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
status: string
source_revision: string
source_read_time: timestamp[s]
table_rows: struct<documents: int64, instruments: int64, events: int64>
child 0, documents: int64
child 1, instruments: int64
child 2, events: int64
partition_counts: struct<documents: struct<2016: int64, 2017: int64, 2018: int64, 2019: int64, 2020: int64, 2021: int6 (... 248 chars omitted)
child 0, documents: struct<2016: int64, 2017: int64, 2018: int64, 2019: int64, 2020: int64, 2021: int64, 2022: int64, 20 (... 49 chars omitted)
child 0, 2016: int64
child 1, 2017: int64
child 2, 2018: int64
child 3, 2019: int64
child 4, 2020: int64
child 5, 2021: int64
child 6, 2022: int64
child 7, 2023: int64
child 8, 2024: int64
child 9, 2025: int64
child 10, 2026: int64
child 1, instruments: struct<2026: int64>
child 0, 2026: int64
child 2, events: struct<2017: int64, 2018: int64, 2019: int64, 2020: int64, 2021: int64, 2022: int64, 2023: int64, 20 (... 36 chars omitted)
child 0, 2017: int64
child 1, 2018: int64
child 2, 2019: int64
child 3, 2020: int64
child 4, 2021: int64
child 5, 2022: int64
child 6, 2023: int64
child 7, 2024: int64
child 8, 2025: int64
child 9, 2026: int64
foreign_key_errors: int64
rejection_tests: struct<duplicate_document: bool, orphan_event_document: bool, orphan_event_instrument: bool>
child 0, duplicate_document: bool
child 1, orphan_event_document
...
_case_gte_50: int64
child 2, worst_case_gte_100: int64
child 3, fd_ratio_gte_1_2: int64
child 4, fd_ratio_gte_1_5: int64
child 5, fd_ratio_gte_2: int64
child 6, runway_lte_6: int64
child 7, runway_lte_12: int64
child 8, runway_lte_18: int64
child 9, expiry_3m_gte_10: int64
child 10, expiry_3m_gte_20: int64
child 11, expiry_6m_gte_15: int64
child 12, expiry_6m_gte_30: int64
child 13, expiry_12m_gte_25: int64
child 14, expiry_12m_gte_40: int64
child 15, needs_review: int64
artifacts: list<item: struct<table: string, file: string, rows: int64, bytes: int64, sha256: string>>
child 0, item: struct<table: string, file: string, rows: int64, bytes: int64, sha256: string>
child 0, table: string
child 1, file: string
child 2, rows: int64
child 3, bytes: int64
child 4, sha256: string
sort_top_10: struct<worst_case_dilution_pct: list<item: string>, fd_ratio: list<item: string>, expiry_pct_lt_3m: (... 128 chars omitted)
child 0, worst_case_dilution_pct: list<item: string>
child 0, item: string
child 1, fd_ratio: list<item: string>
child 0, item: string
child 2, expiry_pct_lt_3m: list<item: string>
child 0, item: string
child 3, expiry_pct_lt_6m: list<item: string>
child 0, item: string
child 4, expiry_pct_lt_12m: list<item: string>
child 0, item: string
child 5, ticker_asc: list<item: string>
child 0, item: string
browser_cache_budget_bytes: int64
rows_compared_to_production: int64
to
{'status': Value('string'), 'source_revision': Value('string'), 'source_read_time': Value('timestamp[s]'), 'rows_compared_to_production': Value('int64'), 'filter_counts': {'worst_case_gte_25': Value('int64'), 'worst_case_gte_50': Value('int64'), 'worst_case_gte_100': Value('int64'), 'fd_ratio_gte_1_2': Value('int64'), 'fd_ratio_gte_1_5': Value('int64'), 'fd_ratio_gte_2': Value('int64'), 'runway_lte_6': Value('int64'), 'runway_lte_12': Value('int64'), 'runway_lte_18': Value('int64'), 'expiry_3m_gte_10': Value('int64'), 'expiry_3m_gte_20': Value('int64'), 'expiry_6m_gte_15': Value('int64'), 'expiry_6m_gte_30': Value('int64'), 'expiry_12m_gte_25': Value('int64'), 'expiry_12m_gte_40': Value('int64'), 'needs_review': Value('int64')}, 'sort_top_10': {'worst_case_dilution_pct': List(Value('string')), 'fd_ratio': List(Value('string')), 'expiry_pct_lt_3m': List(Value('string')), 'expiry_pct_lt_6m': List(Value('string')), 'expiry_pct_lt_12m': List(Value('string')), 'ticker_asc': List(Value('string'))}, 'total_parquet_bytes': Value('int64'), 'browser_cache_budget_bytes': Value('int64'), 'artifacts': List({'table': Value('string'), 'file': Value('string'), 'rows': Value('int64'), 'bytes': Value('int64'), 'sha256': 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 478, 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 2818, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, 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 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, 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 2369, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
status: string
source_revision: string
source_read_time: timestamp[s]
table_rows: struct<documents: int64, instruments: int64, events: int64>
child 0, documents: int64
child 1, instruments: int64
child 2, events: int64
partition_counts: struct<documents: struct<2016: int64, 2017: int64, 2018: int64, 2019: int64, 2020: int64, 2021: int6 (... 248 chars omitted)
child 0, documents: struct<2016: int64, 2017: int64, 2018: int64, 2019: int64, 2020: int64, 2021: int64, 2022: int64, 20 (... 49 chars omitted)
child 0, 2016: int64
child 1, 2017: int64
child 2, 2018: int64
child 3, 2019: int64
child 4, 2020: int64
child 5, 2021: int64
child 6, 2022: int64
child 7, 2023: int64
child 8, 2024: int64
child 9, 2025: int64
child 10, 2026: int64
child 1, instruments: struct<2026: int64>
child 0, 2026: int64
child 2, events: struct<2017: int64, 2018: int64, 2019: int64, 2020: int64, 2021: int64, 2022: int64, 2023: int64, 20 (... 36 chars omitted)
child 0, 2017: int64
child 1, 2018: int64
child 2, 2019: int64
child 3, 2020: int64
child 4, 2021: int64
child 5, 2022: int64
child 6, 2023: int64
child 7, 2024: int64
child 8, 2025: int64
child 9, 2026: int64
foreign_key_errors: int64
rejection_tests: struct<duplicate_document: bool, orphan_event_document: bool, orphan_event_instrument: bool>
child 0, duplicate_document: bool
child 1, orphan_event_document
...
