 K in all cases. Lower temperature limit is 0.5 K for helium, 4 K for neon, 50 K for argon.

This is a searchable database of NIST patents

This data set includes the time series data from 16 electrochemical, optical, temperature and humidity sensors in 60 experiments to characterize the conditions preceding cooktop ignition compared to the conditions of normal cooking. The sensors are placed in the exhaust duct above a mock-up kitchen cooktop. Experiments cover a broad range of conditions, including both unattended cooking and normal cooking scenarios, where 39 experiments led to auto-ignition.

SCATMECH is a library of object-oriented C++ computer codes originally developed for disseminating models for polarized light scattering from surfaces and aerosols and for diffraction from gratings. The pySCATMECH package has been developed as an interface to the SCATMECH library, simplifying use of the codes and allowing for more rapid development of software for these applications.

ABDNavigator is software written for scanning probe (e.g. scanning tunneling microscope) control and sample navigation of atom-based devices. Here atom-based devices refer to devices whose components span from the micron scale range down to sub nanometer and are probed, and typically fabricated by scanning tunneling microscope.