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
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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