This data set provides the measurement data from EUV diffractometry, simulated fits to these data, and parametric values and uncertainties from these fits as first reported in Barnes et al., "Lab-based multi-wavelength EUV diffractometry for critical dimension metrology", Proc. SPIE 134261V (2025). These values and uncertainties for parametric geometries have been determined from these EUV diffractometry data using a tabletop coherent high- harmonic generation (HHG) source for four line-space arrays with CDs below 50 nm.

The following data files include mechanical behavior and corresponding X-ray diffraction measurement results associated with the 2022 Additive Manufacturing Benchmark test series (AM Bench) AMB2022-04 set of benchmarks. This set of benchmarks is a direct extension to the measurement data provided by an earlier set of benchmarks, AMB2018-01.

The yabadaba Python package provides an abstraction of database and data interactions. This makes it possible to interact with multiple different database infrastructures, and to manage the data interpretation and transformations of multiple different data schemas. The end goal is to provide a tool that makes it easy for data generators and maintainers to build user-friendly APIs that use common query methods and can be added in a modular fashion.

Security automation reference data is currently housed within the National Vulnerability Database (NVD). The NVD is the U.S. Government repository of security automation data based on security automation specifications. This data provides a standards-based foundation for the automation of software asset, vulnerability, and security configuration management; security measurement; and compliance activities. This data supports security automation efforts based on the Security Content Automation Protocols (SCAP).

These data will appear in [1]. The abstract for that paper is given below:

The files include experimental data from the publication titled "The trace of heat: on the predictive power of modeling transient diffusion" (https://doi.org/10.1007/s40964-025-01147-9). This data was used to develop assess the validity of the transient heat equation with phase change and temperature-dependent coefficients as a model to predict the evolution of melt pools for rapid turnaround scan strategies in a laser powder bed fusion (PBF-LB) additive manufacturing (AM) process.

This document is part of a series of reports describing experimental property measurements completed at the National Institute for Petroleum and Energy Research (NIPER) in Bartlesville, Oklahoma, in the 1980s and 1990s. Members of the Bartlesville Thermodynamics Group included William D. "Bill" Good, William V. "Bill" Steele, Bruce E. Gammon, Norris K. Smith, Stephen E. Knipmeyer, An "Andy" Nguyen, Timothy D. Klots, I. A. "Alex" Hossenlopp, Aaron P. Rau, William B. Collier, John F. Messerly, Ann G. Osborn, Susan Lee-Bechtold, Donald G. Archer, Ian R.

Dataset from AFM single-step and multi-step indentations performed on hybrid-bonding copper patterns at room temperature

Readme_AI is a Model Context Protocol (MCP) server using the FastMCP library that dynamically builds context for LLMs using its JSON file specification.

https://github.com/usnistgov/readme_ai

Abstract (from manuscript): In situ X-ray tomography experiments provide powerful insight into material deformation and damage evolution under mechanical load. However, laboratory-based tomography setups often suffer from long acquisition times that hamper real-time observation. In this work, we demonstrate a deep-learning-based reconstruction method capable of high-quality volume reconstructions from sparse (10X fewer) projection datasets.