2012 Machine Learning Data Set for NASA's Solar Dynamics Observatory - Atmospheric Imaging Assembly
Fouhey, David
David
Fouhey
https://orcid.org/0000-0001-5028-5161
Electrical Engineering and Computer Science Department, University of Michigan
Jin, Meng
Meng
Jin
https://orcid.org/0000-0002-9672-3873
SETI Institute
Lockheed Martin Solar & Astrophysics Laboratory
Cheung, Mark
Mark
Cheung
https://orcid.org/0000-0003-2110-9753
Lockheed Martin Solar & Astrophysics Laboratory
Hansen Experimental Physics Laboratory, Stanford University
Munoz-Jaramillo, Abndres
Abndres
Munoz-Jaramillo
https://orcid.org/0000-0002-4716-0840
Southwest Research Institute
Galvez, Richard
Richard
Galvez
https://orcid.org/0000-0002-4780-9566
Center for Data Science, New York University
Thomas, Rajat
Rajat
Thomas
https://orcid.org/0000-0002-5362-4816
Department of Psychiatry, University of Amsterdam
Wright, Paul
Paul
Wright
https://orcid.org/0000-0001-9021-611X
SUPA School of Physics and Astronomy, University of Glasgow
Szenicer, Alexander
Alexander
Szenicer
https://orcid.org/0000-0002-4829-5739
University of Oxford, Department of Earth Sciences
Bobra, Monica G.
Monica G.
Bobra
https://orcid.org/0000-0002-5662-9604
Hansen Experimental Physics Laboratory, Stanford University
Liu, Yang
Yang
Liu
https://orcid.org/0000-0002-0671-689X
Hansen Experimental Physics Laboratory, Stanford University
Mason, James
James
Mason
https://orcid.org/0000-0002-3783-5509
NASA Goddard Space Flight Center
Lockheed Martin
IBM
NASA Solar Dynamics Observatory/Atmospheric Imaging Assembly
NNG04EA00C
NASA Frontier Development Lab
NNX14AT27A
Quantitative Data
Stanford Digital Repository
2018
2018
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 deliverable of the 2018 NASA Frontier Development Lab program. This page includes data from 2012. Data from 2010, 2011, and 2013-2018 are also available. See links to related items elsewhere on this page.
NASA
Solar Dynamics Observatory (SDO)
Atmospheric Imaging Assembly (AIA)
Helioseismic and Magnetic Imager (HMI)
Extreme Ultraviolet Variability Experiment (EVE)
Heliophysics
Astronomy
Sun
Solar Irradiance
Solar Magnetic Field
Solar EUV
Machine Learning
Computer Vision
Deep Learning
Python
10.25740/1VYZ-B592
https://purl.stanford.edu/dc156hp0190
https://doi.org/10.25740/ppax-bf07
https://doi.org/10.25740/sb4q-wj06
https://doi.org/10.25740/2zme-3q44
https://doi.org/10.25740/3jhw-x180
https://doi.org/10.25740/0fbp-re41
https://doi.org/10.25740/64cr-bc95
https://doi.org/10.25740/c8bw-ar96
https://doi.org/10.25740/pknx-5s37
Creative Commons Attribution (CC-BY)