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

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 2011. Data from 2010 and 2012-2018 are also available. See links to related items elsewhere on this page.

本研究发布一套经精心整理的数据集，源自美国国家航空航天局（National Aeronautics and Space Administration, NASA）太阳动力学观测台（Solar Dynamics Observatory, SDO）任务，其格式适配机器学习研究场景。该数据集以一级科学产品为基础，我们已完成各类仪器校正操作，将数据降采样至可控的空间与时间分辨率，并对观测结果完成空间与时间维度的同步对齐。我们期望这套精选数据集能够推动太阳物理学乃至广义物理科学领域的机器学习研究，提升SDO任务的科学产出价值。本工作为2018年NASA前沿发展实验室（Frontier Development Lab）项目的交付成果。本页面包含2011年的观测数据，2010年及2012年至2018年的数据集亦可供获取。相关资源的链接可参见本页面其他位置。