Source code for a WebAssembly web application that explores the contents of standardized friction ridge metadata (i.e., "Type 9") within ANSI/NIST-ITL compatible records

Physical interaction component libraries, covering areas not currently standardized in SysPhS.

Supplemental material to the article "Optical n(p, T_90) measurement suite 1: He, Ar, and N_2" by PF Egan and Y Yang in International Journal of Thermophysics 2023.The archive file contains research data and analysis scripts as follows:* The n(p, T_90) dataset for helium. A Python script analyzes the data to produce the estimate of temperature-dependent compressibility, and reproduces Fig. 2 from the article.* The n(p, T_90) dataset for argon. A Python script analyzes the data to determine T - T_90, and reproduces Fig. 3 from the article.

This code and example files were used to run the self-consistent field theory parameterization in the publication "Predicting the toughness of compatibilized polymer blends" by Robert J. S. Ivancic (OrcID: https://orcid.org/0000-0001-9969-2534, National Institute of Standards and Technology, Material Measurement Laboratory, Division 642, Group 1) and Debra J.

This high-fidelity dataset measures the holistic load flexibility performance of a suite of commonly used commercial building heating, ventilating, and air-conditioning (HVAC) and thermal storage equipment, under a variety of testing conditions. It was generated as a part DOE award number DE-EE0009153: Hardware-in-the-loop Laboratory Performance Verification of Flexible Building Equipment in a Typical Commercial Building.

Round rl-lavaworld-jul2023-train Train DatasetThis is the training data used to create and evaluate trojan detection software solutions. This data, generated at NIST, consists of Reinforcement Learning agents trained to navigate the Lavaworld Minigrid environment. 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.

TrojAI cyber-apk-nov2023 Train DatasetThis is the training data used to create and evaluate trojan detection software solutions. This data, generated at NIST, consists of small feed forward multi-layer perceptron type neural network models classifying APK feature vectors as malware or clean. 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.

TrojAI cyber-network-c2-feb2024 Train DatasetThis is the training data used to create and evaluate trojan detection software solutions. This data, generated at NIST, consists of ResNet18 and ResNet34 neural network models that classify botnet command and control (c2) and benign network traffic packets trained on the USTC-TFC2016 dataset. 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.

Data corresponding to Figures for Adkins et al J. Quant Spectrosc. Radiat. Transf. (2023). https://doi.org/10.1016/j.jqsrt.2023.108732

The development of large residual elastic strains and stresses during laser powder-bed fusion (LPBF) additive manufacturing is one of the most significant barriers to widespread adoption. Accurate modeling of these strains and stresses is broadly recognized as an effective tool for mitigating these challenges, but rigorous validation data are needed. This data publication includes measurement data from diffraction-based characterizations of residual elastic strains in as-built (not heat treated) artifacts manufactured as part the the 2018 Additive Manufacturing Benchmark Series (AM Bench).