SRM 3461 is an AFM sized chip with an array of seven cantilevers on each chip. The uniformity of the chips offered for sale from wafer 2 is excellent and the SRM certificate reports values and uncertainties for the entire lot of chips for sale; however, this dataset provides the raw data used to certify the SRM. Since each chip is serialized, the raw data for a specific SRM chip can be accessed from this dataset. In addition to the laser Doppler vibrometry data and optical micrograph, this dataset contains the Mathematica code used to process the 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).

Software to perform audio device characterization using transmit volume optimization (TVO) of push-to-talk communication devices performing Mission Critical Voice (MCV) quality of experience (QoE) measurements.

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.

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.

Round 8 Holdout 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 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 dataset contains mechanical testing data at two different loading rates for two different aging conditions (wet and dry at 70 °C) for five different extraction time points for ultra high molar mass polyethylene (UHMMPE) unidirectional (UD) laminate. In each folder there is a spreadsheet (.csv) with the test data collected during loading, and a (.avi) video file for each of the tests done under those conditions.

The DVU dataset consists of 10 creative common movies for training and 4 creative common movies for testing used in the Deep Video Understanding grand challenges in 2020 and 2021. Total duration is about 17 hr.

A compilation of single fiber tensile tests for 6 different materials at 8 different gauge lengths. Fibers included are 3 different aramids, a liquid crystal polyester, regenerated cellulose, and nylon 6,6.

This dataset contains the raw data for the paper: Garey, W.; Henderson, T.; Sun, Y.; Rouil, R. and Gamboa, S. (2021). Modeling MCPTT and User Behavior in ns-3. In Proceedings of the 11th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - SIMULTECH, ISBN 978-989-758-528-9, pages 30-41. DOI: 10.5220/0010513200300041. This includes: Figure 4 - The talk spurt and talk session duration CDF. Figure 5 - Orchestrator pusher model example. Figure 7 - MCPTT Access Time for immediately granted requests with queuing disabled.