Replication Data for: Plasma Density Estimation from Ionograms and Geophysical Parameters with Deep Learning

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Sartipzadeh, Kian; Kvammen, Andreas; Gustavsson, Björn; Gulbrandsen, Njål; Johnsen, Magnar G.; Huyghebaert, Devin; Vierinen, Juha, 2026, "Replication Data for: Plasma Density Estimation from Ionograms and Geophysical Parameters with Deep Learning", https://doi.org/10.18710/CFSVA2, DataverseNO, V1
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Description

This dataset contains the replication data and code for the article "Plasma Density Estimation from Ionograms and Geophysical Parameters with Deep Learning". The project introduces "Kian-Net", a deep learning model designed to estimate electron density profiles in the ionosphere by fusing ionogram images with geophysical parameters.

The Kian-Net model utilizes a fusion architecture (FuDMLP) combining an IonoCNN (Convolutional Neural Network) for processing ionogram images and a GeoDMLP (Deep Multilayer Perceptron) for processing geophysical parameters. These two branches are fused to predict plasma density profiles. The model is trained using plasma density profiles observed by the EISCAT UHF radar as ground truth.

The dataset is organized into modules:
1. Training_KIAN_Net/: Scripts and source data for training the model.
2. Testing_KIAN_Net/: Scripts for evaluating the model on independent test days, including pre-trained weights.
3. Predicting_KIAN_Net/: Scripts for generating predictions on new data.
4. Plotting/: Scripts and data for generating the figures presented in the publication.

Data ranges from 2012 to 2022 and includes magnetometer data and ionograms from the Tromsø Geophysical Observatory, geophysical data from OMNIWeb, and EISCAT UHF radar data.

(2025-12-22)
Subject Computer and Information Science; Physics
Keyword Plasma Density Estimation, Deep Learning, Ionogram, High-latitude
Related Publication Is Supplement To: Sartipzadeh, K., Kvammen, A., Gustavsson, B., Gulbrandsen, N., Johnsen, M. G., Huyghebaert, D., & Vierinen, J. (2025). Plasma Density Estimation from Ionograms and Geophysical Parameters with Deep Learning. EGUsphere, 2025, 1-32. [Preprint]url: 10.5194/egusphere-2025-3070
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00_ReadMe.txt
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Published Jan 23, 2026
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MD5: ff253e35d0702e06d694cc74980ecca0
The primary documentation file containing project metadata, detailed file lists, methodological descriptions, and terms of use for the dataset.
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Plotting.zip
ZIP Archive - 7.0 MB
Published Jan 23, 2026
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MD5: 2f2ddb3430f67244397a14ceb4551171
An archive containing the scripts and intermediate data files used to generate the figures and plots presented in the associated publication.
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Predicting_KIAN_Net.zip
ZIP Archive - 981.6 MB
Published Jan 23, 2026
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MD5: 2ebce6f6d2e78dc1b050fb8404338647
An archive containing scripts and data for running predictions on new, unseen data using the trained Kian-Net model.
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requirements.txt
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Published Jan 23, 2026
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MD5: 0e6c59368895da0e07f61a9e209250d6
A text file listing all Python libraries and specific versions required to run the provided code and reproduce the results.
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Testing_KIAN_Net.zip
ZIP Archive - 493.0 MB
Published Jan 23, 2026
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MD5: db99a617fcf4dc6b2e531b1d9ddb90db
An archive containing the trained model weights, model architecture definitions, and the test datasets used for evaluating model performance.
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Training_KIAN_Net.zip
ZIP Archive - 14.4 MB
Published Jan 23, 2026
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MD5: 22e020c3eb511edc88ba00fb870df1ac
An archive containing the source code (train_kian_net.py) and training datasets (EISCAT, Geophysical, Ionograms) used to train the Kian-Net model.
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