Dragon Finance Corpus
Collection
▓▒░ A balanced corpus of public financial data across 12 classes — US and world official sources; every figure hub-measured. ░▒▓ • 13 items • Updated • 1
Dataset Preview
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The dataset generation failed
Error code: DatasetGenerationError
Exception: TypeError
Message: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1539, in _prepare_split_single
for key, record in generator:
^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 621, in wrapped
for item in generator(*args, **kwargs):
~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 131, in _generate_examples
for example_idx, example in enumerate(self._get_pipeline_from_tar(tar_path, tar_iterator)):
~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 32, in _get_pipeline_from_tar
for filename, f in tar_iterator:
^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/track.py", line 49, in __iter__
for x in self.generator(*self.args):
~~~~~~~~~~~~~~^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 1419, in _iter_from_urlpath
with xopen(urlpath, "rb", download_config=download_config, block_size=0) as f:
~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 976, in xopen
file_obj = fs.open(paths[0], mode)
File "<string>", line 3, in open
File "/usr/local/lib/python3.14/unittest/mock.py", line 1176, in __call__
return self._mock_call(*args, **kwargs)
~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/unittest/mock.py", line 1180, in _mock_call
return self._execute_mock_call(*args, **kwargs)
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/unittest/mock.py", line 1247, in _execute_mock_call
result = effect(*args, **kwargs)
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 794, in wrapped
tracker.files[urlpath] = {"read": 0, "size": int(f.size)}
~~~^^^^^^^^
TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1379, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 956, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1401, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1579, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
zip unknown | __key__ string | __url__ string |
|---|---|---|
"UEsDBBQACAgIAMAwXF0AAAAAAAAAAAAAAAAUAAAAV1NfQ0JQT0xfY3N2X2NvbC5jc3ZU2N3ObFlyHdZX0QO0AUWsn4i4pCUSICy(...TRUNCATED) | ./WS_CBPOL_csv_col | "hf://datasets/dragonlimited/Dragon-Finance-Global@bfa3955c7823f20ad5b0b56e0104cd8da225af39/bis-bulk(...TRUNCATED) |
"UEsDBBQACAgIAExHW10AAAAAAAAAAAAAAAAWAAAAV1NfQ0JTX1BVQl9jc3ZfY29sLmNzduy9W5IcR5Ku+T6rSOmX6ZYT6cfdzPx(...TRUNCATED) | ./WS_CBS_PUB_csv_col | "hf://datasets/dragonlimited/Dragon-Finance-Global@bfa3955c7823f20ad5b0b56e0104cd8da225af39/bis-bulk(...TRUNCATED) |
"UEsDBBQACAgIAONBGl0AAAAAAAAAAAAAAAATAAAAV1NfQ0JUQV9jc3ZfY29sLmNzduy93ZYcx5Fued9PkUs3La2VSIb/u6GvQBG(...TRUNCATED) | ./WS_CBTA_csv_col | "hf://datasets/dragonlimited/Dragon-Finance-Global@bfa3955c7823f20ad5b0b56e0104cd8da225af39/bis-bulk(...TRUNCATED) |
"UEsDBBQACAgIAKVwxVwAAAAAAAAAAAAAAAAcAAAAV1NfQ1BNSV9DQVNITEVTU19jc3ZfY29sLmNzdty921ZcV5qte7+fguarzNZ(...TRUNCATED) | ./WS_CPMI_CASHLESS_csv_col | "hf://datasets/dragonlimited/Dragon-Finance-Global@bfa3955c7823f20ad5b0b56e0104cd8da225af39/bis-bulk(...TRUNCATED) |
"UEsDBBQACAgIAK5wxVwAAAAAAAAAAAAAAAAXAAAAV1NfQ1BNSV9DVDFfY3N2X2NvbC5jc3bcvdlyG1m2pnnfTyELszbrNnN67Xm(...TRUNCATED) | ./WS_CPMI_CT1_csv_col | "hf://datasets/dragonlimited/Dragon-Finance-Global@bfa3955c7823f20ad5b0b56e0104cd8da225af39/bis-bulk(...TRUNCATED) |
"UEsDBBQACAgIALVwxVwAAAAAAAAAAAAAAAAXAAAAV1NfQ1BNSV9DVDJfY3N2X2NvbC5jc3bcvdlSY1mWrnt/ngJLs1MWaXuxavY(...TRUNCATED) | ./WS_CPMI_CT2_csv_col | "hf://datasets/dragonlimited/Dragon-Finance-Global@bfa3955c7823f20ad5b0b56e0104cd8da225af39/bis-bulk(...TRUNCATED) |
"UEsDBBQACAgIAJdwxVwAAAAAAAAAAAAAAAAbAAAAV1NfQ1BNSV9ERVZJQ0VTX2Nzdl9jb2wuY3N23b3bVhtZuiV830+h4ZvOHCN(...TRUNCATED) | ./WS_CPMI_DEVICES_csv_col | "hf://datasets/dragonlimited/Dragon-Finance-Global@bfa3955c7823f20ad5b0b56e0104cd8da225af39/bis-bulk(...TRUNCATED) |
"UEsDBBQACAgIAJJwxVwAAAAAAAAAAAAAAAAcAAAAV1NfQ1BNSV9JTlNUSVRVVF9jc3ZfY29sLmNzduy923IbV7Yt+N5fgVY/dFV(...TRUNCATED) | ./WS_CPMI_INSTITUT_csv_col | "hf://datasets/dragonlimited/Dragon-Finance-Global@bfa3955c7823f20ad5b0b56e0104cd8da225af39/bis-bulk(...TRUNCATED) |
"UEsDBBQACAgIAItwxVwAAAAAAAAAAAAAAAAZAAAAV1NfQ1BNSV9NQUNST19jc3ZfY29sLmNzdsy93ZJcx5WleT9PAZub6TaLCvP(...TRUNCATED) | ./WS_CPMI_MACRO_csv_col | "hf://datasets/dragonlimited/Dragon-Finance-Global@bfa3955c7823f20ad5b0b56e0104cd8da225af39/bis-bulk(...TRUNCATED) |
"UEsDBBQACAgIAGV1l1wAAAAAAAAAAAAAAAAcAAAAV1NfQ1BNSV9QQVJUSUNJUF9jc3ZfY29sLmNzduy923bbSJI2er+fAssXs3r(...TRUNCATED) | ./WS_CPMI_PARTICIP_csv_col | "hf://datasets/dragonlimited/Dragon-Finance-Global@bfa3955c7823f20ad5b0b56e0104cd8da225af39/bis-bulk(...TRUNCATED) |
End of preview.
