This data publication includes electron backscatter diffraction (EBSD) data used to support the development of ISO 13067 "Microbeam Analysis - Electron Backscatter Diffraction - Measurement of Average Grain Size" as part of a Versailles Project on Advanced Materials and Standards (VAMAS) work area. EBSD data was collected on two commercially pure titanium samples. Two different scanning electron microscopes were used, and two staff independently performed measurements of 5 selected areas.

The CCQM_Retrospectoscope system combines a nominally complete database of results from Consultative Committee for the Amount of Substance: Metrology in Chemistry and Biology (CCQM) studies with a number of graphical tools for trying to make sense of the data. This system supports a diverse collection of often eye-opening appraisals of participation and measurement performance throughout the history of the CCQM activities.

Our work towards a Structured Evaluation Methodology for Artificial Intelligence Technology (SEMAIT) aims to provide plots, tools, methods, and strategies to extract insights out of various machine learning (ML) and Artificial Intelligence (AI) data.

Included in this software is the MIg analyZeR (mizr) R software package that produces various plots. It was initially developed within the Multimodal Information Group (MIG) at the National Institute of Standards and Technology (NIST).

Digital light processing (DLP) vat photopolymerization (VP) additive manufacturing (AM) uses patterned UV light to selectively cure a liquid photopolymer into a solid layer. Subsequent layers are printed on to preceding layers to eventually form a desired 3 dimensional (3D) part. This data set characterizes the 3D geometry of a single layer of voxels (volume pixels) printed with photomasks assigned random intensity levels at every pixel. The masks are computer generated, then printed onto a glass cover slide.

The data are associated with figures in R. Tao, S. P. Phansalkar, A. M. Forster, B. Han, Investigation of Cure Kinetics of Advanced Epoxy Molding Compound Using Dynamic Heating Scan: An Overlooked Second Reaction, 2023 IEEE 73rd Electronic Components and Technology Conference (ECTC), Orlando, Florida, May 30 - June 2, 2023. https://doi.org/10.1109/ECTC51909.2023.00225

This is a test collection for passage and document retrieval, produced in the TREC 2023 Deep Learning track.

Hestia Fossil Fuel Carbon Dioxide Emissions Inventory for Urban Regions (Hestia FFCO2) provides data products for Los Angeles Basin, Northeast corridor, Indianapolis, and other U.S. Cities. Hestia FFCO2 datasets quantify greenhouse gases (GHG), such as carbon dioxide, emitted by urban regions, since cities are major contributors of anthropogenic GHG emissions. The Hestia FFCO2 datasets provide high spatial and temporal resolution CO2 concentrations at sub-county resolutions and annual/hourly time scales, specific to the region.

Cross-language information extraction and retrieval datasets developed for the evaluation of the IARPA BETTER program. The documents come from CommonCrawl. The IE annotations in three schemas are by MITRE and ARLIS. The IR queries and relevance judgments were done at NIST, and NIST was asked by IARPA to distribute the data in its final form. The tasks are all cross-language from English into one of Arabic, Farsi, Russian, Chinese, and Korean

This data set contains data used to generate plots in the paper "Microwave Characterization of Parylene C Dielectric and Barrier Properties".

It contains the broadband S-parameter measurements of Parylene C coated CPWs exposed to water and ionic fluid, simulation set-up files and RLCG results, formatted data for each of the plots, and scripts to generate each figure.

Simulations must be opened using ANSYS Electronics Desktop.
Scripts must be executed using MATLAB.
See readme file for complete description of each individual file.

This dataset contains a comparison of packet loss counts vs handovers using four different methods: baseline, heuristic, distance, and machine learning, as well as the data used to train a machine learning model. This data was generated as a result of the work described in the paper, "O-RAN with Machine Learning in ns-3," by the authors Wesley Garey, Tanguy Ropitault, Richard Rouil, Evan Black, and Weichao Gao from the 2023 Workshop on ns-3 (WNS3 2023), that was June 28-29, 2023, in Arlington, VA, USA, and published by ACM, New York, NY, USA.