The PerfLoc Prize Competition (https://perfloc.nist.gov) was developed by NIST during 2015-2017 and was run during 2017-2018. The Competition was concluded with a single winner on May 16, 2018. However, NIST believes the data collected for the PerfLoc Competition is still of value to the R&D community, because there is still room to develop better signal processing and data fusion algorithms that would fuse various types of smartphone data collected in this project to develop indoor localization apps with higher localization accuracy.

The Camp Fire ignited on November 8, 2018 in the foothills of the Sierra Nevada in Butte County, California. The first 24 hours were characterized by a fast-moving fire with initial spread driven by high winds up to 22 m/s (50 mi/h) and long-range spotting up to 6.3 km (3.9 mi) into the community. The fire quickly impacted the communities of Concow, Paradise, and Magalia. The Camp Fire became the most destructive and deadly fire in California history, with over 18000 destroyed structures, 700 damaged structures, and 85 fatalities.

NIST Fingerprint Image Quality (NFIQ) 2 is open source software that links image quality of optical and ink 500 PPI fingerprints to operational recognition performance. This allows quality values to be tightly defined and then numerically calibrated, which in turn allows for the standardization needed to support a worldwide deployment of fingerprint sensors with universally interpretable image qualities.

This data repository contains original files (fcs) of flow cytometry experiments. The data was used to demonstrate the use of stochastic regression to quantify subpopulations of cells that have distinctly different genome copies per cell within a heterogenous population of Escherichia coli (E. coli) cells.  This new approach gives estimates of signal and noise, the former of which is used for analysis, and the latter is used to quantify uncertainty.

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

The purpose of this mapping is to provide the relationship between NIST Cybersecurity Framework (CSF) v1.1 and the NERC Critical Infrastructure Protection (CIP) Standards.Target Audience:The intended audience are NERC registered entities of the Energy Sector Critical Infrastructure, electric segment, seeking to enhance the cyber security of the bulk electric system.This record supersedes https://doi.org/10.18434/mds2-2348

The following data files include microstructure measurement results associated with the 2022 Additive Manufacturing Benchmark test series (AM Bench 2022) AMB2022-03 set of benchmarks. These AMB2022-03 benchmarks explore a range of individual and overlapping melt pool behaviors using individual laser tracks and 2D arrays of laser tracks (pads) on solid metal IN718 plates. For the individual laser tracks, a range of laser parameters was used, with variations in laser power, speed, and spot diameter.

M-cresol purple is the most widely used pH indicator dye for seawater pH measurements. Impurities in the indicator are known to absorb strongly at one of the wavelengths used in spectrophotometric pH determination and lead to large biases in the pH measurements. This repository contains data and Matlab scripts to facilitate the implementation of a DD-SIMCA model for detecting residual impurities in purified m-cresol purple (mCP) relevant to climate quality seawater pH measurements.

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.