PURR Registry

Data for Characterization of Acoustic Emissions from Analogue Rocks using Sparse Regression-DMDc

10.4231/4K64-4818

Charles Fieseler , Chven Mitchell , Laura J Pyrak-Nolte , Nathan Kutz

06/14/2022

Acoustic waveforms collected during the monitoring of moisture loss in synthetic rock samples composed of mortar, and mortar with either distributed clay or localized clay, under ambient laboratory conditions.

acoustic signals clay cracking Fractures Machine Learning Physics

RoboMal Malware Detection Dataset

10.4231/YN7G-H807

Byung-cheol Min , Haozhe Zhou , Richard Voyles , Upinder Kaur , Xiaxin Shen

10/15/2021

The RoboMal Dataset consists of 450 binary executables designed to facilitate malware detection in robotic software.

Engineering Technology Machine Learning malware Python Robotics security

Multi-Species Prediction of Physiological Traits with Hyper-Spectral Modeling

10.4231/FPHP-0153

Meng-yang Lin , Mitchell R Tuinstra

02/11/2022

High-throughput hyperspectral imaging in corn and sorghum can be used in multi-species models to predict water and nitrogen status of plants within and across these crop species.

Abiotic stress Agronomy Corn Ecophysiology High-throughput Phenotyping Machine Learning nitrogen content partial least square regression relative water content Remote Sensing Sorghum

General Aviation Flight Phase Identification using Automatic Dependent Surveillance-Broadcast (ADS-B)

10.4231/X0HZ-MF23

John H Mott , Qilei Zhang

08/23/2021

Dataset including KLAF ADS-B data and synthetic flight data for clustering validation purposes.

ADS-B Aviation Technology Clustering Machine Learning

Evaluating Economic Opportunities for Product Recycling via the Sherwood Principle and Machine Learning - Supporting Information

10.4231/E80W-7941

Aihua Huang , John W. Sutherland , Sidi Deng , Xiaoyu Zhou , Yuehwern Yih

09/03/2020

This repository contains the supporting information for the manuscript regarding Sherwood principle and Machine learning. All critical underlying data files, along with a flow chart that describes the methodologies applied in the paper are enclosed.

Circular Economy Empirical Models Environmental and Ecological Engineering Machine Learning Sherwood Principle

Modeling the Sea Level Changes in Guam

10.4231/0A0F-7A84

Avnika Manaktala

10/14/2019

This project works on understanding the different statistical models that are available to analyze and predict mean sea level changes in Guam.

Climate Change Data Education Earth and Atmospheric Sciences FAIR Data Machine Learning Sea level rise Statistical analysis Statistical Methods

Structural attributes derived from Google Street View imagery, Louisiana coastal zone

10.4231/1TZX-BR43

David R Johnson , Ed Delp , Fu-Chen Chen , Mohammad Jahanshahi

07/28/2020

Structural attributes relevant to flood risk (foundation height/type, square footage, number of stories, building type), produced using machine learning for automated image analysis of GSV images.

flood risk Industrial Engineering Machine Learning structure attributes

Data of global wetland methane emissions from artificial neural network modeling v1.0

10.4231/3YX4-EY30

Licheng Liu , Qianlai Zhuang

01/21/2020

Source code of an ANN model, site level data, input data, output data and visualization results, which are presented in the manuscript "Inventorying Global Wetland Methane Emissions Based on In Situ Data and an Artificial Neural Network...

Artificial Neural Network (ANN) Atmospheric Chemistry Modeling Biogeochemistry Earth and Atmospheric Sciences Interactive Data Language (IDL) Machine Learning Matlab Methane Dynamics Model (MDM) Methane Emission

Datasets

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Total is 18 Results