This dataset contains information from investigating the effects of micro-computed tomographic imaging irradiation on vinyl nitrile foam and code for a finite element user material to model these effects. More details are available in the README.txt and data_summary.txt files in the main directory.

The National Institute of Standards and Technology (NIST) maintains the United States' primary standards for traceable RF and mm-wave power measurements. In the 2.4 mm connector type, the specialized sensors that NIST currently uses as primary standards are not commercially available, which raises concerns about the long-term sustainability of NIST's measurement capabilities. These concerns motivated us to explore the option of using commercially available thermoelectric power sensors.

We present the ChemNLP library that can be used for (1) curating open access datasets for materials and chemistry literature, developing and comparing traditional machine learning, transformers and graph neural network models for (2) classifying and clustering texts, (3) named entity recognition for large-scale text-mining, (4) abstractive summarization for generating titles of articles from abstracts, (5) text generation for suggesting abstracts from titles, (6) integration with density functional theory dataset for identifying potential candidate materials such as superconductors, and (7)

Graph neural networks (GNN) have been shown to provide substantial performance improvements for atomistic material representation and modeling compared with descriptor-based machine learning models. While most existing GNN models for atomistic predictions are based on atomic distance information, they do not explicitly incorporate bond angles, which are critical for distinguishing many atomic structures. Furthermore, many material properties are known to be sensitive to slight changes in bond angles.

Lack of rigorous reproducibility and validation are major hurdles for scientific development across many fields. Materials science in particular encompasses a variety of experimental and theoretical approaches that require careful benchmarking. Leaderboard efforts have been developed previously to mitigate these issues. However, a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with both perfect and defect materials data is still lacking.

Parametrized tight-binding models fit to first-principles calculations can provide an efficient and accurate quantum mechanical method for predicting properties of molecules and solids. However, well-tested parameter sets are generally only available for a limited number of atom combinations, making routine use of this method difficult. Furthermore, many previous models consider only simple two-body interactions, which limits accuracy.

The JARVIS-Tools is an open-access software package for atomistic data-driven materials design. JARVIS-Tools can be used for a) setting up calculations, b) analysis and informatics, c) plotting, d) database development and e) web-page development.

Delta-sigma algorithms are used to determine the desired sequence of quantum-based voltage pulses used by the Josephson Arbitrary Waveform Synthesizer (JAWS) to create calculable voltage waveforms. This data set contains a comparison (Fig 1. of the CPEM 2024 abstract) of a 1 kHz pulse pattern with rms amplitude of 2 mV without post-processing and with post-processing which modifies the problematic pulse pattern to remove incorrect pulses at the 0.75 ms pattern wrap point.

The data included here is for the phase error calculations for different VNA calibrations without and with relaxation of the RX cable. We show that the phase errors reduce significantly in the latter case. This dataset is for our paper that will appear in the 107th ARFTG Microwave Measurement Conference (ARFTG = The Automatic Radio Frequency Techniques Group) taking place on 12 June 2026 in Boston, MA.

Dataset for multiple publishable figures in the paper entitled "Stability Study of ac Voltage Source using Josephson Voltage Standards"
in CPEM 2024 abstract