The dataset viewer is not available for this subset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
~~~~~~~~~~~~~~~~~~~~~~~~~^
StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/hdf5/hdf5.py", line 49, in _split_generators
import h5py
ModuleNotFoundError: No module named 'h5py'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 71, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
~~~~~~~~~~~~~~~~~~~~~~~^
path=dataset,
^^^^^^^^^^^^^
config_name=config,
^^^^^^^^^^^^^^^^^^^
token=hf_token,
^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
path,
...<6 lines>...
**config_kwargs,
)
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Koopman Data Assimilation Dataset
Dataset accompanying the benchmark for Data Assimilation (DA) tasks with Koopman-inspired models.
Code repository: github.com/Wenxuan52/KoopmanDABench (Currently private, will release codebase when paper finished)
What Is Hosted Where
The project is split across two locations:
- GitHub repo — source code, plus a few small files under
data/(e.g. data-generation scripts, small reference files, configs). - This Hugging Face dataset — the large pre-computed
.npy/.h5files that are too big to track in Git.
On a new machine, cloning the GitHub repo and then downloading this dataset into the same data/ folder gives you the complete setup. The two sets of files do not overlap.
Final data/ Layout
After both git clone and hf download, the data/ folder should look like:
data/
├── kol_generate_data/ # from GitHub
├── ERA5_High/
│ ├── process_weatherbench.py # from GitHub
│ ├── test.py # from GitHub
│ └── raw_data/ # from Hugging Face
├── ERA5/ # from Hugging Face
├── dam/ # from Hugging Face
├── cylinder/ # from Hugging Face
└── kol/ # from Hugging Face
Quick Start on a New Machine
1. Clone the code from GitHub
git clone https://github.com/Wenxuan52/KoopmanDA.git
cd KoopmanDA
This already populates data/ with the small files tracked in Git (e.g. generation scripts).
2. Set up the Python environment
conda create -n koopmanda python=3.10 -y
conda activate koopmanda
pip install -r requirements.txt # or: pip install -e .
pip install -U huggingface_hub
3. Pull the large data files from Hugging Face into data/
Download into the existing data/ folder — files from GitHub are not affected:
hf download BruceYuan/KoopmanDA \
--repo-type dataset \
--local-dir ./data
Equivalent Python call:
from huggingface_hub import snapshot_download
snapshot_download(
repo_id="BruceYuan/KoopmanDA",
repo_type="dataset",
local_dir="./data",
)
The project should now run exactly as it did on the original machine.
License
Released under the MIT License.
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