Dataset Viewer
The dataset viewer is not available for this dataset.
Cannot get the config names for the dataset.
Error code: ConfigNamesError
Exception: AttributeError
Message: 'str' object has no attribute 'items'
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 67, in compute_config_names_response
config_names = get_dataset_config_names(
path=dataset,
token=hf_token,
)
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 161, in get_dataset_config_names
dataset_module = dataset_module_factory(
path,
...<4 lines>...
**download_kwargs,
)
File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 1182, in dataset_module_factory
raise e1 from None
File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 1157, in dataset_module_factory
).get_module()
~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 692, in get_module
config_name: DatasetInfo.from_dict(dataset_info_dict)
~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/info.py", line 284, in from_dict
return cls(**{k: v for k, v in dataset_info_dict.items() if k in field_names})
^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'str' object has no attribute 'items'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.
Notes on Astronomy Text Tabular data
Overview
Preparation notes and schema examples for Astronomy tasks using Text Tabular data. Full source material is intentionally not bundled, so provenance and licensing remain explicit.
Included Material
dataloader.py— loading, cleaning and split preparation.dataset_infos.json— schema and split metadata.metadata_sample.jsonl— readable records for schema checks.
Validation Checklist
- check source licenses before redistribution
- scan for duplicates across splits
- inspect missing values and label balance
- watch for modality-specific corruption
Risks And Limitations
External sources may be unrepresentative, mis-licensed, or contain sensitive fields. Source review, privacy checks and bias analysis are the user's responsibility before training or redistribution.
Data Sources
- external sources are referenced, not redistributed
- record retrieval date and filtering rules
- keep provenance explicit before any training run
Intended Use
- pipeline testing and source adaptation
- controlled research split preparation
- not a training corpus — schema inspection only
Processing Choices
- Storage format: lmdb
- Preprocessing: minimal
- Augmentation: none
- Split strategy: kfold 5
- Sampling: balanced
- Quality checks: strict
- Labeling: semi auto
Files
| File | Purpose |
|---|---|
dataloader.py |
primary artifact |
README.md |
this documentation |
dataset_infos.json |
schema metadata |
metadata_sample.jsonl |
schema sample |
License
Distributed under the cc0-1.0 license. External datasets keep their own terms — check them separately.
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