Work Description

Title: Dataset for "Energetic Electron Flux Predictions in the near-Earth Plasma Sheet from Solar Wind Driving" Open Access Deposited

h
Attribute Value
Methodology
  • The data were collected from public data sources at NASA Space Physics Data Facility ( https://cdaweb.gsfc.nasa.gov) and the Laboratory for Atmospheric and Space Physics Interactive Solar Irradiance Data Center ( https://lasp.colorado.edu/lisird). The data presented in this repository are those that have been processed to be used as inputs to a neural network model. The output data from that model and the model assessment metrics are also included in this repository.

  • The methodology for how the data were processed from their original sources and used in training a predictive neural network is found in a Jupyter Notebook titled "model_usage_example.ipynb". Full source code is publicly available (under a GNU General Public License) at the GitHub repository:  https://github.com/briswi/ESWPSNN.
Description
  • The data included are those that were used in the creation of a model described in the manuscript titled "Predictions of Electron Flux in the near-Earth Plasma Sheet from Solar Wind Driving" by Swiger et al., 2022, published in the Space Weather Journal. doi: pending, TBD

  • The manuscript describes the development and assessment of a model that predicts electron flux (from 83 eV to 93 keV energies) in a region of Earth's magnetosphere called the plasma sheet. The model uses inputs of solar wind parameters including, but not limited, to solar wind speed and the interplanetary magnetic field.
Creator
Depositor
  • swigerbr@umich.edu
Contact information
Discipline
Funding agency
  • National Aeronautics and Space Administration (NASA)
ORSP grant number
  • AWD016106
Keyword
Citations to related material
  • Swiger, B. M., Liemohn, M. W., Ganushkina, N. Y., & Dubyagin, S. V. (2022). Energetic electron flux predictions in the near-Earth plasma sheet from solar wind driving. Space Weather, 20, e2022SW003150. https://doi.org/10.1029/2022SW003150
Resource type
Curation notes
  • The readme file was updated on Nov 6, 2022 to include a full citation to the article associated with this data set.
Last modified
  • 11/30/2022
Published
  • 09/13/2022
Language
DOI
  • https://doi.org/10.7302/araa-6f62
License
To Cite this Work:
Swiger, B. M., Liemohn, L. W., Ganushkina, N. Y., Dubyagin, S. V. (2022). Dataset for "Energetic Electron Flux Predictions in the near-Earth Plasma Sheet from Solar Wind Driving" [Data set], University of Michigan - Deep Blue Data. https://doi.org/10.7302/araa-6f62

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Files (Count: 7; Size: 1.38 GB)

Thumbnailthumbnail-column Title Original Upload Last Modified File Size Access Actions
README.txt 2022-11-06 2022-11-06 3.06 KB Open Access
model_usage_example.ipynb 2022-09-06 2022-09-06 102 KB Open Access
ModelComparison.zip 2022-09-06 2022-09-06 4.02 MB Open Access
Predictions.zip 2022-09-06 2022-09-06 172 MB Open Access
SavedModel.zip 2022-09-06 2022-09-06 2.79 MB Open Access
Stats.zip 2022-09-06 2022-09-06 21.6 MB Open Access
Training.zip 2022-09-06 2022-09-07 1.18 GB Open Access

Title
-----
Data for "Energetic Electron Flux Predictions in the near-Earth Plasma Sheet from Solar Wind Driving"

Authors
-------
Swiger, Brian
Liemohn, Mike
Ganushkina, Natalia
Dubyagin, Stepan

Contact
-------
For questions about these data, please contact via email:
Brian Swiger
swigerbr@umich.edu

Data Usage
----------
The data included are those that were used in the creation of a model described in the manuscript titled "Energetic Electron Flux Predictions in the near-Earth Plasma Sheet from Solar Wind Driving" by Swiger et al., 2022, published in the Space Weather Journal.

The full citation to this article is: Swiger, B. M., Liemohn, M. W., Ganushkina, N. Y., & Dubyagin, S. V. (2022). Energetic electron flux predictions in the near-Earth plasma sheet from solar wind driving. Space Weather, 20, e2022SW003150. https://doi.org/10.1029/2022SW003150

The manuscript describes the development and assessment of a model that predicts
electron flux (from 83 eV to 93 keV energies) in a region of Earth's magnetosphere called the plasma sheet. The model uses inputs of solar wind parameters including, but not limited, to solar wind speed and the interplanetary magnetic field.

Data Original Sources
---------------------
Data are origially from sources at:
--NASA's Space Physics Data Facility (https://cdaweb.gsfc.nasa.gov)
--LASP LISIRD (https://lasp.colorado.edu/lisird)

Data License
------------
The data contained in this repository are under a
Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0)

Data Methodology
----------------
The methodology for how the data were processed from their original sources and used in training a predictive neural network is found in a Jupyter Notebook,
titled "model_usage_example.ipynb". Full source code is publicly available (under a GNU General Public License) at the GitHub repository:
https://github.com/briswi/ESWPSNN

Data Organization
-----------------
The data are organized in the following folders.
-Training: contains the data that was used to train the model.
-Stats: the assessment of the model, all of the calculated metrics.
-SavedModel: the model's coefficients and weights, saved and opened with Keras/Tensorflow (see https://keras.io and https://www.tensorflow.org)
-Preditions: the model outputs
-ModelComparison: data used in comparison with an existing model of plasma
sheet electron flux (called Dubyagin2016 in the accompanying manuscript).

Data Formats
------------
The data files are saved as either python pickle (.pkl), HDF5 (.h5), or comma
separated values (.csv) format.
Python pickle files can be read with python's built-in pickle module (see
https://docs.python.org/3/library/pickle.html).

The model is saved in two formats. The first is HDF5 (.h5), and the second is
in TensorFlow save model (TFSM) format. The TFSM (and associated files) should be opened by passing the folder name 'v122_model_tfsm' to the appropriate TensorFlow reader. The files inside of this folder are not intended to be accessed directly. Please see
https://www.tensorflow.org/guide/keras/save_and_serialize for detailed usage.

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