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Dataset . 2022
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2022
License: CC BY
Data sources: ZENODO
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ZENODO
Dataset . 2022
License: CC BY
Data sources: Datacite
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https://doi.org/10.5281/zenodo...
Dataset . 2022
License: CC BY
Data sources: Sygma
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A database of MMS bow shock crossings compiled using machine learning

Authors: Lalti, Ahmad; Khotyaintsev, Yuri V.; Dimmock, Andrew P.; Johlander, Andreas; Graham, Daniel B.; Olshevsky, Vyacheslav;

A database of MMS bow shock crossings compiled using machine learning

Abstract

We use a machine learning approach to automatically identify shock crossings from the Magnetospheric Multiscale (MMS) spacecraft. We compile a database of 2797 crossings including various spacecraft related and shock related parameters for each event. Furthermore, for each event we provide an overview plot containing key parameters of the shock crossing. A Technical report detailing the content of the database can be found at the DOI: http://dx.doi.org/10.1029/2022JA030454

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selected citations
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).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
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