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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
embpy_static_embedding: struct<description: string, entity_type: string, id_harmonization: struct<alias_columns: list<item: (... 527 chars omitted)
child 0, description: string
child 1, entity_type: string
child 2, id_harmonization: struct<alias_columns: list<item: string>, enabled: bool, n_duplicate_canonical_ids: int64, n_unresol (... 111 chars omitted)
child 0, alias_columns: list<item: string>
child 0, item: string
child 1, enabled: bool
child 2, n_duplicate_canonical_ids: int64
child 3, n_unresolved_ids: int64
child 4, organism: string
child 5, source_id_type: string
child 6, target_id_type: string
child 7, unresolved_id_policy: string
child 3, id_key: string
child 4, id_type: string
child 5, index_columns: list<item: string>
child 0, item: string
child 6, matrix_key: string
child 7, model_key: string
child 8, n_dims: int64
child 9, n_entities: int64
child 10, organism: string
child 11, schema_version: string
child 12, source: struct<name: string, path: string, size_bytes: int64, suffix: string>
child 0, name: string
child 1, path: string
child 2, size_bytes: int64
child 3, suffix: string
child 13, source_id_type: string
child 14, storage: string
child 15, target_id_type: string
child 16, values_path: string
n_missing_input_ids: int64
n_nan_input_rows: int64
n_dims: int64
n_nan_input_values: int64
index_columns: list<item: string>
child 0, item: string
uns_path: string
n_nan_input_columns: int64
entity_type: string
values_path: string
package_dir: string
schema_version: string
n_duplicate_input_ids: int64
id_type: string
dtype: string
source: struct<name: string, path: string, size_bytes: int64, suffix: string>
child 0, name: string
child 1, path: string
child 2, size_bytes: int64
child 3, suffix: string
created_at: timestamp[s]
organism: string
index_path: string
n_entities: int64
matrix_key: string
id_key: string
description: string
source_metadata: struct<>
metadata_path: string
source_id_type: string
id_harmonized: bool
target_id_type: string
nan_policy: string
shape: list<item: int64>
child 0, item: int64
id_harmonization: struct<alias_columns: list<item: string>, enabled: bool, n_duplicate_canonical_ids: int64, n_unresol (... 111 chars omitted)
child 0, alias_columns: list<item: string>
child 0, item: string
child 1, enabled: bool
child 2, n_duplicate_canonical_ids: int64
child 3, n_unresolved_ids: int64
child 4, organism: string
child 5, source_id_type: string
child 6, target_id_type: string
child 7, unresolved_id_policy: string
key: string
to
{'created_at': Value('timestamp[s]'), 'description': Value('string'), 'dtype': Value('string'), 'entity_type': Value('string'), 'id_harmonization': {'alias_columns': List(Value('string')), 'enabled': Value('bool'), 'n_duplicate_canonical_ids': Value('int64'), 'n_unresolved_ids': Value('int64'), 'organism': Value('string'), 'source_id_type': Value('string'), 'target_id_type': Value('string'), 'unresolved_id_policy': Value('string')}, 'id_harmonized': Value('bool'), 'id_key': Value('string'), 'id_type': Value('string'), 'index_columns': List(Value('string')), 'index_path': Value('string'), 'key': Value('string'), 'matrix_key': Value('string'), 'metadata_path': Value('string'), 'n_dims': Value('int64'), 'n_duplicate_input_ids': Value('int64'), 'n_entities': Value('int64'), 'n_missing_input_ids': Value('int64'), 'n_nan_input_columns': Value('int64'), 'n_nan_input_rows': Value('int64'), 'n_nan_input_values': Value('int64'), 'nan_policy': Value('string'), 'organism': Value('string'), 'package_dir': Value('string'), 'schema_version': Value('string'), 'shape': List(Value('int64')), 'source': {'name': Value('string'), 'path': Value('string'), 'size_bytes': Value('int64'), 'suffix': Value('string')}, 'source_id_type': Value('string'), 'source_metadata': {}, 'target_id_type': Value('string'), 'uns_path': Value('string'), 'values_path': Value('string')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
return get_rows(
^^^^^^^^^
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2227, in __iter__
for key, pa_table in self._iter_arrow():
^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2251, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 494, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 384, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 299, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 128, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2321, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2249, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
