Dataset Viewer
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
title: string
document_label: string
subject_domains: list<item: string>
child 0, item: string
subject_summary: string
panel_titles: list<item: string>
child 0, item: string
variables: list<item: struct<name: string, unit: struct<source_text: string>, axis_roles: list<item: string>, s (... 85 chars omitted)
child 0, item: struct<name: string, unit: struct<source_text: string>, axis_roles: list<item: string>, statistical_ (... 73 chars omitted)
child 0, name: string
child 1, unit: struct<source_text: string>
child 0, source_text: string
child 2, axis_roles: list<item: string>
child 0, item: string
child 3, statistical_forms: list<item: struct<source_text: string, normalized_value: string>>
child 0, item: struct<source_text: string, normalized_value: string>
child 0, source_text: string
child 1, normalized_value: string
dimensions: list<item: struct<name: string, categories: list<item: struct<source_text: string>>>>
child 0, item: struct<name: string, categories: list<item: struct<source_text: string>>>
child 0, name: string
child 1, categories: list<item: struct<source_text: string>>
child 0, item: struct<source_text: string>
child 0, source_text: string
population_group: string
visualization_types: list<item: struct<normalized_value: string>>
child 0, item: struct<normalized_value: string>
child 0, normalized_value: string
temporal_coverage: struct<period: struct<source_text: string, start: string, end: string, relation: string, precision: (... 8 chars omitted)
child 0, period: struct<source_text: string, start: string, end: string, relation: string, precision: string>
child 0, source_text: string
child 1, start: string
child 2, end: string
child 3, relation: string
child 4, precision: string
geographic_coverage: struct<locations: list<item: struct<source_text: string>>>
child 0, locations: list<item: struct<source_text: string>>
child 0, item: struct<source_text: string>
child 0, source_text: string
provenance: struct<sources: list<item: struct<name: string>>>
child 0, sources: list<item: struct<name: string>>
child 0, item: struct<name: string>
child 0, name: string
languages: list<item: struct<tag: string>>
child 0, item: struct<tag: string>
child 0, tag: string
analysis_methods: list<item: string>
child 0, item: string
interpretive_notes: list<item: string>
child 0, item: string
to
{'title': Value('string'), 'document_label': Value('string'), 'subject_summary': Value('string'), 'panel_titles': List(Value('string')), 'variables': List({'name': Value('string'), 'axis_roles': List(Value('string')), 'statistical_forms': List({'source_text': Value('string'), 'normalized_value': Value('string')})}), 'population_group': Value('string'), 'visualization_types': List({'normalized_value': Value('string')}), 'languages': List({'tag': Value('string')}), 'interpretive_notes': List(Value('string')), 'analysis_methods': List(Value('string'))}
because column names don't match
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 2951, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2461, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2486, 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 547, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 430, 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 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
title: string
document_label: string
subject_domains: list<item: string>
child 0, item: string
subject_summary: string
panel_titles: list<item: string>
child 0, item: string
variables: list<item: struct<name: string, unit: struct<source_text: string>, axis_roles: list<item: string>, s (... 85 chars omitted)
child 0, item: struct<name: string, unit: struct<source_text: string>, axis_roles: list<item: string>, statistical_ (... 73 chars omitted)
child 0, name: string
child 1, unit: struct<source_text: string>
child 0, source_text: string
child 2, axis_roles: list<item: string>
child 0, item: string
child 3, statistical_forms: list<item: struct<source_text: string, normalized_value: string>>
child 0, item: struct<source_text: string, normalized_value: string>
child 0, source_text: string
child 1, normalized_value: string
dimensions: list<item: struct<name: string, categories: list<item: struct<source_text: string>>>>
child 0, item: struct<name: string, categories: list<item: struct<source_text: string>>>
child 0, name: string
child 1, categories: list<item: struct<source_text: string>>
child 0, item: struct<source_text: string>
child 0, source_text: string
population_group: string
visualization_types: list<item: struct<normalized_value: string>>
child 0, item: struct<normalized_value: string>
child 0, normalized_value: string
temporal_coverage: struct<period: struct<source_text: string, start: string, end: string, relation: string, precision: (... 8 chars omitted)
child 0, period: struct<source_text: string, start: string, end: string, relation: string, precision: string>
