We provide a large image parameter dataset extracted from the Solar Dynamics Observatory (SDO) mission's AIA instrument, for the period of January 2011 through the current date, with the cadence of six minutes, for nine wavelength channels. The volume of the dataset for each year is just short of 1 TiB. Towards achieving better results in the region classification of active regions and coronal holes, we improve upon the performance of a set of ten image parameters, through an in depth evaluation of various assumptions that are necessary for calculation of these image parameters. Then, where possible, a method for finding an appropriate settings for the parameter calculations was devised, as well as a validation task to show our improved results. In addition, we include comparisons of JP2 and FITS image formats using supervised classification models, by tuning the parameters specific to the format of the images from which they are extracted, and specific to each wavelength. The results of these comparisons show that utilizing JP2 images, which are significantly smaller files, is not detrimental to the region classification task that these parameters were originally intended for. Finally, we compute the tuned parameters on the AIA images and provide a public API (http://dmlab.cs.gsu.edu/dmlabapi) to access the dataset. This dataset can be used in a range of studies on AIA images, such as content-based image retrieval or tracking of solar events, where dimensionality reduction on the images is necessary for feasibility of the tasks.
A Machine-Learning-Ready Dataset Prepared from the Solar and Heliospheric Observatory Mission
Center for Mathematics and Computer Science（数学与计算机科学中心）；W.W. Hansen Experimental Physics Laboratory, Stanford University（斯坦福大学W.W.汉森实验物理实验室）；US Naval Research Laboratory（美国海军研究实验室）；CIRES, University of Colorado, Boulder（科罗拉多大学博尔德分校CIRES）；NOAA, Space Weather Prediction Center（美国国家海洋和大气管理局空间天气预测中心）
A Machine Learning Dataset Prepared From the NASA Solar Dynamics Observatory Mission
New York University（纽约大学）；University of Michigan（密歇根大学）；Lockheed Martin Solar & Astrophysics Laboratory（洛克希德·马丁太阳与天体物理实验室）；SETI Institute（搜寻地外文明研究所）；University of Oxford（牛津大学）；Southwest Research Institute（西南研究院）；Stanford University（斯坦福大学）；University of Glasgow（格拉斯哥大学）；NASA Goddard Space Flight Center（美国国家航空航天局戈达德太空飞行中心）；University of Amsterdam（阿姆斯特丹大学）
Closeby Habitable Exoplanet Survey (CHES). IV. Synergy between astrometry and direct imaging missions of the Habitable World Observatory for detecting Earth-like planets
HWO Target Stars and Systems: A Prioritized Community List of Potential Stellar Targets for the Habitable Worlds Observatory's ExoEarth Survey
The University of Arizona（亚利桑那大学）；NASA Goddard Space Flight Center（NASA戈达德太空飞行中心）；University of California, Berkeley（加州大学伯克利分校）；California Institute of Technology（加州理工学院）；Mississippi State University（密西西比州立大学）；Louisiana State University（路易斯安那州立大学）；NASA Ames Research Center（NASA艾姆斯研究中心）；NASA Exoplanet Science Institute-Caltech/IPAC（NASA系外行星科学研究所-加州理工/IPAC）；Cornell University（康奈尔大学）
Spectral Analyses Of The Hottest White Dwarfs: Grids Of Spectral Energy Distributions For Extremely Hot, Compact Stars In The Framework Of The Virtual Observatory