embpy_static_embedding: struct<description: string, entity_type: string, id_harmonization: struct<alias_columns: list<item: (... 527 chars omitted)
child 0, description: string
child 1, entity_type: string
child 2, id_harmonization: struct<alias_columns: list<item: string>, enabled: bool, n_duplicate_canonical_ids: int64, n_unresol (... 111 chars omitted)
child 0, alias_columns: list<item: string>
child 0, item: string
child 1, enabled: bool
child 2, n_duplicate_canonical_ids: int64
child 3, n_unresolved_ids: int64
child 4, organism: string
child 5, source_id_type: string
child 6, target_id_type: string
child 7, unresolved_id_policy: string
child 3, id_key: string
child 4, id_type: string
child 5, index_columns: list<item: string>
child 0, item: string
child 6, matrix_key: string
child 7, model_key: string
child 8, n_dims: int64
child 9, n_entities: int64
child 10, organism: string
child 11, schema_version: string
child 12, source: struct<name: string, path: string, size_bytes: int64, suffix: string>
child 0, name: string
child 1, path: string
child 2, size_bytes: int64
child 3, suffix: string
child 13, source_id_type: string
child 14, storage: string
child 15, target_id_type: string
child 16, values_path: string
n_missing_input_ids: int64
n_nan_input_rows: int64
n_dims: int64
n_nan_input_values: int64
index_columns: list<item: string>
child 0, item: string
uns_path: string
n_nan_input_columns: int64
entity_type: string
values_path: string
package_dir: string
schema_version: string
n_duplicate_input_ids: int64
id_type: string
dtype: string
source: struct<name: string, path: string, size_bytes: int64, suffix: string>
child 0, name: string
child 1, path: string
child 2, size_bytes: int64
child 3, suffix: string
created_at: timestamp[s]
organism: string
index_path: string
n_entities: int64
matrix_key: string
id_key: string
description: string
source_metadata: struct<>
metadata_path: string
source_id_type: string
id_harmonized: bool
target_id_type: string
nan_policy: string
shape: list<item: int64>
child 0, item: int64
id_harmonization: struct<alias_columns: list<item: string>, enabled: bool, n_duplicate_canonical_ids: int64, n_unresol (... 111 chars omitted)
child 0, alias_columns: list<item: string>
child 0, item: string
child 1, enabled: bool
child 2, n_duplicate_canonical_ids: int64
child 3, n_unresolved_ids: int64
child 4, organism: string
child 5, source_id_type: string
child 6, target_id_type: string
child 7, unresolved_id_policy: string
key: string
to
{'created_at': Value('timestamp[s]'), 'description': Value('string'), 'dtype': Value('string'), 'entity_type': Value('string'), 'id_harmonization': {'alias_columns': List(Value('string')), 'enabled': Value('bool'), 'n_duplicate_canonical_ids': Value('int64'), 'n_unresolved_ids': Value('int64'), 'organism': Value('string'), 'source_id_type': Value('string'), 'target_id_type': Value('string'), 'unresolved_id_policy': Value('string')}, 'id_harmonized': Value('bool'), 'id_key': Value('string'), 'id_type': Value('string'), 'index_columns': List(Value('string')), 'index_path': Value('string'), 'key': Value('string'), 'matrix_key': Value('string'), 'metadata_path': Value('string'), 'n_dims': Value('int64'), 'n_duplicate_input_ids': Value('int64'), 'n_entities': Value('int64'), 'n_missing_input_ids': Value('int64'), 'n_nan_input_columns': Value('int64'), 'n_nan_input_rows': Value('int64'), 'n_nan_input_values': Value('int64'), 'nan_policy': Value('string'), 'organism': Value('string'), 'package_dir': Value('string'), 'schema_version': Value('string'), 'shape': List(Value('int64')), 'source': {'name': Value('string'), 'path': Value('string'), 'size_bytes': Value('int64'), 'suffix': Value('string')}, 'source_id_type': Value('string'), 'source_metadata': {}, 'target_id_type': Value('string'), 'uns_path': Value('string'), 'values_path': Value('string')}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Embpy Static Embeddings
This dataset contains static gene and protein embeddings packaged with embpy. Embedding values are stored in Zarr arrays, while row identifiers, provenance, species metadata, and AnnData-like .uns metadata are stored in sidecar metadata files.
