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 here: Dataset GitHub Data is hosted publicly on AWS S3 (s3://nasa-radiant-data/helioai-datasets/ionosphere-data-public/) — no credentials required. An example notebook loading and working with the data can be found here: Google Colaboratory Notebook Dataset here only represents the directory structure of the dataset.
- University of Oxford United Kingdom
- Jet Propulsion Lab United States
- University of New Hampshire United States
- National Aeronautics and Space Administration United States
- European Space Research and Technology Centre Netherlands
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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