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---
license: cc-by-4.0
tags:
- space
- plasma
- physics
size_categories:
- 100K<n<1M
pretty_name: Vlasiator Dataset for Machine Learning Studies
citation: |
@misc{vlasiator2025mldata,
title = {Vlasiator Dataset for Machine Learning Studies},
author = {Zaitsev, Ivan and Holmberg, Daniel and Alho, Markku and Bouri, Ioanna and
Franssila, Fanni and Jeong, Haewon and Palmroth, Minna and Roos, Teemu},
year = {2025},
publisher = {Hugging Face},
url = {https://huggingface.co/datasets/deinal/spacecast-data},
doi = {10.57967/hf/7027},
}
---
# Vlasiator Dataset for Machine Learning Studies
The data is stored in [Zarr](https://zarr.dev).
It can be downloaded to a local `data` directory with:
```
from huggingface_hub import snapshot_download
snapshot_download(
repo_id="deinal/spacecast-data",
repo_type="dataset",
local_dir="data"
)
```
This will yield a local `data` folder that can be used with [spacecast](https://github.com/fmihpc/spacecast):
```
data/
├── graph/ - Directory containing graphs for training
├── run_1.zarr/ - Vlasiator run 1 with ρ = 0.5 cm⁻³ solar wind
├── run_2.zarr/ - Vlasiator run 2 with ρ = 1.0 cm⁻³ solar wind
├── run_3.zarr/ - Vlasiator run 3 with ρ = 1.5 cm⁻³ solar wind
├── run_4.zarr/ - Vlasiator run 4 with ρ = 2.0 cm⁻³ solar wind
├── static.zarr/ - Static features x, z, r coordinates
├── vlasiator_config.yaml - Configuration file for neural-lam
├── vlasiator_run_1.yaml - Configuration file for datastore 1, referred to from vlasiator_config.yaml
├── vlasiator_run_2.yaml - Configuration file for datastore 2, referred to from vlasiator_config.yaml
├── vlasiator_run_3.yaml - Configuration file for datastore 3, referred to from vlasiator_config.yaml
└── vlasiator_run_4.yaml - Configuration file for datastore 4, referred to from vlasiator_config.yaml
```
Preprocess the runs with [mllam-data-prep](https://github.com/mllam/mllam-data-prep), run:
```
mllam_data_prep data/vlasiator_run_1.yaml
mllam_data_prep data/vlasiator_run_2.yaml
mllam_data_prep data/vlasiator_run_3.yaml
mllam_data_prep data/vlasiator_run_4.yaml
```
This produces training-ready Zarr stores in the data directory.
Simple, multiscale, and hierarchical graphs are included already, but can be created using the following commands:
```
python -m neural_lam.create_graph --config_path data/vlasiator_config.yaml --name simple --levels 1 --coarsen-factor 5 --plot
python -m neural_lam.create_graph --config_path data/vlasiator_config.yaml --name multiscale --levels 3 --coarsen-factor 5 --plot
python -m neural_lam.create_graph --config_path data/vlasiator_config.yaml --name hierarchical --levels 3 --coarsen-factor 5 --hierarchical --plot
```
## Citation
```
@misc{vlasiator2025mldata,
title = {Vlasiator Dataset for Machine Learning Studies},
author = {Zaitsev, Ivan and Holmberg, Daniel and Alho, Markku and Bouri, Ioanna and
Franssila, Fanni and Jeong, Haewon and Palmroth, Minna and Roos, Teemu},
year = {2025},
publisher = {Hugging Face},
url = {https://huggingface.co/datasets/deinal/spacecast-data},
doi = {10.57967/hf/7027},
}
``` |