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).

Daily mean and raw voltage volume transport data of the Florida Current collected with a submarine cable spanning from South Florida to the Grand Bahama Island in the Florida Strait from 1 January 2021 to 31 December 2021. These data were collected and submitted by Denis Volkov of the Atlantic Oceanographic and Meteorological Laboratory (AOML) as part of the Western Boundary Time Series/Florida Current Transport project. The web page for these data can be found at http://www.aoml.noaa.gov/phod/floridacurrent.

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.

Daily mean and raw voltage volume transport data of the Florida Current collected with a submarine cable spanning from South Florida to the Grand Bahama Island in the Florida Strait from 1 January 2022 to 31 December 2022. These data were collected and submitted by Denis Volkov of the Atlantic Oceanographic and Meteorological Laboratory (AOML) as part of the Western Boundary Time Series/Florida Current Transport project. The web page for these data can be found at http://www.aoml.noaa.gov/phod/floridacurrent.

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.