_case_gte_50: int64
child 2, worst_case_gte_100: int64
child 3, fd_ratio_gte_1_2: int64
child 4, fd_ratio_gte_1_5: int64
child 5, fd_ratio_gte_2: int64
child 6, runway_lte_6: int64
child 7, runway_lte_12: int64
child 8, runway_lte_18: int64
child 9, expiry_3m_gte_10: int64
child 10, expiry_3m_gte_20: int64
child 11, expiry_6m_gte_15: int64
child 12, expiry_6m_gte_30: int64
child 13, expiry_12m_gte_25: int64
child 14, expiry_12m_gte_40: int64
child 15, needs_review: int64
artifacts: list<item: struct<table: string, file: string, rows: int64, bytes: int64, sha256: string>>
child 0, item: struct<table: string, file: string, rows: int64, bytes: int64, sha256: string>
child 0, table: string
child 1, file: string
child 2, rows: int64
child 3, bytes: int64
child 4, sha256: string
sort_top_10: struct<worst_case_dilution_pct: list<item: string>, fd_ratio: list<item: string>, expiry_pct_lt_3m: (... 128 chars omitted)
child 0, worst_case_dilution_pct: list<item: string>
child 0, item: string
child 1, fd_ratio: list<item: string>
child 0, item: string
child 2, expiry_pct_lt_3m: list<item: string>
child 0, item: string
child 3, expiry_pct_lt_6m: list<item: string>
child 0, item: string
child 4, expiry_pct_lt_12m: list<item: string>
child 0, item: string
child 5, ticker_asc: list<item: string>
child 0, item: string
browser_cache_budget_bytes: int64
rows_compared_to_production: int64
to
{'status': Value('string'), 'source_revision': Value('string'), 'source_read_time': Value('timestamp[s]'), 'rows_compared_to_production': Value('int64'), 'filter_counts': {'worst_case_gte_25': Value('int64'), 'worst_case_gte_50': Value('int64'), 'worst_case_gte_100': Value('int64'), 'fd_ratio_gte_1_2': Value('int64'), 'fd_ratio_gte_1_5': Value('int64'), 'fd_ratio_gte_2': Value('int64'), 'runway_lte_6': Value('int64'), 'runway_lte_12': Value('int64'), 'runway_lte_18': Value('int64'), 'expiry_3m_gte_10': Value('int64'), 'expiry_3m_gte_20': Value('int64'), 'expiry_6m_gte_15': Value('int64'), 'expiry_6m_gte_30': Value('int64'), 'expiry_12m_gte_25': Value('int64'), 'expiry_12m_gte_40': Value('int64'), 'needs_review': Value('int64')}, 'sort_top_10': {'worst_case_dilution_pct': List(Value('string')), 'fd_ratio': List(Value('string')), 'expiry_pct_lt_3m': List(Value('string')), 'expiry_pct_lt_6m': List(Value('string')), 'expiry_pct_lt_12m': List(Value('string')), 'ticker_asc': List(Value('string'))}, 'total_parquet_bytes': Value('int64'), 'browser_cache_budget_bytes': Value('int64'), 'artifacts': List({'table': Value('string'), 'file': Value('string'), 'rows': Value('int64'), 'bytes': Value('int64'), 'sha256': 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.
DilutionRisk ASX Dataset
Versioned public tables and search indexes for dilutionrisk.com.
Status: foundation only — no production data has been migrated yet.
Planned release contents:
- companies and dated company snapshots;
- ASX announcement metadata and public artifact URLs;
- capital instruments and instrument events;
- screener snapshots; and
- a revision-matched SQLite full-text index.
Each release will identify its exact Hub commit, source watermark, table counts, file hashes, schema version, publisher version and exclusion-manifest version. The target update cadence is daily after the 06:00 Australia/Melbourne source scan, with publication by 10:00.
The versioned manifest contract and validated example are published under
schema/. Immutable release manifests are stored in the public
Bucket at manifests/v1/revisions/{DATASET_COMMIT}.json; the Bucket's
manifests/v1/latest.json pointer is updated only after all referenced objects
and the exact Dataset commit are verified.
Source and licence
Announcement content originates from ASX-listed issuers and ASX distribution. DilutionRisk's business team has confirmed written ASX redistribution permission for the public scope. Public availability does not by itself grant a new open-data licence or transfer rights held by ASX, issuers or other licensors. See LICENSE and the source/attribution fields in each release.
The Dataset is built from an explicit public allow-list. Operational Firestore state, worker logs, retries, embeddings, backups, credentials, signed URLs, held documents and other private fields are excluded.
Accuracy and intended use
Markdown and structured facts are automated transformations and can contain OCR or extraction errors. Always verify a figure against the linked issuer announcement. This is research infrastructure, not financial advice.
For corrections or takedowns, use the Dataset Community tab or contact DilutionRisk through dilutionrisk.com. Include the canonical document ID and source URL, but do not post sensitive content.
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