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