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.

This archive contains the spatial frequency domain Mueller matrix data associated with the paper J. Chue-Sang, M. Litorja, A. M. Goldfain, and T. A. Germer, "Spatial Frequency domain Mueller matrix imaging," J. Biomedical Optics 27(12), 126003 (2022). The paper shows a subset of the data included in this archive. A Python script, analyze.py, is provided to assist the user in reading the data. The script can be run without any arguments from the top folder and will generate all the figures included in this archive.

Provides data that will measure hunger and food security. It will provide data on food expenditure, access to food, and food quality and safety.

Data is for "Effects of Non-Sinusoidal Current Phase Relationships on Single Flux Quantum Circuits" and publication in IEEE Transactions on Applied Superconductivity. Data is from WRspice simulations, as well as MALT margin analysis. Further data analysis was done in python. One set of data is the pulse propagation delay of a chain of JTL's with different current phase relationships. The other set of data is the operating margins of T flip-flop circuits that have been optimized for different current-phase relationships.

This challenge is to predict the macroscopic stress-strain response of compression samples across a range of temperatures taken from the base leg of the IN625 AMB2018-01 build in both the build direction (Z-axis) and a transverse-build direction (Y-axis). The specific temperatures of interest are 298 K, 523 K, and 773 K. The calibration data provided in this dataset corresponds to the build direction compression tests done at 298 K and 773 K.

This is the dataset for 4 publishable figures in 2 page abstracted titled "Josephson arbitrary waveform synthesizer for ac voltage calibrations" submitted to the Conference on Precision Electromagnetic Measurements, CPEM 2022. QLR=Quantum Locking Range.Fig. 1. QLR (2 V rms waveforms) as a function of the dc bias current offset in all 4 arrays of Bias 1.Fig. 2. QLR (2 V rms waveforms) as a function of the compensation current amplitude in all 4 arrays of Bias 1.Fig. 3.

The Intelligent Building Agents (IBA) project is part of the Embedded Intelligence in Buildings Program in the Engineering Laboratory at the National Institute of Standards and Technology (NIST).

Dataset includes stabilograms associated to a cellular communications anomaly detection experiment detailed in the publication Frey M, Gregg M, Rezac JD, Coder JB, Kord A, Otterstetter A, Quimby J, Weiss A (2022) Stabilograms: Testbed stability in a pilot experiment for cellular communications anomaly detection (National Institute of Standards and Technology, Boulder, CO), (Under Review at time of data submission).

Simulated transmission curves illustrating an efficient new calculation method. Data was produced for a publication, and is indexed by figure.