The SARS-CoV-2 virus has put a strain on the supply and distribution of N95 masks, with some hospitals electing to reuse masks after disinfection. Currently, vaporized hydrogen peroxide (VHP) mask disinfection systems are being deployed at multiple locations throughout the United States, for example in shipping containers and in small rooms. Such enclosures will be of a different size, have different surface materials and have varying air change rates; all these parameters will impact the VHP dose that the masks receive.

This data was used for main results for the paper entitled "Coherent Optical Clock Down-Conversion for Microwave Frequencies with 10-18 Instability".
We could calculate relative phase fluctuation and Allan deviation for both Yb optical clocks and 10 GHz microwaves. Uncertainty of our down-conversion system was also calculated from this.

System models and model-based engineering methods have the promise of transforming the way that industrial engineers interact with production and logistics systems. Model-based methods play a role in improving communication between stakeholders, interoperability between systems, automated access to consistent analysis models, and multi-disciplinary design methods for complex systems.

This GitHub repository contains a downloadable snapshot of National Institute of Standards and Technology's COVID-19 Data Repository, curated from the COVID-19 Open Research Dataset (CORD-19) provided by the Allen Institute for AI. Curated Archive for Covid-19 Research Challenge Dataset- The COVID-19 Data Repository provides searchable CORD-19 data and metadata, including full-text extracted from the original CORD-19 JavaScript Object Notation (JSON) files. It is built using the Configurable Data Curation System (CDCS) developed at NIST.

This report describes the test equipment, process, analysis, and data file formats for the Numisheet 2020 tensile and tension/compression testing of the four materials associated with the Numisheet 2020 conference benchmarks. The four materials include two steel alloys, DP980 for Benchmark 1 and DP1180 for Benchmark 2, and two 6000 series aluminum alloys, AA6xxx-T4 for Benchmark 1 and AA6xxx-T81 for Benchmark 2, that will be referred to here as BM1-DP980, BM2-DP1180, BM1-6xxx-T4, and BM2-6xxx-T81, respectively.

An archival version of the potter C++ library for integrating potentials to obtain virial coefficients.

Python module 'optbayesexpt' uses optimal Bayesian experimental design methods to control measurement settings in order to efficiently determine model parameters. Given an parametric model - analogous to a fitting function - Bayesian inference uses each measurement 'data point' to refine model parameters. Using this information, the software suggests measurement settings that are likely to efficiently reduce uncertainties. A TCP socket interface allows the software to be used from experimental control software written in other programming languages.

This data set provides heat detector temperatures in a single story three-compartment structure. 1000 sets of detector temperatures are generated using CData [1]. The data set are obtained based on simulation runs with various t-squared fires. The peak heat release rate and time to peak range from approximately 50 kW to 2200 kW and from 50 s to 1400 s, respectively. A detailed description of this work can be found in Ref. [2]. [1] Tam, W.C., Fu, E.Y., Peacock, R., Reneke, P., Wang, J., Li, J. and Cleary, T., 2020.

The goal of the MUD (Manufacturer Usage Description) specification is to provide a means for manufacturers of Things to indicate what sort of access and network functionality they require for the Thing to properly function. A manufacturer associates a MUD file with a device which specifies an ACL for the device to within deployment specific parameters. The MUD standard is defined in RFC 8520 This repository publishes a public domain scalable implementation of the IETF MUD standard. MUD is implemented on SDN capable switches using OpenDaylight as the SDN controller.

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.