2014 Machine Learning Data Set for NASA's Solar Dynamics Observatory - Atmospheric Imaging Assembly
Abstract/Contents
- Abstract
- 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 2014. Data from 2010-2013 and 2015-2018 are also available. See links to related items elsewhere on this page.
Description
Type of resource | software, multimedia |
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Date created | 2018 |
Publication date | 2018 |
Creators/Contributors
Subjects
Subject | NASA |
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Subject | Solar Dynamics Observatory (SDO) |
Subject | Atmospheric Imaging Assembly (AIA) |
Subject | Helioseismic and Magnetic Imager (HMI) |
Subject | Extreme Ultraviolet Variability Experiment (EVE) |
Subject | Heliophysics |
Subject | Astronomy |
Subject | Sun |
Subject | Solar Irradiance |
Subject | Solar Magnetic Field |
Subject | Solar EUV |
Subject | Machine Learning |
Subject | Computer Vision |
Subject | Deep Learning |
Subject | Python |
Genre | Dataset |
Genre | Quantitative data |
Bibliographic information
Access conditions
- Use and reproduction
- User agrees that, where applicable, content will not be used to identify or to otherwise infringe the privacy or confidentiality rights of individuals. Content distributed via the Stanford Digital Repository may be subject to additional license and use restrictions applied by the depositor.
- License
- This work is licensed under a Creative Commons Attribution 3.0 Unported license (CC BY).
Preferred citation
- Preferred citation
- Fouhey, David and Jin, Meng and Cheung, Mark and Munoz-Jaramillo, Andres and Galvez, Richard and Thomas, Rajat and Wright, Paul and Szenicer, Alexander and Bobra, Monica G. and Liu, Yang and Mason, James. (2018). 2014 Machine Learning Data Set for NASA's Solar Dynamics Observatory - Atmospheric Imaging Assembly. Stanford Digital Repository. Available at: https://purl.stanford.edu/sr325xz9271 https://doi.org/10.25740/3jhw-x180
Collection
Stanford Research Data
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