Machine learning databases used for Journal of Geophysical Research: Space Physics manuscript: "New capabilities for prediction of high-latitude ionospheric scintillation: A novel approach with machine learning."

• Main Authors: McGranaghan, Ryan, [e-mail], Https//Orcid.Org/0000-0002-9605-0007, Mannucci, Anthony, Http//Orcid.Org/0000-0003-2391-8490, Mattmann, Chris, Https//Orcid.Org/0000-0001-7086-3889, Wilson, Brian, Chadwick, Richard
• Format: Dataset
• Language: unknown
• Published: figshare 2018
• Subjects: Plasma Physics; Space Science; 20109 Space and Solar Physics; FOS Physical sciences; 90904 Navigation and Position Fixing; FOS Environmental engineering; 20107 Mesospheric, Ionospheric and Magnetospheric Physics; Arctic; Canadian High Arctic Ionospheric Network
• Online Access: https://dx.doi.org/10.6084/m9.figshare.6813131; https://figshare.com/articles/Machine_learning_databases_used_for_Journal_of_Geophysical_Research_Space_Physics_manuscript_New_capabilities_for_prediction_of_high-latitude_ionospheric_scintillation_A_novel_approach_with_machine_learning_/6813131

These data are described by the Journal of Geophysical Research: Space Physics manuscript: "New capabilities for prediction of high-latitude ionospheric scintillation: A novel approach with machine learning." The file is organized as a comma separated values (.csv) file for ease of use with Python Pandas DataFrames. The data included are for observations from the Canadian High Arctic Ionospheric Network (CHAIN). CHAIN data are combined with solar and geomagnetic activity data to form a 'machine learning database' in which input 'features' are provided at a given time and attached to a 'label' that is the ionospheric phase scintillation at a future time (for prediction). The prediction lead time in these files is one hour. Full details of the input features and predictive task are provided in the paper. Data are provided in two separate files for the years 2015 and 2016.