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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."
dataset
posted on 2018-07-17, 15:01 authored by Ryan McGranaghanRyan McGranaghan, ryan.mcgranaghan@colorado.edu, https://orcid.org/0000-0002-9605-0007, Anthony Mannucci, http://orcid.org/0000-0003-2391-8490, Chris MattmannChris Mattmann, https://orcid.org/0000-0001-7086-3889, Brian Wilson, Richard ChadwickThese 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.
