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/webdataset/webdataset.py", line 81, in _split_generators
first_examples = list(islice(pipeline, self.NUM_EXAMPLES_FOR_FEATURES_INFERENCE))
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 32, in _get_pipeline_from_tar
fs: fsspec.AbstractFileSystem = fsspec.filesystem("memory")
~~~~~~~~~~~~~~~~~^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/fsspec/registry.py", line 302, in filesystem
cls = get_filesystem_class(protocol)
File "/usr/local/lib/python3.14/site-packages/fsspec/registry.py", line 239, in get_filesystem_class
raise ValueError(f"Protocol not known: {protocol}")
ValueError: Protocol not known: memory
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.
State Dataset
Dataset Description
State_dataset is a collection of datasets used for State single-cell expression modeling and perturbation prediction tasks. It comprises four data categories: Parse, Tahoe, Replogle-Nadig, and SE-167M-Human. The primary data is in AnnData/H5AD format, accompanied by gene embeddings (PyTorch .pt), dataset split configurations (TOML), and upstream license files.
Supported Tasks
This repository corresponds to the experiment configurations in the State directory:
ST-HVG-Parse and ST-SE-Parse use Parse data for few-shot/zero-shot splits by cell type or donor; ST-HVG-Tahoe uses Tahoe data for generalization evaluation; Replogle data is used for perturbation validation; and SE-600M/config.yaml describes the organization of large-scale cellxgene/Tahoe training data and gene embeddings.
Data Format and Structure
The following sizes are based on file statistics from the current directory. File sizes may vary between data versions:
| Subset | Main Files | Current File Size |
|---|---|---|
| Parse | parse_concat_full.h5ad |
Approximately 342.3 GiB |
| Replogle-Nadig | 5 .h5ad files |
Approximately 49.3 GiB |
| Tahoe smoke | c36.h5ad, c39.h5ad, c44.h5ad |
Approximately 5.0 GiB |
| SE-167M-Human smoke | 1 .pt file + 4 .h5ad files |
Approximately 607 MiB |
H5AD files can be read with scanpy/anndata, while PT files can be read with PyTorch. The data paths in the configuration files are examples for the runtime environment. After migrating the data to a local environment, update the paths in the State configurations to the actual mount paths.
How to Use the Dataset
Download the dataset:
hf download --dataset OneScience-Group/State_datasets --local-dir ./data
After mounting this directory in the runtime environment, update the data path in the corresponding TOML file to the actual path. For example:
[datasets]
parse = "/path/to/State_dataset/State-Parse-Filtered"
Read an H5AD file:
import anndata as ad
adata = ad.read_h5ad("State-Parse-Filtered/parse_concat_full.h5ad", backed="r")
print(adata)
Sharded Archives
Because the complete directory is approximately 401 GiB, it has been split into multiple Zstandard-compressed shards of 90 GiB (binary) each. The shards are consecutive parts of the same compressed stream and cannot be decompressed independently; they must first be concatenated in order:
cat State_dataset.tar.zst.part-* > State_dataset.tar.zst
zstd -d State_dataset.tar.zst -c | tar -xf -
Alternatively, stream the decompression directly without materializing the merged file:
cat State_dataset.tar.zst.part-* | zstd -d -c | tar -xf -
For shard filenames, actual sizes, and SHA256 checksums, refer to State_dataset.tar.zst.sha256, which was generated in the same directory.
Official OneScience Information
| Platform | OneScience Main Repository | Skills Repository |
|---|---|---|
| Gitee | https://gitee.com/onescience-ai/onescience | https://gitee.com/onescience-ai/oneskills |
| GitHub | https://github.com/onescience-ai/OneScience | https://github.com/onescience-ai/oneskills |
Citation and License
- Parse data source: Parse Biosciences, “Performance of Evercode WT v3 in Human Immune Cells (PBMCs)”; see
State-Parse-Filtered/README.mdandCC-NC-4.0-License.txt. - For Replogle-Nadig, Tahoe, and SE-167M-Human data, comply with the licenses, citation requirements, and usage restrictions of the respective upstream datasets.
- This README only describes the current directory structure and does not alter the copyright or license terms of any upstream data.
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