Records contained within the NIST public data listing are part of the National Institute of Standards and Technology (NIST) enterprise data inventory. These data products are generated as part of the NIST mission, spanning multiple disciplines of scientific, engineering and technology research. This listing is provided as a data.json file, using the standard Project Open Data metadata schema v. 1.1.

Dataset for figures in the publication, "Using EM Side-Channels Near a Bluetooth Server Implementation to Monitor Bit-Level Leakages in BLE Communications Channels," by V.V Iyer and J.D. Rezac (GLSVLSI 2024). The abstract for this publication is as follows: Unintentional electromagnetic (EM) emanations from general purpose embedded systems have been comprehensively studied as data recovery and monitoring tools for hardware security.

The product search track focuses on IR tasks in the world of product search and discovery. This track seeks to understand what methods work best for product search, improve evaluation methodology, and provide a reusable dataset which allows easy benchmarking in a public forum.

The Deep Learning track focuses on IR tasks where a large training set is available, allowing us to compare a variety of retrieval approaches including deep neural networks and strong non-neural approaches, to see what works best in a large-data regime.

Metadata for all final NIST Technical Series publications available online, created and maintained by the NIST Research Library.

The Authoring Tools for Multimedia Content (AToMiC) Track 2023 aims to build reliable benchmarks for multimedia search systems. The focus of this track is to develop and evaluate IR techniques for text-to-image and image-to-text search problems.

TrojAI llm-pretrain-apr2024 Train DatasetThis is the training data used to create and evaluate trojan detection software solutions. This data, generated at NIST, consists Llama2 Large Language Models refined using fine-tuning and LoRA to perform next token prediction. A known percentage of these trained AI models have been poisoned with triggers which induces modified behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers into the model weights.

In this paper we describe an enhanced three-antennagain extrapolation technique that allows one to determine antennagain with significantly fewer data points and at closerdistances than with the well-established traditional three-antennagain extrapolation technique that has been in use for overfive decades. As opposed to the traditional gain extrapolationtechnique, where high-order scattering is purposely ignored soas to isolate only the direct antenna-to-antenna coupling, we showthat by incorporating third-order scattering the enhanced gainextrapolation technique can be obtained.

This data was used in the TREC 2015 and 2016 total recall track. The goal of the total recall track was to help develop retrieval systems tuned to retrieving ALL relevant information, as opposed to common web search engines where one good answer could be sufficient.

This archive contains the bidirectional reflectance distribution function (BRDF) data of real-world samples associated with the paper Aaron M. Goldfain, Grace E. Waters, Lynn Davis, Heather J. Patrick, and Thomas A. Germer, "Validated UV-C bidirectional reflectance distribution function measurements with a spectrophotometer directional reflectance module," to be submitted (2024). The figures in the paper show a subset of the data included in this archive.This archive contains the measured BRDF of materials commonly found in public spaces where UV disinfection may be used.