Datasets:

Emulated-Inc
/

lintrack

Error code:   StreamingRowsError
Exception:    TypeError
Message:      Couldn't cast array of type
struct<geff: struct<geff_version: string, directed: bool, axes: list<item: struct<name: string, type: string, unit: null, min: double, max: double, scale: double, scaled_unit: null, offset: null>>, node_props_metadata: struct<t: struct<identifier: string, dtype: string, varlength: bool, unit: null, name: null, description: null>, z: struct<identifier: string, dtype: string, varlength: bool, unit: null, name: null, description: null>, y: struct<identifier: string, dtype: string, varlength: bool, unit: null, name: null, description: null>, x: struct<identifier: string, dtype: string, varlength: bool, unit: null, name: null, description: null>>, edge_props_metadata: struct<>, extra: struct<estimated_number_of_nodes: double>>>
to
{}
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 2312, in cast_table_to_schema
                  cast_array_to_feature(
                  ~~~~~~~~~~~~~~~~~~~~~^
                      table[name] if name in table_column_names else pa.array([None] * len(table), type=schema.field(name).type),
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                      feature,
                      ^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1861, in wrapper
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                                           ~~~~^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2158, in cast_array_to_feature
                  raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")
              TypeError: Couldn't cast array of type
              struct<geff: struct<geff_version: string, directed: bool, axes: list<item: struct<name: string, type: string, unit: null, min: double, max: double, scale: double, scaled_unit: null, offset: null>>, node_props_metadata: struct<t: struct<identifier: string, dtype: string, varlength: bool, unit: null, name: null, description: null>, z: struct<identifier: string, dtype: string, varlength: bool, unit: null, name: null, description: null>, y: struct<identifier: string, dtype: string, varlength: bool, unit: null, name: null, description: null>, x: struct<identifier: string, dtype: string, varlength: bool, unit: null, name: null, description: null>>, edge_props_metadata: struct<>, extra: struct<estimated_number_of_nodes: double>>>
              to
              {}

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Zebrafish Cell Tracking During Development

Detect and track cells through 3D space and time in time-lapse fluorescence microscopy of a developing specimen. Image volumes are Zarr v3 stores of shape (T, Z, Y, X), typically (100, 64, 256, 256) uint16, chunked one timepoint per chunk. Training samples pair each volume with a .geff ground-truth tracking graph; test samples are volumes only, and come from a different specimen than the training samples.

The ground-truth annotations are sparse: not every cell in every frame is labelled. Each .geff records an estimated_number_of_nodes in its metadata giving the estimated true cell count for that sample, which is what makes the sparse labels usable as a denominator despite being incomplete.

The data tree lives under data/, exactly as the benchmark environment presents it at /app/lintrack. Its own description is at data/README.md.

5254 files, 18407017775 bytes (17.1 GiB)
structure hash (sha256 over sorted path\tsize lines): 46916e74b8a676e1a2b85e07c4bfffbbf250e4011742a75dc279373f9637a56d
data.manifest.tsv at the repo root lists every file as sha256 size path

Use

from huggingface_hub import snapshot_download

snapshot_download("Emulated-Inc/lintrack", repo_type="dataset", revision="REVISION",
                  local_dir="./lintrack", allow_patterns="data/*")

Pin revision to a commit sha rather than a branch if you need reproducibility.

Provenance and licence

Derived from the CZI Biohub Cell Tracking During Development Kaggle competition (https://www.kaggle.com/competitions/biohub-cell-tracking-during-development), whose data is released under CC0 1.0 (public domain). The public and private evaluation split used by the benchmark this dataset serves is carved from that competition's open training set; no held-out competition data is included here.

Downloads last month: 30

Total file size:

18.4 GB

Zebrafish Cell Tracking During Development

Contents

Use

Provenance and licence