Round 8 Train DatasetThis is the training data used to construct and evaluate trojan detection software solutions. This data, generated at NIST, consists of natural language processing (NLP) AIs trained to perform extractive question answering (QA on English text. 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.

These LC-MS and LC-MS/MS raw data were collected for purposes of an interlaboratory study evaluating the multi-attribute method (MAM). Tryptic digests of native NISTmAb (the "Reference" and the "Unknown" samples), degraded NISTmAb (the "pH Stress" sample) and NISTmAb spiked with 15 heavy-labeled synthetic peptides (the "Spike" sample) were sent to each participating laboratory. One injection of each digest was acquired in MS-only mode, while a second injection was acquired in MS/MS mode.

Materials discovery and development necessarily begins with the preparation and identification of product phase(s). Crystalline compounds can be identified by their characteristic diffraction patterns using X-rays, neutrons, and or electrons. An estimated 20,000 X-ray diffractometers and a comparable number of electron microscopes are used daily in materials research and development laboratories for this purpose.

This tutorial provides mechanical drawings, electrical schematics, parts lists, STL files for 3D-printed parts, IGS files, and instructions for automated machining necessary for construction of a dual-protease, subzero, liquid chromatography system for hydrogen-deuterium exchange mass spectrometry. Electrical schematics for construction of a multi-zone temperature controller that regulate to ±0.1 oC are also included in this tutorial.

Round 1 Training DatasetThe data being generated and disseminated is the training data used to construct trojan detection software solutions. This data, generated at NIST, consists of human level AIs trained to perform a variety of tasks (image classification, natural language processing, etc.). 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.

Round 1 Test DatasetThe data being generated and disseminated is the test data used to evaluate trojan detection software solutions. This data, generated at NIST, consists of human level AIs trained to perform a variety of tasks (image classification, natural language processing, etc.). 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.

Round1 Holdout DatasetThe data being generated and disseminated is the holdout data used to evaluate trojan detection software solutions. This data, generated at NIST, consists of human level AIs trained to perform a variety of tasks (image classification, natural language processing, etc.). 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.

Round 2 Training DatasetThe data being generated and disseminated is the training data used to construct trojan detection software solutions. This data, generated at NIST, consists of human level AIs trained to perform image classification. 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.

Supporting datasets and algorithms (R-based) for the manuscript entitled "Development of a Tool to Determine the Variability of Consensus Mass Spectra", including an R Markdown script to reproduce the manuscript's figures.

This Standard Reference Material (SRM) is intended for use as a primary calibration standard for the quantitative determination of barium. A unit of SRM 3104a consists of five 10 mL sealed borosilicate glass ampoules of an acidified aqueous solution prepared gravimetrically to contain a known mass fraction of barium. The solution contains nitric acid at a volume fraction of approximately 1 %, which is equivalent to a concentration (molarity) of approximately 0.16 mol/L. This data is public in the Certificate of Analysis for this material.