Error code:   ConfigNamesError
Exception:    TypeError
Message:      'str' object is not a mapping
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 66, in compute_config_names_response
                  config_names = get_dataset_config_names(
                                 ^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 161, in get_dataset_config_names
                  dataset_module = dataset_module_factory(
                                   ^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 1031, in dataset_module_factory
                  raise e1 from None
                File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 1004, in dataset_module_factory
                  ).get_module()
                    ^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 605, in get_module
                  dataset_infos = DatasetInfosDict.from_dataset_card_data(dataset_card_data)
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/info.py", line 386, in from_dataset_card_data
                  dataset_info = DatasetInfo._from_yaml_dict(dataset_card_data["dataset_info"])
                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/info.py", line 317, in _from_yaml_dict
                  yaml_data["features"] = Features._from_yaml_list(yaml_data["features"])
                                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 2031, in _from_yaml_list
                  return cls.from_dict(from_yaml_inner(yaml_data))
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 2027, in from_yaml_inner
                  return {name: from_yaml_inner(_feature) for name, _feature in zip(names, obj)}
                                ^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 2024, in from_yaml_inner
                  return {"_type": snakecase_to_camelcase(_type), **unsimplify(obj)[_type]}
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
              TypeError: 'str' object is not a mapping

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

CellARC-100K Meta

The meta release extends the core dataset with detailed episode metadata, rule-table exports, and auxiliary statistics suitable for analysis and simulator reconstruction.

Coverage Stratification

Partition	Coverage ρ	Episodes
train	mixed ρ (0.00–0.50)	100,000
validation	ρ = 0.10	200
validation	ρ = 0.20	200
validation	ρ = 0.40	200
validation	ρ = 0.05	200
validation	ρ = 0.80	200
test	ρ = 0.10	200
test	ρ = 0.20	200
test	ρ = 0.40	200
test	ρ = 0.05	200
test	ρ = 0.80	200

Additional Files

data/*/meta/ — per-shard metadata JSONL with morphology, coverage, and fingerprints.
metadata/dataset_stats.json — manifest snapshot after packaging.
metadata/rule_table_schema.json — documentation for the rule-table payload.
*_seen_fingerprints.txt — split-wise fingerprints for deduplication.

Rule Table Format

Each metadata record stores rule_table with lexicographically ordered outputs for all neighbourhoods of width 2 * radius + 1. The helper below converts the table into a callable suitable for cellpylib:

def make_rule(rule_payload):
    k = rule_payload["alphabet_size"]
    values = rule_payload["values"]

    def apply(neighbourhood, *_):
        idx = 0
        for cell in neighbourhood:
            idx = idx * k + cell
        return values[idx]

    return apply

To load the metadata shards:

from datasets import load_dataset

meta = load_dataset(
    "mireklzicar/cellarc_100k_meta",
    data_files={"train": "data/train/meta/train_part*_meta.jsonl"},
    split="train",
)

Licensing

Dataset: Creative Commons Attribution 4.0 International (CC BY 4.0)
Code: MIT License

Downloads last month: 126