This dataset contains raw mechanical data (strain, stress and strain rate curves and initial temperatures) for compression experiments performed on annealed 1045 steel using the NIST Kolsky Bar apparatus and a servohydraulic test machine that were used to generate fit coefficients for the plasticity model described in the NIST publication entitled: "A Dynamic Plasticity Model for Rapidly-Heated 1045 Steel up to 1000 °C" with authors S. P. Mates and S.-Y. Li.

Supplementary files to accompany the the paper "Super-Ancillary Equations for Cubic Equations of State" of Ian Bell and Ulrich Deiters in Ind. Eng. Chem. Res. Abstract: Calculation of thermodynamic phase equilibrium is error-prone and can fail both near the critical point and at very low temperatures due to the limited precision available in double precision arithmetic. Most importantly, these calculations frequently represent a computational bottleneck.

This data is provided as a supplement to NIST Technical Note 2159 Laboratory Method for Recording AWS-3 LTE Waveforms available at https://doi.org/10.6028/NIST.TN.2159. In particular, the data provided here are a compressed version of all the IQ recordings discussed in the report, with diagnostic information. The data is structured as a compressed archive, Data.zip, for each experimental configuration and capture repeat, resulting in 112 compressed archives organized by directory structure.

As monoclonal antibodies have become a vital resource in medicine, knowledge of their complex molecular structures has increased in importance. Thousands of Fab and Fc fragments are described in the Protein Data Bank. Whole antibodies have been imaged by EM methods and in a few cases, crystallized. The central hinge lacks a unique stable conformation and its dynamic properties are important to antibody function.

This record contains a Python script that was used to generate the phase boundaries with superancillary Chebyshev expansion curves. Running the script in Python 3.8 will output the figures and results from the paper. This paper was presented at the ORC 2021 conference in Munich, Germany, paper # 141. Requirements: conda/pip: numpy, scipy, matplotlib, pandas. pip: CoolProp, ChebTools

This dataset contains results from numerical analysis of the sidelink physical layer capacities for LTE and NR.

This data set contains files included in the detailed instructional demonstration paper submitted to Integrating Materials and Manufacturing Innovation. The detailed instructional demonstration paper includes documentation detailing how to configure and carry out a repeatable Rietveld Refinement with the software MAUD. The data set provides: diffraction data from two different neutron diffraction measurements, crystallographic information files, and configuration files for the refinement process.

Data for generation of figures in "Near-infrared cavity ring-down spectroscopy measurements of nitrous oxide in the (4200)<-(0000) and (5000),<-(0000) bands" DOI: https://doi.org/10.1016/j.jqsrt.2021.107527

Dataset for generation of figures in Air-broadening in near-infrared carbon dioxide line shapes: quantifying contributions from O2, N2, and Ar (https://doi.org/10.1016/j.jqsrt.2021.107669)

Round 6 Test DatasetThis is the test data used to construct and evaluate trojan detection software solutions. This data, generated at NIST, consists of natural language processing (NLP) AIs trained to perform text sentiment classification on English text. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers.