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85 results found

Thermal Conductivity of Binary Mixtures of 1,1,1,2-Tetrafluoroethane(R-134a), 2,3,3,3-Tetrafluoropropene (R-1234yf), and trans-1,3,3,3-Tetrafluoropropene (R-1234ze(E)) Refrigerants

Data provided by  National Institute of Standards and Technology

Workflow: The data that falls into this category are gathered from a measurement program and are published in the archival literature.

Tags: Advanced Materials,Energy,Environment and Climate,Physical Infrastructure,Safety,Security and Forensics,

Modified: 2024-02-22

Views: 0

Speed of Sound Measurements of Binary Mixtures of Difluoromethane (R-32) with 2,3,3,3-Tetrafluoropropene (R-1234yf) or trans-1,3,3,3-Tetrafluoropropene (R-1234ze(E)) Refrigerants

Data provided by  National Institute of Standards and Technology

Speed of Sound Measurements of Binary Mixtures of Difluoromethane (R-32) with 2,3,3,3-Tetrafluoropropene (R-1234yf) or trans-1,3,3,3-Tetrafluoropropene (R-1234ze(E)) Refrigerants

Tags: Advanced Materials,Energy,Environment and Climate,Physical Infrastructure,Safety,Security and Forensics,

Modified: 2024-02-22

Views: 0

4D-STEM characterization results from a thermal annealing series of poly(3[2-(2-methoxyethoxy)ethoxy]-methylthiophene-2,5-diyl) organic electrochemical transistor films

Data provided by  National Institute of Standards and Technology

This repository contains two types of datasets: 4D-STEM data collected from a thermal annealing series of P3MEEMT films. 4D-STEM data:Dose series: Collected from P3MEEMT film annealed at 115 C. Each HDF5 file was generated from the raw data using the fpdpy software package to reshape and embed metadata. No other preprocessing was performed. Annealing series: Collected from P3MEEMT films annealed at 75 C, 115 C, 145 C, and 175 C. Each temperature contains two sets of 4D-STEM data collected at low and high magnification.

Tags: Advanced Materials,Organic Semiconductors,4D-STEM,OECT,TEM,GIWAXS,

Modified: 2024-02-22

Views: 0

Dataset of channels and received IEEE 802.11ay signals for sensing applications in the 60GHz band

Data provided by  National Institute of Standards and Technology

The dataset can be used to develop and test algorithms for communication and sensing in the 60GHz band. The dataset consists of synthetically generated indoor mm-wave channels between a MIMO transmitter and a MIMO receivers. Multiple targets are moving in the room. Number of targets, velocity of each target and trajectory are randomized across the dataset. The dataset contains also noisy received IEEE 802.11ay channel estimation fields. The dataset is suitable for development and testing of machine/deep learning algorithms.

Tags: Sensing,communication,millimeter wave,dataset,machine learning,deep learning,channel model,IEEE 802.11ay,IEEE 802.11bf,

Modified: 2024-02-22

Views: 0

SRM 2969 Vitamin D Metabolites in Frozen Human Serum (Total 25-Hydroxyvitamin D Low Level) Data

Data provided by  National Institute of Standards and Technology

SRM 2969 Electronic files containing certified values and their uncertainties, and the data used to assign those values

Tags: vitamin D,25-hydroxyvitamin D,serum,mass spectrometry,

Modified: 2024-02-22

Views: 0

SRM 2970 Vitamin D Metabolites in Frozen Human Serum (25-Hydroxyvitamin D2 High Level) Data

Data provided by  National Institute of Standards and Technology

Electronic files containing certified values and their uncertainties, and the data used to assign those values.

Tags: vitamin D,25-hydroxyvitamin D,serum,mass spectrometry,

Modified: 2024-02-22

Views: 0

**SUPERSEDED** Software and Data for Modeling OFDM Communication Signals with Generative Adversarial Networks

Data provided by  National Institute of Standards and Technology

This software and data have been superseded. Please visit https://doi.org/10.18434/mds2-2532

Tags: generative adversarial network,machine learning,wireless communications,

Modified: 2024-02-22

Views: 0

Data Supporting "Seabird Tissue Archival and Monitoring Project (STAMP) Data from 1999-2010"

Data provided by  National Institute of Standards and Technology

Here we provide curated analytical chemistry data for eggs collected from 1999 to 2010 on a subset of species and analytes that were measured regularly and reasonably systematically. Included in this publication are 487 samples analyzed for 174 ubiquitous environmental contaminants such as (poly)brominated diphenyl ethers (BDEs), mercury, organochlorine pesticides (OCPs), and polychlorinated biphenyls (PCBs). Data were collated to form a dataset useful in chemometric and related analyses of the marine ecosystem in the north Pacific Ocean.

Tags: NIST Biorepository,eggs,tissues,BDEs,PBDEs,PCBs,Pesticides,mercury,trace elements,heavy metals,organic,inorganic,chemistry,stable isotopes,genetics,Environment and Climate,chemometric,machine learning,ML,seabird,bird,Pacific Ocean,

Modified: 2024-02-22

Views: 0

ANDiE: the Autonomous Neutron Diffraction Explorer.

Data provided by  National Institute of Standards and Technology

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).

Tags: Autonomous Experiments,neutron diffraction,machine learning,Active Learning,Artificial Intelligence,

Modified: 2024-02-22

Views: 0

Closed-loop Autonomous Materials Exploration and Optimization 1.0

Data provided by  National Institute of Standards and Technology

Code and demonstration data for the paper, "On-the-fly closed-loop materials discovery via Bayesian active learning," Kusne, A.G., Yu, H., Wu, C. et al. Nat Commun 11, 5966 (2020). https://doi.org/10.1038/s41467-020 19597-w Code: Closed-loop autonomous materials exploration and optimization. This code is used to control an autonomous materials exploration and optimization platform. It guides subsequent experiments to learn about a material's phase map and target functional properties in a unified framework.

Tags: autonomous,machine learning,phase map,materials optimization,

Modified: 2024-02-22

Views: 0