Round 9 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 one of three tasks, sentiment classification, named entity recognition, or extractive question answering 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.

This repository contains results for experiments on generative modeling of synthetic Orthogonal-Frequency Division Multiplexing (OFDM) communication signals. (This record supersedes Software and Data for Modeling OFDM Communication Signals with Generative Adversarial Networks, formerly at https://doi.org/10.18434/mds2-2428)

Simantha is a discrete event simulation package written in Python that is designed to model the behavior of discrete manufacturing systems. Specifically, it focuses on asynchronous production lines with finite buffers. It also provides functionality for modeling the degradation and maintenance of machines in these systems. Classes for five basic manufacturing objects are included: source, machine, buffer, sink, and maintainer. These objects can be defined by the user and configured in different ways to model various real-world manufacturing systems.

The Enriched Citation API provides the Intellectual Property 5 (IP5 - EPO, JPO, KIPO, CNIPA, and USPTO) and the Public with greater insight into the patent evaluation process. It allows users to quickly view information about which references, or prior art, were cited in specific patent application Office Actions, including: bibliographic information of the reference, the claims that the prior art was cited against, and the relevant sections that the examiner relied upon.

The Enriched Citation API provides the Intellectual Property 5 (IP5 - EPO, JPO, KIPO, CNIPA, and USPTO) and the Public with greater insight into the patent evaluation process. It allows users to quickly view information about which references, or prior art, were cited in specific patent application Office Actions, including: bibliographic information of the reference, the claims that the prior art was cited against, and the relevant sections that the examiner relied upon.

NIST has created a Test System to measure conformance of Computer-Aided Design (CAD) software to American Society of Mechanical Engineers (ASME) standards for Product and Manufacturing Information (PMI), specifically geometric dimensioning and tolerancing (GD&T) information. The test system includes: test case definitions (2D CAD drawings with PMI), CAD models generated from the test cases, STEP files generated from the CAD models, and verification and validation reports.

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.

The STEP File Analyzer and Viewer is a software tool that generates a spreadsheet or a set of CSV (comma-separated value) files from a STEP (ISO 10303 Standard for Exchange of Product model data) Part 21 file. STEP files are used to represent product and manufacturing information (PMI) and for data exchange and interoperability between Computer-Aided Design (CAD), Manufacturing (CAM), Analysis (CAE), and Inspection (CMM) software related to the smart manufacturing digital thread. STEP is also used for the long-term archiving and retrieval of product data.

ANDiE the Autonomous Neutron Diffraction Explorer is a tool for autonomously discovering the magnetic transition temperature and transition dynamics of a material from neutron diffraction experiments. The Jupyter notebooks used to implement ANDiE can be found here: https://github.com/usnistgov/ANDiE-v1_0 The Jupyter notebooks contained therein are of ANDiE as implemented at the WAND2 instrument at the HB-2C beamline at the High-Flux Isotope Reactor (HFIR) at Oak Ridge National Laboratory (ORNL).

Round 8 Test 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. 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. This dataset consists of 360 QA AI models using a small set of model architectures.