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dream-cubed
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DreamCubedNaturalSample

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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
format_version: int64
is_processed: bool
num_samples: int64
voxel_shape: list<item: int64>
  child 0, item: int64
voxel_dtype: string
paths: struct<voxels: string, biome_labels: string>
  child 0, voxels: string
  child 1, biome_labels: string
voxel_representation: string
embedding_dim: null
embeddings_path: null
class_labels_format: string
num_blocks: int64
num_classes: int64
metadata_applied_during_processing: bool
source: struct<path: string, had_metadata: bool, metadata_applied_at_source: bool>
  child 0, path: string
  child 1, had_metadata: bool
  child 2, metadata_applied_at_source: bool
sample_of: string
sample_source_indices_path: string
sample_selection: struct<strategy: string, per_label_target: int64, selected_source_indices_count: int64, labels: stru (... 683 chars omitted)
  child 0, strategy: string
  child 1, per_label_target: int64
  child 2, selected_source_indices_count: int64
  child 3, labels: struct<0: struct<available: int64, sampled: int64>, 1: struct<available: int64, sampled: int64>, 10: (... 586 chars omitted)
      child 0, 0: struct<available: int64, sampled: int64>
          child 0, available: int64
          child 1, sampled: int64
      child 1, 1: struct<available: int64, sampled: int64>
          child 0, available: int64
          child 1, sampled: int64
      child 2, 10: struct<available: int64, sampled: int64>
          child 0, available: int64
          child 1, sampled: int64
      child 3, 11: struct<available: int64, sampled: int64>
          child 0, available: int64
          child 1, sampled: int64
      child 4, 12: struct<available: int64, sampled: int64>
          child 0, available: int64
          child 1, sampled: int64
      child 5, 13: struct<available: int64, sampled: int64>
          child 0, available: int64
          child 1, sampled: int64
      child 6, 14: struct<available: int64, sampled: int64>
          child 0, available: int64
          child 1, sampled: int64
      child 7, 2: struct<available: int64, sampled: int64>
          child 0, available: int64
          child 1, sampled: int64
      child 8, 3: struct<available: int64, sampled: int64>
          child 0, available: int64
          child 1, sampled: int64
      child 9, 4: struct<available: int64, sampled: int64>
          child 0, available: int64
          child 1, sampled: int64
      child 10, 5: struct<available: int64, sampled: int64>
          child 0, available: int64
          child 1, sampled: int64
      child 11, 6: struct<available: int64, sampled: int64>
          child 0, available: int64
          child 1, sampled: int64
      child 12, 7: struct<available: int64, sampled: int64>
          child 0, available: int64
          child 1, sampled: int64
      child 13, 8: struct<available: int64, sampled: int64>
          child 0, available: int64
          child 1, sampled: int64
      child 14, 9: struct<available: int64, sampled: int64>
          child 0, available: int64
          child 1, sampled: int64
to
{'format_version': Value('int64'), 'is_processed': Value('bool'), 'num_samples': Value('int64'), 'voxel_shape': List(Value('int64')), 'voxel_dtype': Value('string'), 'paths': {'voxels': Value('string'), 'biome_labels': Value('string')}, 'class_labels_format': Value('string'), 'num_blocks': Value('int64'), 'num_classes': Value('int64'), 'metadata_applied_during_processing': Value('bool'), 'source': {'path': Value('string'), 'had_metadata': Value('bool'), 'metadata_applied_at_source': Value('bool')}, 'sample_of': Value('string'), 'sample_source_indices_path': Value('string'), 'sample_selection': {'strategy': Value('string'), 'per_label_target': Value('int64'), 'selected_source_indices_count': Value('int64'), 'labels': {'0': {'available': Value('int64'), 'sampled': Value('int64')}, '1': {'available': Value('int64'), 'sampled': Value('int64')}, '10': {'available': Value('int64'), 'sampled': Value('int64')}, '11': {'available': Value('int64'), 'sampled': Value('int64')}, '12': {'available': Value('int64'), 'sampled': Value('int64')}, '13': {'available': Value('int64'), 'sampled': Value('int64')}, '14': {'available': Value('int64'), 'sampled': Value('int64')}, '2': {'available': Value('int64'), 'sampled': Value('int64')}, '3': {'available': Value('int64'), 'sampled': Value('int64')}, '4': {'available': Value('int64'), 'sampled': Value('int64')}, '5': {'available': Value('int64'), 'sampled': Value('int64')}, '6': {'available': Value('int64'), 'sampled': Value('int64')}, '7': {'available': Value('int64'), 'sampled': Value('int64')}, '8': {'available': Value('int64'), 'sampled': Value('int64')}, '9': {'available': Value('int64'), 'sampled': Value('int64')}}}}
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
              format_version: int64
              is_processed: bool
              num_samples: int64
              voxel_shape: list<item: int64>
                child 0, item: int64
              voxel_dtype: string
              paths: struct<voxels: string, biome_labels: string>
                child 0, voxels: string
                child 1, biome_labels: string
              voxel_representation: string
              embedding_dim: null
              embeddings_path: null
              class_labels_format: string
              num_blocks: int64
              num_classes: int64
              metadata_applied_during_processing: bool
              source: struct<path: string, had_metadata: bool, metadata_applied_at_source: bool>
                child 0, path: string
                child 1, had_metadata: bool
                child 2, metadata_applied_at_source: bool
              sample_of: string
              sample_source_indices_path: string
              sample_selection: struct<strategy: string, per_label_target: int64, selected_source_indices_count: int64, labels: stru (... 683 chars omitted)
                child 0, strategy: string
