Connecting the Dots: A Machine Learning Ready Dataset for Ionospheric Forecasting Models
Wolniewicz, Linnea; Kelebek, Halil; Mestici, Simone; Vergalla, Michael; Acciarini, Giacomo; Poduval, Bala; Verkhoglyadova, Olga; +4 Authors
Wolniewicz, Linnea; Kelebek, Halil; Mestici, Simone; Vergalla, Michael; Acciarini, Giacomo; Poduval, Bala; Verkhoglyadova, Olga; guhathakurta, madhulika; Berger, Thomas; Baydin, Atılım Güneş; Soboczenski, Frank;
Connecting the Dots: A Machine Learning Ready Dataset for Ionospheric Forecasting Models
Dataset to support the work "Connecting the Dots: A Machine Learning Ready Dataset for Ionospheric Forecasting Models" Description can be found in the README.md file of the dataset. To access the dataset, go here: https://gofile.me/7O2fB/3rDfVCOAI (soon to be publicly accessible). The uploaded dataset here only represents the directory structure of the true dataset.
- Sapienza University of Rome Italy
- Jet Propulsion Lab United States
- European Space Agency France
- University of Oxford United Kingdom
- National Aeronautics and Space Administration United States
Machine Learning, Ionosphere
Machine Learning, Ionosphere
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selected citations
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These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
Citations provided by BIP!popularityPopularity provided by BIP!
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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