Summary
- Repository:
theislab/Embpy_Data - Schema version:
embpy.static_embedding_package.v1 - Generated at:
2026-06-01T15:07:46+00:00 - Number of embeddings:
11 - Total indexed entities:
201,717 - Species keys:
human_9606 - NCBI taxonomy IDs:
9606 - Default gene identifier policy:
ensembl_id - Unresolved gene identifier policy:
drop
Available Embeddings
| key | entity | species | id type | rows | dims | description |
|---|---|---|---|---|---|---|
| crispr_gene_effect | gene | human_9606 | ensembl_id | 17,087 | 1,178 | DepMap CRISPR gene effect matrix, genes as rows after transposition. |
| crispr_gene_effect_1178 | gene | human_9606 | ensembl_id | 17,087 | 1,178 | DepMap CRISPR gene effect embedding, scaled, 1178d. |
| crispr_gene_effect_205 | gene | human_9606 | ensembl_id | 17,916 | 205 | DepMap CRISPR gene effect embedding, scaled, 205d. |
| gene2vec | gene | human_9606 | ensembl_id | 18,795 | 200 | Gene2Vec co-expression embedding, 200d. |
| genept | gene | human_9606 | ensembl_id | 18,807 | 3,072 | GenePT GPT-3.5 text embedding, 3072d, Ensembl-keyed. |
| genept_scaled | gene | human_9606 | ensembl_id | 17,728 | 3,072 | GenePT GPT-3.5 text embedding, z-scored, 3072d. |
| omics | gene | human_9606 | ensembl_id | 19,385 | 256 | Omics 256d static gene embedding, Ensembl-keyed. |
| pops | gene | human_9606 | ensembl_id | 18,383 | 256 | PoPS 256d gene features, Ensembl-keyed. |
| string_functional_9606 | protein | human_9606 | string_protein_id | 19,699 | 512 | STRING/SPACE functional PPI embedding for human proteins, species 9606, 512d. |
| string_node2vec_9606 | protein | human_9606 | string_protein_id | 19,622 | 128 | STRING node2vec PPI embedding for human proteins, species 9606, 128d. |
| wikicrow | gene | human_9606 | ensembl_id | 17,208 | 4,096 | WikiCrow text embedding, scaled, 4096d. |
File Layout
manifest.json
embeddings/<model_key>/
values.zarr/
metadata/
index.parquet
index.csv
metadata.json
uns.json
The dense matrix is stored under values.zarr. The metadata/index.parquet file maps row positions to entity_id values and any preserved aliases such as source IDs or gene symbols.
Loading With embpy
from embpy.pp import HFHandler
hf = HFHandler("theislab/Embpy_Data")
embedding = hf.download_embedding("crispr_gene_effect")
matrix = embedding["embeddings"]
ids = embedding["ids"]
For a local checkout or downloaded snapshot:
from embpy import load_static_embedding_package
store = load_static_embedding_package("/path/to/package", key="crispr_gene_effect")
tp53 = store.get("ENSG00000141510")
tp53_by_symbol = store.get("TP53", id_type="symbol")
Missing identifiers raise by default. Use missing="drop" or missing="nan" when a partial result is acceptable.
Metadata And Species
This card reports species using package-level defaults. Regenerate the package with a current embpy build to also write per-embedding species, taxonomy_id, and species_key fields into metadata/metadata.json.
Validation
The package was designed to be validated locally before upload:
python -m embpy.scripts.package_static_embeddings validate --package /path/to/package
This package intentionally skips 2 source collection(s) containing 2642 file(s). Those collections usually contain per-species artifacts and should be packaged only when a species/taxonomy ID is selected explicitly.
License And Attribution
This repository aggregates embeddings derived from multiple upstream resources. Please check the per-embedding metadata and upstream sources for the applicable licenses and citation terms.
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