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jbduran
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vcc-2026-bench

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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
base_seed: int64
bulk_target_sum: double
cell_eval2_version: string
config_hash: string
control_source_effective: string
control_source_requested: string
derived_seeds: list<item: int64>
  child 0, item: int64
metric_names: list<item: string>
  child 0, item: string
n_splits: int64
real_fingerprint: string
real_fingerprint_meta: string
seed_derivation: string
semantic_identity: string
semantic_params: struct<allow_discrete: bool, allow_fractional_counts: bool, autodetect_input_type: bool, bulk_target (... 935 chars omitted)
  child 0, allow_discrete: bool
  child 1, allow_fractional_counts: bool
  child 2, autodetect_input_type: bool
  child 3, bulk_target_sum: double
  child 4, comparator: string
  child 5, control: string
  child 6, de.auc_pval_floor: string
  child 7, de.auc_pval_floor_value: double
  child 8, de.backend_resolved: string
  child 9, de.clip_value: null
  child 10, de.epsilon: double
  child 11, de.fdr_scope: string
  child 12, de.mean_calc: string
  child 13, de.method: string
  child 14, de.min_abs_log2fc: double
  child 15, de.nan_lfc_policy: string
  child 16, de.p_adj_threshold: double
  child 17, de.sort_by: string
  child 18, device: string
  child 19, discrimination.embed_key: null
  child 20, discrimination.exclude_target_gene: bool
  child 21, discrimination.exclusion_scope: string
  child 22, discrimination.rank_denominator: string
  child 23, discrimination.tie_policy: string
  child 24, filter.filter_gene_min_cpm_cell: double
  child 25, grouped
...
r_value: double, backend: string, clip_value: null, epsi (... 177 chars omitted)
      child 0, auc_pval_floor: string
      child 1, auc_pval_floor_value: double
      child 2, backend: string
      child 3, clip_value: null
      child 4, epsilon: double
      child 5, fdr_scope: string
      child 6, mean_calc: string
      child 7, method: string
      child 8, min_abs_log2fc: double
      child 9, nan_lfc_policy: string
      child 10, p_adj_threshold: double
      child 11, replicate_col: null
      child 12, sort_by: string
  child 10, device: string
  child 11, discrimination: struct<distance: string, embed_key: null, exclude_target_gene: bool, exclusion_scope: string, rank_d (... 39 chars omitted)
      child 0, distance: string
      child 1, embed_key: null
      child 2, exclude_target_gene: bool
      child 3, exclusion_scope: string
      child 4, rank_denominator: string
      child 5, tie_policy: string
  child 12, filter: struct<filter_gene_min_cpm_cell: double>
      child 0, filter_gene_min_cpm_cell: double
  child 13, gather_threads: int64
  child 14, input_type: string
  child 15, max_counts_per_cell: double
  child 16, metrics: string
  child 17, num_threads: int64
  child 18, outdir: string
  child 19, pert_chunk: int64
  child 20, pert_col: string
  child 21, target_gene_map: null
  child 22, target_sum: double
  child 23, validate_input: bool
  child 24, version: string
exclude_target_gene: bool
n_excluded: int64
created_utc: timestamp[s]
emit: string
to
{'allow_discrete_effective': Value('bool'), 'baseline_emission': {'emit': Value('string'), 'genes_control_zero': List(Value('null')), 'max_row_total': Value('float64'), 'max_row_total_full_prediction': Value('float64'), 'max_scaled_noncontrol_row_total': Value('float64'), 'n_explicit_zeros_removed': Value('int64'), 'n_genes_control_zero': Value('int64'), 'n_genes_profile_zero_control_positive': Value('int64'), 'n_rows': Value('int64'), 'profile_mass_unreachable': Value('float64'), 'r_max': Value('float64'), 'r_median_nonzero': Value('float64'), 'seed': Value('int64')}, 'cell_eval2_version': Value('string'), 'comparator': Value('string'), 'config': {'allow_discrete': Value('bool'), 'allow_fractional_counts': Value('bool'), 'autodetect_input_type': Value('bool'), 'bulk_target_sum': Value('float64'), 'cache_pred': Value('null'), 'cache_real': Value('null'), 'cache_strict': Value('bool'), 'control': Value('string'), 'control_source': Value('string'), 'de': {'auc_pval_floor': Value('string'), 'auc_pval_floor_value': Value('float64'), 'backend': Value('string'), 'clip_value': Value('null'), 'epsilon': Value('float64'), 'fdr_scope': Value('string'), 'mean_calc': Value('string'), 'method': Value('string'), 'min_abs_log2fc': Value('float64'), 'nan_lfc_policy': Value('string'), 'p_adj_threshold': Value('float64'), 'replicate_col': Value('null'), 'sort_by': Value('string')}, 'device': Value('string'), 'discrimination': {'distance': Value('string'), 'embed_key': Value('null'), 'exclude_t
...