                child 1, per_label_target: int64
                child 2, selected_source_indices_count: int64
                child 3, labels: struct<0: struct<available: int64, sampled: int64>, 1: struct<available: int64, sampled: int64>, 10: (... 586 chars omitted)
                    child 0, 0: struct<available: int64, sampled: int64>
                        child 0, available: int64
                        child 1, sampled: int64
                    child 1, 1: struct<available: int64, sampled: int64>
                        child 0, available: int64
                        child 1, sampled: int64
                    child 2, 10: struct<available: int64, sampled: int64>
                        child 0, available: int64
                        child 1, sampled: int64
                    child 3, 11: struct<available: int64, sampled: int64>
                        child 0, available: int64
                        child 1, sampled: int64
                    child 4, 12: struct<available: int64, sampled: int64>
                        child 0, available: int64
                        child 1, sampled: int64
                    child 5, 13: struct<available: int64, sampled: int64>
                        child 0, available: int64
                        child 1, sampled: int64
                    child 6, 14: struct<available: int64, sampled: int64>
                        child 0, available: int64
                        child 1, sampled: int64
                    child 7, 2: struct<available: int64, sampled: int64>
                        child 0, available: int64
                        child 1, sampled: int64
                    child 8, 3: struct<available: int64, sampled: int64>
                        child 0, available: int64
                        child 1, sampled: int64
                    child 9, 4: struct<available: int64, sampled: int64>
                        child 0, available: int64
                        child 1, sampled: int64
                    child 10, 5: struct<available: int64, sampled: int64>
                        child 0, available: int64
                        child 1, sampled: int64
                    child 11, 6: struct<available: int64, sampled: int64>
                        child 0, available: int64
                        child 1, sampled: int64
                    child 12, 7: struct<available: int64, sampled: int64>
                        child 0, available: int64
                        child 1, sampled: int64
                    child 13, 8: struct<available: int64, sampled: int64>
                        child 0, available: int64
                        child 1, sampled: int64
                    child 14, 9: struct<available: int64, sampled: int64>
                        child 0, available: int64
                        child 1, sampled: int64
              to
              {'format_version': Value('int64'), 'is_processed': Value('bool'), 'num_samples': Value('int64'), 'voxel_shape': List(Value('int64')), 'voxel_dtype': Value('string'), 'paths': {'voxels': Value('string'), 'biome_labels': Value('string')}, 'class_labels_format': Value('string'), 'num_blocks': Value('int64'), 'num_classes': Value('int64'), 'metadata_applied_during_processing': Value('bool'), 'source': {'path': Value('string'), 'had_metadata': Value('bool'), 'metadata_applied_at_source': Value('bool')}, 'sample_of': Value('string'), 'sample_source_indices_path': Value('string'), 'sample_selection': {'strategy': Value('string'), 'per_label_target': Value('int64'), 'selected_source_indices_count': Value('int64'), 'labels': {'0': {'available': Value('int64'), 'sampled': Value('int64')}, '1': {'available': Value('int64'), 'sampled': Value('int64')}, '10': {'available': Value('int64'), 'sampled': Value('int64')}, '11': {'available': Value('int64'), 'sampled': Value('int64')}, '12': {'available': Value('int64'), 'sampled': Value('int64')}, '13': {'available': Value('int64'), 'sampled': Value('int64')}, '14': {'available': Value('int64'), 'sampled': Value('int64')}, '2': {'available': Value('int64'), 'sampled': Value('int64')}, '3': {'available': Value('int64'), 'sampled': Value('int64')}, '4': {'available': Value('int64'), 'sampled': Value('int64')}, '5': {'available': Value('int64'), 'sampled': Value('int64')}, '6': {'available': Value('int64'), 'sampled': Value('int64')}, '7': {'available': Value('int64'), 'sampled': Value('int64')}, '8': {'available': Value('int64'), 'sampled': Value('int64')}, '9': {'available': Value('int64'), 'sampled': Value('int64')}}}}
              because column names don't match

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Dream-Cubed Natural Representative Sample

This directory is a deterministic, class-stratified sample of the Dream-Cubed Natural dataset. It is provided for reviewer inspection of data quality; the full dataset remains available at https://huggingface.co/datasets/dream-cubed/DreamCubedNatural.

The sample dataset for human-authored data is available at https://huggingface.co/datasets/dream-cubed/DreamCubedHumanSample

The sample includes raw natural biome chunks and processed train/validation examples.

Creation Procedure

  • Sampling seed: 67
  • Last source mode added: raw
  • Raw target: up to 32 chunks per raw source label
  • Processed target: up to 128 chunks per label per split
  • Strategy: deterministic stratified sampling without replacement
  • Output: compact .npz raw samples and restored, unsharded processed .npy split samples

Exact source paths, selected source indices, sample counts, and output files are recorded in sample_manifest.json. Processed split directories also include source_indices.npy and fresh manifest.json files. Dataset metadata files copied from nearby public dataset files are stored under metadata/ when available.

Inspection Notes

This sample is intentionally small and should not be used for model training or quantitative evaluation. It is meant only to help reviewers inspect file formats, labels, chunk tensors, and overall data quality without downloading the full multi-GB dataset.
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