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**SUPERSEDED** Software and Data for Modeling OFDM Communication Signals with Generative Adversarial Networks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This software and data have been superseded.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
ml_uncertainty: A Python module for estimating uncertainty in predictions of machine learning models
Data provided by National Institute of Standards and Technology
This software is a Python module for estimating uncertainty in predictions of machine learning models. It is a Python package that calculates uncertainties in machine learning models using bootstrapping and residual bootstrapping. It is intended to interface with scikit-learn but any Python package that uses a similar interface should work.
Tags: uncertainty analysis,machine learning,model calibration,
Modified: 2024-02-22
Views: 0
Data from Laboratory Tests of a Prototype Carbon Dioxide Ground-Source Air Conditioner
Data provided by National Institute of Standards and Technology
These data from the laboratory tests of a prototype residential liquid-to-air ground-source air conditioner (GSAC) using CO2 as the refrigerant. The data collection and processing methods are described in detail in this report:
Report Title: "Laboratory Tests of a Prototype Carbon Dioxide Ground-Source Air Conditioner", NIST Technical Note 2068
Publication Date: October 2019
DOI: https://doi.org/10.6028/NIST.TN.2068
Authors: Harrison Skye, Wei Wu
Tags: Air conditioner,carbon dioxide,CO2,ground-source heat pump,subcritical and transcritical cycles,
Modified: 2024-02-22
Views: 0
Challenge Round 0 (Dry Run) Test Dataset
Data provided by National Institute of Standards and Technology
This dataset was an initial test harness infrastructure test for the TrojAI program. It should not be used for research. Please use the more refined datasets generated for the other rounds. The data being generated and disseminated is training, validation, and test 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.).
Tags: Trojan Detection,Artificial Intelligence,ai,machine learning,Adversarial Machine Learning,
Modified: 2024-02-22
Views: 0
High Accuracy Near-infrared Carbon Dioxide Intensity Measurements to Support Remote Sensing
Data provided by National Institute of Standards and Technology
Data files for the publication "High Accuracy Near-infrared Carbon Dioxide Intensity Measurements to Support Remote Sensing" in Geophysical Research Letters
Tags: greenhouse gases,carbon dioxide,remote sensing,
Modified: 2024-02-22
Views: 0
Active Evaluation Software for Selection of Ground Truth Labels
Data provided by National Institute of Standards and Technology
This software repository contains a python package Aegis (Active Evaluator Germane Interactive Selector) package that allows us to evaluate machine learning systems's performance (according to a metric such as accuracy) by adaptively sampling trials to label from an unlabeled test set to minimize the number of labels needed. This includes sample (public) data as well as a simulation script that tests different label-selecting strategies on already labelled test sets. This software is configured so that users can add their own data and system outputs to test evaluation.
Tags: active evaluation,machine learning,ar,
Modified: 2024-02-22
Views: 0
Simulated Radar Waveform and RF Dataset Generator for Incumbent Signals in the 3.5 GHz CBRS Band
Data provided by National Institute of Standards and Technology
This software tool generates simulated radar signals and creates RF datasets. The datasets can be used to develop and test detection algorithms by utilizing machine learning/deep learning techniques for the 3.5 GHz Citizens Broadband Radio Service (CBRS) or similar bands. In these bands, the primary users of the band are federal incumbent radar systems. The software tool generates radar waveforms and randomizes the radar waveform parameters.
Tags: 3.5 GHz,CBRS,LTE,ESC,radar,radio frequency signals,spectrum,machine learning,deep learning,detection,
Modified: 2024-02-22
Views: 0