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.

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.

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). A key part of the IBA Project is the IBA Laboratory (IBAL), a unique facility consisting of a mixed system of off the shelf equipment, including chillers and air handling units, controlled by a data acquisition system and capable of supporting building system optimization research under realistic and reproducible operating conditions.

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.

On-resonance Rydberg atom-based radio-frequency (RF) electric field sensing methods remain limited by the narrow frequency signal detection bands available by resonant transitions. The use of
an additional RF tuner field to dress or shift a target Rydberg state can be used to return a detuned signal field to resonance and thus dramatically extend the frequency range available for resonant

This tool uses techniques from ASTM E3200 for evaluating manufacturing investments from the perspective of environmentally sustainable manufacturing by pairing economic methods of investment analysis with environmental aspect of manufacturing. The economic techniques used include net present value, internal rate of return, payback period, and hurdle rate. These four techniques are deterministic, meaning that they deal with known values that are certain. The tool also conducts a sensitivity analysis using Monte Carlo techniques. The tool answers questions such as:

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.