This data publication contains the raw, extracted, and derived (e.g., peak areas) mass spectrometry data for illicit drug and mixture samples. These samples were analyzed by wipe-based collection or solvent-extraction, thermal desorption, and a transportable high-resolution time-of-flight mass spectrometer. Data were collected with both an in-line dielectric barrier discharge ionization source and an acetone-dopoant assisted vacuum ultraviolet (VUV) photoionization source.

Supporting information for the NIST Internal Report entitled: Per- and Polyfluoroalkyl Substances - Non-Targeted Analysis Interlaboratory Study Final Report. The files include four comma-separated value (CSV) text files, three of the CSV text files contain summary tables of all per and polyfluoroalkyl substances (PFAS) detected in the three test samples from the interlaboratory report (sample_a_table.csv, sample_b_table.csv, sample_c_table.csv) including the individual laboratory reported identities and their associated identification confidence levels.

Sim-PROCESD is a discrete event simulation package written in Python that is designed to model the behavior of discrete manufacturing systems. Specifically, it focuses on asynchronous production lines. It also provides functionality for modeling the degradation and maintenance of machines in these systems. Sim-PROCESD provides class definitions for manufacturing devices/components that can be configured by the user to model various real-world manufacturing systems.

This dataset contains data used in the investigation of Universally Unique Identifiers (UUIDs) that will enable a standards-based digital thread of product data in ISO 10303-242. Included are EXPRESS schema used for implementation and .stp files that were exported from native CAD (CATIA V5, Creo, and NX). UUIDs were assigned to CAD features during .stp export for each of the four design iterations.

A heterogeneous dataset of human measurement data and human-generated text. This dataset was generated by TechSolve Inc. (techsolve.org) as a collaborative effort with NIST. Respondents were asked to observe and evaluate a machining process in which a rotary bit (the "tool") removed layers of a workpiece until the tool was worn to exhaustion. One trial and 19 official experiments were completed, one for each of 20 tools.

A table of known interferences for per- and polyfluoroalkyl substances using mass spectrometry.

Data here contain and describe an open-source structured query language (SQLite) portable database containing high resolution mass spectrometry data (MS1 and MS2) for per- and polyfluorinated alykl substances (PFAS) and associated metadata regarding their measurement techniques, quality assurance metrics, and the samples from which they were produced. These data are stored in a format adhering to the Database Infrastructure for Mass Spectrometry (DIMSpec) project. That project produces and uses databases like this one, providing a complete toolkit for non-targeted analysis.

This data publication contains the mass spectrometry chemical characterization of microplastic and nanoplastic chemical analysis. The data from this study includes mass spectra of pure, mixed, and weathered microplastics and nanoplastics at high and low fragmentation, extracted ion chronograms, Kendrick mass defect plots, code, and the derived and processed data. The data analysis code (MATLAB 2022a*) used for unsupervised learning of cluster and compositional relationships is also included.

An experiment was set up within a machine tool at the National Institute of Standards and Technology (NIST) to test vision-based thermal drift tracking methods. A wireless microscope within a tool holder in the spindle is used to capture videos of image targets attached to the worktable. For each target, one video is captured during spindle rotation orthogonal to the worktable and another video is captured during axis translation orthogonal to the worktable.

Python scripts and Python+Qt graphical user interface for calculating Feldman-Cousins confidence intervals for low-count Poisson processes in the presence of a known background and for Gaussian processes with a physical lower limit of 0.