NIST developed a reference material consisting of synthetic fragments of the SARS-CoV-2 virus RNA, which is the target of molecular diagnostic tests. These RNA fragments can assist in the development and validation of RT-qPCR assays for the detection SARS-CoV-2. The RNA fragments are characterized for concentration using digital PCR methods, may be used to assess limits of detection for SARS-CoV-2 assays, and may calibrate other in-house or commercial SARS-CoV-2 controls. This dataset includes data used for RTGM 10169 SARs-Cov-2 Research Grade Test Material validation.

X-ray computed tomography (XCT) datasets with known ground truth pores were developed using realistic XCT simulation. Non-overlapping spherical pores of varying sizes are randomly distributed in a cylindrical part near surfaces and within the core. Ground truth data, ground truth binary data, and reconstructed data for three different signal-to-noise ratios (SNRs) are provided. The data set can be used for evaluation and comparison of image segmentation/detection algorithms.

This dataset includes the code needed to reproduce all the results from "Superancillary equations for PC-SAFT" by Ian Bell (NIST) and Ulrich Deiters (Cologne).It includes C++ code for the development of the formulation, and the scripts and other files needed to build the python package and build all the plots from the publicationSee the README.md file for more detailed information of the contents

Data for "Primary measurement of gaseous elemental mercury concentration with a dynamic range of six decades" article for submission

Data for the figures in the manuscript entitled "Time Programmable Frequency Comb: Generation and Application to Quantum-Limited Dual-Comb Ranging"

"High dynamic range electro-optic dual-comb interrogation of optomechanical sensors" to be published in Optics Letters.

Supplemental material to the article "Expansivity of fused quartz glass measured within 6 x 10^-10 /K" by P F Egan in International Journal of Thermophysics 2024.

Dataset and scripts supporting the article "D-to-A_eff converter", a proceeding presented at the NCSLI Workshop & Symposium 2022, Grapevine TX.The dataset consists of two main parts: dimensional measurements and conversion scripts.

This set corresponds to a (pending) publication, where we attempt to model spectral features appearing in the lab by calculating many segments of an inhomogeneous field. Every data set here is a transmission value, either normalized to 1 in modeled data, or an arbitrary-scaled voltage reading from a photodiode onto an oscilloscope. These are given in scans over coupling photon detuning, delta_C, which is divided by 2 pi, and given in MHz. Arrays are scans over delta_C, and position/fieldstrength, for figure 3.

This research software package contains Python code to execute experiments on deep generative modeling of classical random process models for noise time series. Specifically, it includes Pytorch implementations of two generative adversarial network (GAN) models for time series based on convolutational neural networks (CNNs): WaveGAN, a 1-D CNN model, and STFT-GAN, a 2-D CNN model. In addition, there are methods for generating and evaluating noise time series defined several by classical random process models.