string'), 'embed_key': Value('null'), 'exclude_target_gene': Value('bool'), 'exclusion_scope': Value('string'), 'rank_denominator': Value('string'), 'tie_policy': Value('string')}, 'filter': {'filter_gene_min_cpm_cell': Value('float64')}, 'gather_threads': Value('int64'), 'input_type': Value('string'), 'max_counts_per_cell': Value('float64'), 'metrics': Value('string'), 'num_threads': Value('int64'), 'outdir': Value('string'), 'pert_chunk': Value('int64'), 'pert_col': Value('string'), 'target_gene_map': Value('null'), 'target_sum': Value('float64'), 'validate_input': Value('bool'), 'version': Value('string')}, 'control': Value('string'), 'control_source_effective': Value('string'), 'control_source_requested': Value('string'), 'created_utc': Value('timestamp[s]'), 'de_pred_fingerprint': Value('null'), 'de_real_fingerprint': Value('null'), 'de_real_supplied': Value('bool'), 'degenerate_metrics': List(Value('null')), 'emit': Value('string'), 'exclude_target_gene': Value('bool'), 'input_type_pred_effective': Value('string'), 'input_type_real_effective': Value('string'), 'metric_normalization': List(List(Value('string'))), 'metrics': List(Value('string')), 'n_excluded': Value('int64'), 'n_genes': Value('int64'), 'n_perturbations': Value('int64'), 'pert_col': Value('string'), 'resolved_de_backend': Value('string'), 'resolved_device': Value('string'), 'seed': Value('int64'), 'source': Value('string'), 'source_fingerprint': Value('string'), 'source_fingerprint_strict': Value('bool')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 149, 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 129, 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 544, 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 3052, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2559, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2584, 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 558, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 440, 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 361, 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 2381, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2309, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              base_seed: int64
              bulk_target_sum: double
              cell_eval2_version: string
              config_hash: string
              control_source_effective: string
              control_source_requested: string
              derived_seeds: list<item: int64>
                child 0, item: int64
              metric_names: list<item: string>
                child 0, item: string
              n_splits: int64
              real_fingerprint: string
              real_fingerprint_meta: string
              seed_derivation: string
              semantic_identity: string
              semantic_params: struct<allow_discrete: bool, allow_fractional_counts: bool, autodetect_input_type: bool, bulk_target (... 935 chars omitted)
                child 0, allow_discrete: bool
                child 1, allow_fractional_counts: bool
                child 2, autodetect_input_type: bool
                child 3, bulk_target_sum: double
                child 4, comparator: string
                child 5, control: string
                child 6, de.auc_pval_floor: string
                child 7, de.auc_pval_floor_value: double
                child 8, de.backend_resolved: string
                child 9, de.clip_value: null
                child 10, de.epsilon: double
                child 11, de.fdr_scope: string
                child 12, de.mean_calc: string
                child 13, de.method: string
                child 14, de.min_abs_log2fc: double
                child 15, de.nan_lfc_policy: string
                child 16, de.p_adj_threshold: double
                child 17, de.sort_by: string
                child 18, device: string
                child 19, discrimination.embed_key: null
                child 20, discrimination.exclude_target_gene: bool
                child 21, discrimination.exclusion_scope: string
                child 22, discrimination.rank_denominator: string
                child 23, discrimination.tie_policy: string
                child 24, filter.filter_gene_min_cpm_cell: double
                child 25, grouped
              ...