child 0, source_text: string
child 1, start: string
child 2, end: string
child 3, relation: string
child 4, precision: string
geographic_coverage: struct<locations: list<item: struct<source_text: string>>>
child 0, locations: list<item: struct<source_text: string>>
child 0, item: struct<source_text: string>
child 0, source_text: string
provenance: struct<sources: list<item: struct<name: string>>>
child 0, sources: list<item: struct<name: string>>
child 0, item: struct<name: string>
child 0, name: string
languages: list<item: struct<tag: string>>
child 0, item: struct<tag: string>
child 0, tag: string
analysis_methods: list<item: string>
child 0, item: string
interpretive_notes: list<item: string>
child 0, item: string
to
{'title': Value('string'), 'document_label': Value('string'), 'subject_summary': Value('string'), 'panel_titles': List(Value('string')), 'variables': List({'name': Value('string'), 'axis_roles': List(Value('string')), 'statistical_forms': List({'source_text': Value('string'), 'normalized_value': Value('string')})}), 'population_group': Value('string'), 'visualization_types': List({'normalized_value': Value('string')}), 'languages': List({'tag': Value('string')}), 'interpretive_notes': List(Value('string')), 'analysis_methods': List(Value('string'))}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Data Snapshot Metadata Gold
This dataset contains human-reviewed metadata for 102 data snapshots: figures
and tables extracted from institutional documents. Each JSON file describes one
snapshot image in the
ai4data/data-snapshot
dataset.
This repository contains snapshot-level metadata only. It does not contain the
snapshot images or the separate source-document metadata published in
ai4data/data-snapshot.
Dataset structure
All records are currently assigned to the train split.
| Source collection | Figures | Tables | Total |
|---|---|---|---|
| UNHCR | 17 | 17 | 34 |
| PRWP | 17 | 17 | 34 |
| Refugee | 17 | 17 | 34 |
| Total | 51 | 51 | 102 |
The repository is organized by source collection and snapshot type:
data_snapshot_metadata_gold/
├── prwp/{figure,table}/*.json
├── refugee/{figure,table}/*.json
├── unhcr/{figure,table}/*.json
├── data_snapshot_metadata_schema_v1.4.schema.json
└── README.md
Metadata filenames follow this convention:
{source_document}_{figure|table}_{index}.json
The corresponding snapshot has the same basename and a .png extension in
ai4data/data-snapshot. Source-document names may contain underscores, so use
the full basename for matching rather than splitting on every underscore.
Loading the dataset
from datasets import load_dataset
dataset = load_dataset(
"ai4data/data_snapshot_metadata_gold",
split="train",
)
Metadata schema
The records conform to Data Snapshot Metadata Schema v1.4. The included
data_snapshot_metadata_schema_v1.4.schema.json
is a frozen, generated JSON Schema for this dataset release.
The canonical schema is implemented in Pydantic. This release is pinned to
commit
2b317a4d95b1c929b32c790b678646fdfca16eef.
The JSON Schema uses JSON Schema Draft 2020-12. The complete field definitions, constraints, descriptions, and examples are provided in the schema file.
Schema v1.4 is fixed for this gold release. Subsequent schema changes will use a new version, beginning with v1.5, and will require a separate metadata review and dataset update.
Dataset creation
The metadata was initially produced through model-assisted structured extraction from data snapshot images. The generated records were subsequently reviewed and corrected by a human annotator against the images. Source-document metadata was not supplied to the extraction model.
"Gold" means human-reviewed. It does not imply that every field is objectively unambiguous or independently verified against the complete source document.
All 102 records were validated against the canonical Schema v1.4 Pydantic model before publication.
Intended uses
The dataset supports work on:
- structured metadata extraction from figures and tables;
- evaluation of schema-constrained extraction systems;
- metadata quality review and error analysis; and
- research on describing data-bearing content in institutional documents.
Limitations
- The dataset contains 102 selected snapshots and is not representative of all institutional documents or visualization types.
- Metadata is based on evidence visible in each snapshot image. Relevant context elsewhere in the source document may not be represented.
- Some fields require interpretation and may admit more than one reasonable annotation.
- The current release contains only a
trainsplit. Train/test assignments will be defined in a later dataset release.
License
The metadata in this repository is released under the MIT License. Snapshot images are not included here and remain subject to the licensing and attribution terms of their source dataset and original publishers.
Paper and citation
[TBD]
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