12 Works

2011 Machine Learning Data Set for NASA's Solar Dynamics Observatory - Atmospheric Imaging Assembly

David Fouhey, Meng Jin, Mark Cheung, Abndres Munoz-Jaramillo, Richard Galvez, Rajat Thomas, Paul Wright, Alexander Szenicer, Monica G. Bobra, Yang Liu & James Mason
We present a curated dataset from the NASA Solar Dynamics Observatory (SDO) mission in a format suitable for machine learning research. Beginning from level 1 scientific products we have processed various instrumental corrections, downsampled to manageable spatial and temporal resolutions, and synchronized observations spatially and temporally. We anticipate this curated dataset will facilitate machine learning research in heliophysics and the physical sciences generally, increasing the scientific return of the SDO mission. This work is a...

2013 Machine Learning Data Set for NASA's Solar Dynamics Observatory - Atmospheric Imaging Assembly

David Fouhey, Meng Jin, Mark Cheung, Abndres Munoz-Jaramillo, Richard Galvez, Rajat Thomas, Paul Wright, Alexander Szenicer, Monica G. Bobra, Yang Liu & James Mason
We present a curated dataset from the NASA Solar Dynamics Observatory (SDO) mission in a format suitable for machine learning research. Beginning from level 1 scientific products we have processed various instrumental corrections, downsampled to manageable spatial and temporal resolutions, and synchronized observations spatially and temporally. We anticipate this curated dataset will facilitate machine learning research in heliophysics and the physical sciences generally, increasing the scientific return of the SDO mission. This work is a...

2012 Machine Learning Data Set for NASA's Solar Dynamics Observatory - Atmospheric Imaging Assembly

David Fouhey, Meng Jin, Mark Cheung, Abndres Munoz-Jaramillo, Richard Galvez, Rajat Thomas, Paul Wright, Alexander Szenicer, Monica G. Bobra, Yang Liu & James Mason
We present a curated dataset from the NASA Solar Dynamics Observatory (SDO) mission in a format suitable for machine learning research. Beginning from level 1 scientific products we have processed various instrumental corrections, downsampled to manageable spatial and temporal resolutions, and synchronized observations spatially and temporally. We anticipate this curated dataset will facilitate machine learning research in heliophysics and the physical sciences generally, increasing the scientific return of the SDO mission. This work is a...

2017 Machine Learning Data Set for NASA's Solar Dynamics Observatory - Atmospheric Imaging Assembly

David Fouhey, Meng Jin, Mark Cheung, Abndres Munoz-Jaramillo, Richard Galvez, Rajat Thomas, Paul Wright, Alexander Szenicer, Monica G. Bobra, Yang Liu & James Mason
We present a curated dataset from the NASA Solar Dynamics Observatory (SDO) mission in a format suitable for machine learning research. Beginning from level 1 scientific products we have processed various instrumental corrections, downsampled to manageable spatial and temporal resolutions, and synchronized observations spatially and temporally. We anticipate this curated dataset will facilitate machine learning research in heliophysics and the physical sciences generally, increasing the scientific return of the SDO mission. This work is a...

2018 Machine Learning Data Set for NASA's Solar Dynamics Observatory - Atmospheric Imaging Assembly

David Fouhey, Meng Jin, Mark Cheung, Abndres Munoz-Jaramillo, Richard Galvez, Rajat Thomas, Paul Wright, Alexander Szenicer, Monica G. Bobra, Yang Liu & James Mason
We present a curated dataset from the NASA Solar Dynamics Observatory (SDO) mission in a format suitable for machine learning research. Beginning from level 1 scientific products we have processed various instrumental corrections, downsampled to manageable spatial and temporal resolutions, and synchronized observations spatially and temporally. We anticipate this curated dataset will facilitate machine learning research in heliophysics and the physical sciences generally, increasing the scientific return of the SDO mission. This work is a...

Ensemble model output of North American atmospheric CO2 simulation (full WRF-chem output)

S. Feng, T. Lauvaux, K.J. Davis, K. Keller, R. Rayner, T. Oda, K. Gurney, Y. Zhou, C. Williams, A.E. Schuh, J. Liu & I. Baker
The uncertainty in biospheric carbon dioxide (CO2) flux estimates drives divergent projections of future climate and uncertainty in prescriptions for climate mitigation. The terrestrial carbon sink can be inferred from atmospheric CO2 observations with transport models via inversion methods. Regional CO2 flux estimates remain uncertain due to the mixture of uncertainties caused by transport models, prior estimates of biospheric fluxes, large-scale CO2 boundary inflow, the assumptions in the inversion process, and the limited density of...

