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ZENODO
Dataset . 2024
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2024
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2024
License: CC BY
Data sources: Datacite
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Clustering of Global Magnetospheric Observations

Authors: Edmond, James; Raeder, Joachim; Ferdousi, Banafsheh; Argall, Matthew; Innocenti, Maria Elena;

Clustering of Global Magnetospheric Observations

Abstract

The data here are for an article submission to Journal of Geophysical Research: Machine Learning and Computation. It includes: A dataset of THEMIS and MMS magnetic field and ion observations used to train the model (mms_and_themis.csv) The bow shock (bs.csv) and magnetopause (mp.csv) crossings found from the resulting predictions A labeled dataset containing MMS1 observations that is joined with the labels of Olshevsky et al (2021) (labeled_mms1_NovDec2017.csv). Our model's predictions are also included in this dataset. These datasets can be easily loaded using the Python Data Analysis Library, Pandas. The models we created for our article are included in a separate Zenodo repository.

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    popularity
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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!
1
Average
Average
Average