U.S. flag

An official website of the United States government

Dot gov

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Https

Secure .gov websites use HTTPS
A lock () or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

Breadcrumb

  1. Home

Dataset Search

Search results

87 results found

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

MAM Consortium Interlaboratory Study Raw Data

Data provided by  National Institute of Standards and Technology

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.

Tags: attribute analytics,interlaboratory study,multi-attribute method,MAM Consortium,mass spectrometry,new peak detection,NPD,purity testing,round robin,targeted analytics,NISTmAb,

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

NIST Inorganic Crystal Structure Database (ICSD)

Data provided by  National Institute of Standards and Technology

Materials discovery and development necessarily begins with the preparation and identification of product phase(s). Crystalline compounds can be identified by their characteristic diffraction patterns using X-rays, neutrons, and or electrons. An estimated 20,000 X-ray diffractometers and a comparable number of electron microscopes are used daily in materials research and development laboratories for this purpose.

Tags: chemical structures,crystallography,crystal structures,diffraction,disorder,electrons,identification,inorganic,neutrons,magnetic,metals,minerals,materials,Rietveld,synchrotron,twinned,x-rays,Advanced Materials,manufacturing,Safety,Security and Forensics,

Modified: 2024-02-22

Views: 0

Construction of a Dual-enzyme, Subzero (-30 degree C) Chromatography System and Multi-channel Precision Temperature Controller for Hydrogen-Deuterium Exchange Mass Spectrometry

Data provided by  National Institute of Standards and Technology

This tutorial provides mechanical drawings, electrical schematics, parts lists, STL files for 3D-printed parts, IGS files, and instructions for automated machining necessary for construction of a dual-protease, subzero, liquid chromatography system for hydrogen-deuterium exchange mass spectrometry. Electrical schematics for construction of a multi-zone temperature controller that regulate to ±0.1 oC are also included in this tutorial.

Tags: liquid chromatography,hydrogen-deuterium exchange,mass spectrometry,precision,peptide,protein,proteolysis,proteomics,reference material,temperature control.,

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

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

Optical scattering measurements and simulation data for one-dimensional (1-D) patterned periodic sub-wavelength features

Data provided by  National Institute of Standards and Technology

This data set consists of both measured and simulated optical intensities scattered off periodic line arrays, with simulations based upon an average geometric model for these lines. These data were generated in order to determine the average feature sizes based on optical scattering, which is an inverse problem for which solutions to the forward problem are calculated using electromagnetic simulations after a parameterization of the feature geometry.

Tags: electromagnetic simulations,simulations,experimental,angle-resolved scattering,scattering,gratings,patterned semiconductors,semiconductors,scatterfield microscopy,bright-field microscopy,microscopy,inverse problems,machine learning,

Modified: 2024-02-22

Views: 0