2014 Machine Learning Data Set for NASA's Solar Dynamics Observatory - Atmospheric Imaging Assembly

David Fouhey, Meng Jin, Mark Cheung, Abndres Munoz-Jaramillo, Richard Galvez, Rajat Thomas, Paul Wright, Alexander Szenicer, Monica G. Bobra, Yang Liu & James Mason
We present a curated dataset from the NASA Solar Dynamics Observatory (SDO) mission in a format suitable for machine learning research. Beginning from level 1 scientific products we have processed various instrumental corrections, downsampled to manageable spatial and temporal resolutions, and synchronized observations spatially and temporally. We anticipate this curated dataset will facilitate machine learning research in heliophysics and the physical sciences generally, increasing the scientific return of the SDO mission. This work is a...

Data from: A segmentation algorithm for characterizing Rise and Fall segments in seasonal cycles: an application to XCO2 to estimate benchmarks and assess model bias

Leonardo Calle, Benjamin Poulter & Prabir K. Patra
There is more useful information in the time series of satellite-derived column-averaged carbon dioxide (XCO2) than is typically characterized. Often, the entire time series is treated at once without considering detailed features at shorter timescales, such as nonstationary changes in signal characteristics – amplitude, period and phase. In many instances, signals are visually and analytically differentiable from other portions in a time series. Each rise (increasing) and fall (decreasing) segment in the seasonal cycle is...

2010 Machine Learning Data Set for NASA's Solar Dynamics Observatory - Atmospheric Imaging Assembly

David Fouhey, Meng Jin, Mark Cheung, Abndres Munoz-Jaramillo, Richard Galvez, Rajat Thomas, Paul Wright, Alexander Szenicer, Monica G. Bobra, Yang Liu & James Mason
We present a curated dataset from the NASA Solar Dynamics Observatory (SDO) mission in a format suitable for machine learning research. Beginning from level 1 scientific products we have processed various instrumental corrections, downsampled to manageable spatial and temporal resolutions, and synchronized observations spatially and temporally. We anticipate this curated dataset will facilitate machine learning research in heliophysics and the physical sciences generally, increasing the scientific return of the SDO mission. This work is a...

2015 Machine Learning Data Set for NASA's Solar Dynamics Observatory - Atmospheric Imaging Assembly

David Fouhey, Meng Jin, Mark Cheung, Abndres Munoz-Jaramillo, Richard Galvez, Rajat Thomas, Paul Wright, Alexander Szenicer, Monica G. Bobra, Yang Liu & James Mason
We present a curated dataset from the NASA Solar Dynamics Observatory (SDO) mission in a format suitable for machine learning research. Beginning from level 1 scientific products we have processed various instrumental corrections, downsampled to manageable spatial and temporal resolutions, and synchronized observations spatially and temporally. We anticipate this curated dataset will facilitate machine learning research in heliophysics and the physical sciences generally, increasing the scientific return of the SDO mission. This work is a...

2016 Machine Learning Data Set for NASA's Solar Dynamics Observatory - Atmospheric Imaging Assembly

David Fouhey, Meng Jin, Mark Cheung, Abndres Munoz-Jaramillo, Richard Galvez, Rajat Thomas, Paul Wright, Alexander Szenicer, Monica G. Bobra, Yang Liu & James Mason
We present a curated dataset from the NASA Solar Dynamics Observatory (SDO) mission in a format suitable for machine learning research. Beginning from level 1 scientific products we have processed various instrumental corrections, downsampled to manageable spatial and temporal resolutions, and synchronized observations spatially and temporally. We anticipate this curated dataset will facilitate machine learning research in heliophysics and the physical sciences generally, increasing the scientific return of the SDO mission. This work is a...

Radiolysis of macromolecular organic material in Mars-relevant mineral matrices

A.C. Fox, J.L. Eigenbrode & C. Herrera
The fate of organic material on Mars after deposition is crucial to interpreting the source of these molecules. Previous work has addressed how various organic compounds at millimeter depths in sediments respond to ultraviolet (UV) radiation. In contrast, this study addressed how high-energy particle radiation (200-MeV protons, simulating the effect of galactic cosmic rays at depths of < 4-5 cm) influences organic macromolecules in sediments. Specifically, we report the generation of organic-acid radiolysis products after...

Registration Year

  • 2019
    12

Resource Types

  • Dataset
    12

Affiliations

  • Hansen Experimental Physics Laboratory, Stanford University
    9
  • Electrical Engineering and Computer Science Department, University of Michigan
    9
  • SUPA School of Physics and Astronomy, University of Glasgow
    9
  • NASA Goddard Space Flight Center
    9
  • Center for Data Science, New York University
    9
  • Lockheed Martin Solar & Astrophysics Laboratory
    9
  • SETI Institute
    9
  • Southwest Research Institute
    9
  • Department of Psychiatry, University of Amsterdam
    9
  • University of Oxford, Department of Earth Sciences
    9