A Machine Learning Dataset Prepared From the NASA Solar Dynamics Observatory Mission

@article{Galvez2019AML,
  title={A Machine Learning Dataset Prepared From the NASA Solar Dynamics Observatory Mission},
  author={Richard Galvez and David F. Fouhey and Meng Jin and Alexandre Szenicer and Andr{\'e}s Mu{\~n}oz-Jaramillo and Mark C. M. Cheung and Paul J. Wright and Monica G. Bobra and Yang Liu and James Mason and Rajat Mani Thomas},
  journal={ArXiv},
  year={2019},
  volume={abs/1903.04538}
}
In this paper 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 illustrate the use of this dataset with two example applications: forecasting future EVE irradiance from present EVE irradiance and… 

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