Dataset Viewer
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
sample_order: list<item: string>
child 0, item: string
epoch_definition: string
num_samples: int64
num_tiles: int64
samples: list<item: struct<sample_id: string, manifest: string, processed_h5: string, num_cells: int64, num_t (... 13 chars omitted)
child 0, item: struct<sample_id: string, manifest: string, processed_h5: string, num_cells: int64, num_tiles: int64 (... 1 chars omitted)
child 0, sample_id: string
child 1, manifest: string
child 2, processed_h5: string
child 3, num_cells: int64
child 4, num_tiles: int64
tiles: list<item: struct<sample_id: string, tile_id: string, path: string, num_nodes: int64, num_core: int6 (... 43 chars omitted)
child 0, item: struct<sample_id: string, tile_id: string, path: string, num_nodes: int64, num_core: int64, num_edge (... 31 chars omitted)
child 0, sample_id: string
child 1, tile_id: string
child 2, path: string
child 3, num_nodes: int64
child 4, num_core: int64
child 5, num_edges: int64
child 6, processed_h5: string
top_k: int64
coverage_max: int64
num_hops: int64
max_nodes: int64
context_coverage_mean: double
core_target: int64
num_cells: int64
graph_path: string
processed_h5: string
coverage_min: int64
sample_id: string
to
{'sample_id': Value('string'), 'processed_h5': Value('string'), 'graph_path': Value('string'), 'top_k': Value('int64'), 'core_target': Value('int64'), 'max_nodes': Value('int64'), 'num_hops': Value('int64'), 'num_cells': Value('int64'), 'num_tiles': Value('int64'), 'coverage_min': Value('int64'), 'coverage_max': Value('int64'), 'context_coverage_mean': Value('float64'), 'tiles': List({'sample_id': Value('string'), 'tile_id': Value('string'), 'path': Value('string'), 'num_nodes': Value('int64'), 'num_core': Value('int64'), 'num_edges': Value('int64')})}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
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 127, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
sample_order: list<item: string>
child 0, item: string
epoch_definition: string
num_samples: int64
num_tiles: int64
samples: list<item: struct<sample_id: string, manifest: string, processed_h5: string, num_cells: int64, num_t (... 13 chars omitted)
child 0, item: struct<sample_id: string, manifest: string, processed_h5: string, num_cells: int64, num_tiles: int64 (... 1 chars omitted)
child 0, sample_id: string
child 1, manifest: string
child 2, processed_h5: string
child 3, num_cells: int64
child 4, num_tiles: int64
tiles: list<item: struct<sample_id: string, tile_id: string, path: string, num_nodes: int64, num_core: int6 (... 43 chars omitted)
child 0, item: struct<sample_id: string, tile_id: string, path: string, num_nodes: int64, num_core: int64, num_edge (... 31 chars omitted)
child 0, sample_id: string
child 1, tile_id: string
child 2, path: string
child 3, num_nodes: int64
child 4, num_core: int64
child 5, num_edges: int64
child 6, processed_h5: string
top_k: int64
coverage_max: int64
num_hops: int64
max_nodes: int64
context_coverage_mean: double
core_target: int64
num_cells: int64
graph_path: string
processed_h5: string
coverage_min: int64
sample_id: string
to
{'sample_id': Value('string'), 'processed_h5': Value('string'), 'graph_path': Value('string'), 'top_k': Value('int64'), 'core_target': Value('int64'), 'max_nodes': Value('int64'), 'num_hops': Value('int64'), 'num_cells': Value('int64'), 'num_tiles': Value('int64'), 'coverage_min': Value('int64'), 'coverage_max': Value('int64'), 'context_coverage_mean': Value('float64'), 'tiles': List({'sample_id': Value('string'), 'tile_id': Value('string'), 'path': Value('string'), 'num_nodes': Value('int64'), 'num_core': Value('int64'), 'num_edges': Value('int64')})}
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.
SAMI
This repository contains processed spatial omics inputs, trained SAMI model checkpoints, experiment configurations, and saved inference results. It supports spatial representation learning with RNA, protein, chromatin accessibility, histone modification, and histology features, depending on the dataset.
Contents
| Directory | Processed inputs | Configurations | Modalities used |
|---|---|---|---|
CRC_VisiumHD |
5 | 6 | RNA + H&E |
GSE263617_A1_lymph_node |
1 | 1 | RNA + protein |
Mouse_Brain_ATAC |
1 | 1 | RNA + ATAC |
Mouse_Brain_H3K27ac |
1 | 1 | RNA + H3K27ac |
Mouse_Brain_H3K27me3 |
1 | 1 | RNA + H3K27me3 |
Mouse_Brain_H3K4me3 |
1 | 1 | RNA + H3K4me3 |
Mouse_Embryonic_Brain |
8 | 8 | RNA + ATAC |
Mouse_Spleen |
1 | 1 | RNA + protein |
Simulation1 |
1 | 1 | RNA + protein |
Simulation2 |
1 | 1 | RNA + protein |
Simulation3 |
1 | 1 | RNA + protein |
Simulation4 |
1 | 1 | RNA + protein |
Simulation5 |
1 | 1 | RNA + protein |
Xenium_Human_Breast_Cancer_Rep1 |
1 | 1 | RNA + H&E |
Xenium_Renal_Carcinoma |
1 | 1 | RNA + protein |
human_breast_cancer |
1 | 1 | RNA + H&E |
human_lymph_node |
1 | 4 | RNA + protein + H&E |
spatialLIBD |
12 | 12 | RNA + H&E |
Download
Install huggingface_hub in your Python environment, then download from the
directory in which you plan to run SAMI:
from huggingface_hub import snapshot_download
snapshot_download(
repo_id="GAO612/SAMI",
repo_type="dataset",
local_dir="sami",
)
For a smaller download, select one subset:
snapshot_download(
repo_id="GAO612/SAMI",
repo_type="dataset",
local_dir="sami",
allow_patterns=["Mouse_Embryonic_Brain/E11_0-S1/**"],
)
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