Data published in the paper "Using Open-Path Dual-Comb Spectroscopy to Monitor Methane Emissions from Simulated Grazing Cattle". A controlled methane release experiment was performed to determine if dual comb spectroscopy (DCS) can detect methane concentration enhancements above the atmospheric background produced by a typical herd of beef cattle in an extensive grazing system. Open-path DCS was used to measure downwind and upwind methane concentrations from ten point-sources of methane simulating cattle.

The abstract of the paper [1] is:

This paper describes differential sampling measurements of an ac source and a Josephson arbitrary waveform synthesizer (JAWS).
A new iterative approach for aligning the phases of the JAWS and the source waveforms was implemented to minimize
the differential voltage at the digitizer. A type-A uncertainty of 45 nV/V after 10 min was measured for a commercial
ac source at 1 V rms amplitude and 1 kHz.

Contains scans of a bin filled with different parts ( screws, nuts, rods, spheres, sprockets). For each part type, RGB image and organized 3D point cloud obtained with structured light sensor are provided. In addition, unorganized 3D point cloud representing an empty bin and a small Matlab script to read the files is also provided. 3D data contain a lot of outliers and the data were used to demonstrate a new filtering technique.

Red, Green, and Blue (RGB) and depth markerless body tracking systems were applied for industrial exoskeletons. The original images and their body tracking results are described in this data set. The data set describes the body tracking result changes by exoskeletons, industrial task poses, and camera viewpoints. A full-body exoskeleton, an upper-body exoskeleton, and an exosuit are used in the test. Stand, bend forward, squat, and crouch are defined as industrial poses with stretching arms forward, forward-up, up, forward-down, or down.

This data comprises the information that was used to assign values, the assigned values themselves, and the associated uncertainties. Certificate date 20 May 2024.

Datasets included in manuscript on simultaneous two-ladder sensing, titled "Independent Rydberg Atom Sensing using a Dual-Ladder Scheme" to be submitted to Applied Physics Letters. The data for figure 1 contains experimental EIT traces for both ladders, and figure 2 contains the data for false-color plots showing the polarization-specific response of each ladder.

Summary Data For Interlaboratory Study on Adeno-Associated Virus Particles. This contains all the data to be published in a journal article detailing the measurement of full-to-total ratio by analytical ultracentrifugation, size exclusion chromatography-multiangle light scattering, polymerase chain reaction, and ultraviolet spectrophometry. Samples here consisted of nominally 100% full particles, nominally 0% full particles, and a 50:50 mixture of the two.

Numerical values of all data points shown in figures for manuscript "A 64-pixel mid-infrared single-photon imager based on superconducting nanowire detectors", available on arXiv (https://arxiv.org/abs/2309.16890)
and in Applied Physics Letters 124, 042602, (2024) (https://doi.org/10.1063/5.0178931).

In embedded 3D printing, a nozzle is embedded into a support bath and extrudes filaments or droplets into the bath. Using OpenFOAM, we simulated the extrusion of filaments and droplets into a moving bath, for single filaments, single filaments being disturbed by a nozzle, and printing pairs of filaments. OpenFOAM is an open source computational fluid dynamics solver.

In embedded 3D printing, a nozzle is embedded into a support bath and extrudes filaments or droplets into the bath. However, moving the nozzle near an existing structure and writing new filaments can cause deformation and lack-of-fusion defects. Using OpenFOAM, we simulated what happens when a nozzle moves near an existing filament, and when a nozzle writes a second filament next to an existing filament. OpenFOAM is an open source computational fluid dynamics solver. This work used OpenFOAM 8 on a computing cluster. OpenFOAM input files were generated using Python 3.7.