Dragon Finance — global
stored: 3.28 GB · shards: 6 · class: global · sources: US + global official
World official statistics and multilateral data - the non-US official record, from the institutions that publish it.
Part of the Dragon Finance Corpus — public financial data from US federal agencies, world official institutions and public registries. Nothing licensed, paywalled or registration-only.
Front door: Dragon-Finance-Corpus
— every card, the chunked corpus and the derived artefacts, all measured from the hub;
collection
— this repo and its siblings.
How this was built, and what comes next: ROADMAP.md - the pipeline's phases, its operating procedure, and the sources it will never take.
Coverage
| Multilateral | World Bank indicator catalogue (29,544 available; a finance core by default), IMF DataMapper |
| European Union | Eurostat SDMX datasets (20 finance/macro datasets) and ECB Data Portal flows |
| Central-bank statistics | BIS bulk data sets (27 CSV datasets, selected from the 108 files BIS publishes) |
| Cross-border identifiers | GLEIF legal entity identifiers (filed in the macro class) |
Size
| metric | value |
|---|---|
| stored | 3.28 GB |
| shards | 6 |
| training-ready tokens | 76.4 M (0.28% of the chunked corpus) |
Training-ready text
Measured 2026-11-11 10:22 UTC over 166 of 167 shard(s), 1 counted before its latest append, 1 not yet counted. Tokens use the one convention these cards share - chars / 4 over the chunked text - so each figure is reconcilable against the shards.
| source | chunks | text | tokens | share |
|---|---|---|---|---|
worldbank |
178,683 | 0.290 GB | 72.5 M | 0.27% |
imf-datamapper |
19,668 | 0.012 GB | 2.9 M | 0.01% |
ecb-flows |
2,686 | 0.004 GB | 1.0 M | 0.00% |
Class attribution uses the shard path's first segment, so a tier that shares a segment with
another class (census, federalreserve, sec) is attributed to its dominant class.
Sources present
| source | jurisdiction | data | licence |
|---|---|---|---|
| BIS | global | bulk statistics: rates, credit, debt securities | BIS terms |
| ECB Data Portal | EU | yield curve, bank interest rates, money supply | ECB terms |
| Eurostat | EU | European statistics (SDMX datasets) | public |
| IMF | global | world economic indicators (DataMapper) | IMF terms |
| World Bank | global | world development indicators (catalogue) | CC-BY-4.0 |
What you can do with it
- Build cross-country panels from primary official sources
- Compare national accounts, prices, credit and external positions
- Join non-US statistics to entity identifiers
How this class was gathered
- World Bank indicators, read from the API's own catalogue (a finance core by default; millions of series are available) - CC-BY-4.0.
- Eurostat SDMX datasets, BIS bulk statistics (rates, credit, debt securities), IMF DataMapper indicators and ECB flows; GLEIF legal entity identifiers.
Source selection, source terms, sharding, the record that makes the corpus resumable, and what is excluded are documented once on the front door.
How to analyse it
- World Bank JSON is a two-element array:
[meta, [ {countryiso3code, date, value}, ... ]]- a long panel, so reshape before cross-country work. - Eurostat SDMX-CSV carries
DATAFLOW,TIME_PERIODandOBS_VALUEplus one column per series dimension; IMF DataMapper nests values by country then year, so flatten explicitly. - Key on indicator code + ISO3 country + period.
For engineers
Data is stored as tar shards (the hub caps a directory at 10,000 files and these tiers run to millions). Members keep their original names and relative paths.
import tarfile
with tarfile.open("0000.tar") as tf:
print(tf.getnames()[:5])
tf.extractall("out/")
| shard | size |
|---|---|
README.md |
8.0 KB |
bis-bulk/0000.tar |
226.61 MB |
ecb-flows/0000.tar |
3.67 MB |
eurostat/0000.tar |
2.92 GB |
imf-datamapper/0000.tar |
10.39 MB |
worldbank/indicators/0000.tar |
116.59 MB |
Every shard records its sha256 in the publication record; shards are append-only, so a resumed run re-uploads only what changed. A shard with a failed member is never marked complete, so missed files are retried rather than dropped.
Not advice, no warranty, no endorsement. Compiled from third-party public sources and provided 'as is': derived and model-generated content may be wrong, and source material is routinely restated, amended or superseded. No source agency, exchange or company has reviewed, approved or endorsed this dataset, and none is affiliated with its publisher. Do not use it to re-identify any person, to manipulate any market, or as the sole basis for any decision about any person, entity or instrument. Terms of use:
DISCLAIMER.md.
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