              r_value: double, backend: string, clip_value: null, epsi (... 177 chars omitted)
                    child 0, auc_pval_floor: string
                    child 1, auc_pval_floor_value: double
                    child 2, backend: string
                    child 3, clip_value: null
                    child 4, epsilon: double
                    child 5, fdr_scope: string
                    child 6, mean_calc: string
                    child 7, method: string
                    child 8, min_abs_log2fc: double
                    child 9, nan_lfc_policy: string
                    child 10, p_adj_threshold: double
                    child 11, replicate_col: null
                    child 12, sort_by: string
                child 10, device: string
                child 11, discrimination: struct<distance: string, embed_key: null, exclude_target_gene: bool, exclusion_scope: string, rank_d (... 39 chars omitted)
                    child 0, distance: string
                    child 1, embed_key: null
                    child 2, exclude_target_gene: bool
                    child 3, exclusion_scope: string
                    child 4, rank_denominator: string
                    child 5, tie_policy: string
                child 12, filter: struct<filter_gene_min_cpm_cell: double>
                    child 0, filter_gene_min_cpm_cell: double
                child 13, gather_threads: int64
                child 14, input_type: string
                child 15, max_counts_per_cell: double
                child 16, metrics: string
                child 17, num_threads: int64
                child 18, outdir: string
                child 19, pert_chunk: int64
                child 20, pert_col: string
                child 21, target_gene_map: null
                child 22, target_sum: double
                child 23, validate_input: bool
                child 24, version: string
              exclude_target_gene: bool
              n_excluded: int64
              created_utc: timestamp[s]
              emit: string
              to
              {'allow_discrete_effective': Value('bool'), 'baseline_emission': {'emit': Value('string'), 'genes_control_zero': List(Value('null')), 'max_row_total': Value('float64'), 'max_row_total_full_prediction': Value('float64'), 'max_scaled_noncontrol_row_total': Value('float64'), 'n_explicit_zeros_removed': Value('int64'), 'n_genes_control_zero': Value('int64'), 'n_genes_profile_zero_control_positive': Value('int64'), 'n_rows': Value('int64'), 'profile_mass_unreachable': Value('float64'), 'r_max': Value('float64'), 'r_median_nonzero': Value('float64'), 'seed': Value('int64')}, 'cell_eval2_version': Value('string'), 'comparator': Value('string'), 'config': {'allow_discrete': Value('bool'), 'allow_fractional_counts': Value('bool'), 'autodetect_input_type': Value('bool'), 'bulk_target_sum': Value('float64'), 'cache_pred': Value('null'), 'cache_real': Value('null'), 'cache_strict': Value('bool'), 'control': Value('string'), 'control_source': Value('string'), 'de': {'auc_pval_floor': Value('string'), 'auc_pval_floor_value': Value('float64'), 'backend': Value('string'), 'clip_value': Value('null'), 'epsilon': Value('float64'), 'fdr_scope': Value('string'), 'mean_calc': Value('string'), 'method': Value('string'), 'min_abs_log2fc': Value('float64'), 'nan_lfc_policy': Value('string'), 'p_adj_threshold': Value('float64'), 'replicate_col': Value('null'), 'sort_by': Value('string')}, 'device': Value('string'), 'discrimination': {'distance': Value('string'), 'embed_key': Value('null'), 'exclude_t
              ...
              string'), 'embed_key': Value('null'), 'exclude_target_gene': Value('bool'), 'exclusion_scope': Value('string'), 'rank_denominator': Value('string'), 'tie_policy': Value('string')}, 'filter': {'filter_gene_min_cpm_cell': Value('float64')}, 'gather_threads': Value('int64'), 'input_type': Value('string'), 'max_counts_per_cell': Value('float64'), 'metrics': Value('string'), 'num_threads': Value('int64'), 'outdir': Value('string'), 'pert_chunk': Value('int64'), 'pert_col': Value('string'), 'target_gene_map': Value('null'), 'target_sum': Value('float64'), 'validate_input': Value('bool'), 'version': Value('string')}, 'control': Value('string'), 'control_source_effective': Value('string'), 'control_source_requested': Value('string'), 'created_utc': Value('timestamp[s]'), 'de_pred_fingerprint': Value('null'), 'de_real_fingerprint': Value('null'), 'de_real_supplied': Value('bool'), 'degenerate_metrics': List(Value('null')), 'emit': Value('string'), 'exclude_target_gene': Value('bool'), 'input_type_pred_effective': Value('string'), 'input_type_real_effective': Value('string'), 'metric_normalization': List(List(Value('string'))), 'metrics': List(Value('string')), 'n_excluded': Value('int64'), 'n_genes': Value('int64'), 'n_perturbations': Value('int64'), 'pert_col': Value('string'), 'resolved_de_backend': Value('string'), 'resolved_device': Value('string'), 'seed': Value('int64'), 'source': Value('string'), 'source_fingerprint': Value('string'), 'source_fingerprint_strict': Value('bool')}
              because column names don't match
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VCC 2026 bench data

Processed public CRISPRi Perturb-seq data for the Unbounded Labs Virtual Cell Challenge 2026 harness (projects/vcc-2026 in github.com/jim-glass/unbounded-labs). Download it to ~/vcc/data instead of rebuilding it.
proc/<line>_summary.h5ad: mean CPM per gene for each knockdown (obs: n_cells, mean_umi, kd = target CPM ratio).
bench/<line>/: a leave-one-line-out bench context (real.h5ad, controls_input.h5ad, perts.csv, scoring bundle).
Sources: Replogle et al. 2022 (K562, RPE1; CC BY 4.0) and Nadig et al. 2025 (Jurkat; GEO GSE264667). Contains no Virtual Cell Challenge data.
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Total file size:
9.94 GB