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.

The data are averaged specific heat capacity measurement results and associated expanded uncertainties for nine conventional and alternative aviation fuels (Jet A, Jet A-1, JP-8, JP-5, S-8, S-5, IPK, HRJ Camelina, and HRJ Tallow), which are presented in Tables S10-S18 of the Supplemental Information file accompanying the manuscript entitled "Comparison of Heat Capacity Measurements of Alternative and Conventional Aviation Fuels".

The following data files include microstructure measurement results associated with the 2022 Additive Manufacturing Benchmark test series (AM-Bench 2022) AMB2022-01 benchmark on laser powder bed fusion (LPBF) 3D builds of nickel-based superalloy IN718 test objects.

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.

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.

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

Article and dataset presented at the NCSLI Workshop & Symposium 2022 in Grapevine TX.Three datasets are included, corresponding to three separate runs (cycles of expansivity measurement).Run 1: cycled specimen FP_333 and FP_152 side-by-sideRun 2: FP_152 was potted with slip-fit tubes at its ends, and the run cycled specimen FP_333 and FP_152Potted side-by-sideRun 3: cycled specimen FP_333 and FP_154 side-by-sideThe Python file produces Fig. 3 from the article, and includes some literature/historical data on the thermal expansion of fused quartz glass

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 contains the raw time-series data for a study of liposome stability in aqueous fluid solution over the course of a year. The study was carried out using orthogonal nanoparticle tracking analysis (NTA) and asymmetric flow field flow fractionation (AF4) with multi-angle light scattering techniques to independently measure particle diameter and particle number concentration throughout the course of the study.