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
The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
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 |
|---|---|---|
[
60,
33,
68,
79,
67,
84,
89,
80,
69,
32,
104,
116,
109,
108,
62,
10,
60,
104,
116,
109,
108,
32,
32,
108,
97,
110,
103,
61,
34,
101,
110,
34,
32,
100,
105,
114,
61,
34,
108,
116,
114,
34,
32,
112,
114,
101,
102,
105,
120,... | ./NIPA | hf://datasets/dragonlimited/Dragon-Finance-Macro@d7c3c4053060c8ea7e8b064d941e53aece050538/bea/national/0000.tar |
[
60,
33,
68,
79,
67,
84,
89,
80,
69,
32,
104,
116,
109,
108,
62,
10,
60,
104,
116,
109,
108,
32,
32,
108,
97,
110,
103,
61,
34,
101,
110,
34,
32,
100,
105,
114,
61,
34,
108,
116,
114,
34,
32,
112,
114,
101,
102,
105,
120,... | ./Underlying | hf://datasets/dragonlimited/Dragon-Finance-Macro@d7c3c4053060c8ea7e8b064d941e53aece050538/bea/national/0000.tar |
"UEsDBBQAAAAIAAV7tlYnpC33m0AAAHN0AwASAAAAbmNlbl9tZXRhZGF0YS5qc29u7X17b9tItuf/8ykKjQVsA7Y7dpJOuheLXlq(...TRUNCATED) | ./2018q3_ncen | "hf://datasets/dragonlimited/Dragon-Finance-Macro@d7c3c4053060c8ea7e8b064d941e53aece050538/bea/regio(...TRUNCATED) |
"UEsDBBQAAgAIALp5X1cg2VAV3kBCAE+iNQEhAAAAQ0FFTVAyNU5fX0FMTF9BUkVBU18yMDAxXzIwMjIuY3N2xJrXbixFEEDfkfg(...TRUNCATED) | ./CAEMP25N | "hf://datasets/dragonlimited/Dragon-Finance-Macro@d7c3c4053060c8ea7e8b064d941e53aece050538/bea/regio(...TRUNCATED) |
"UEsDBBQACAAIAL11PVwAAAAAAAAAAAAAAAAWACAAQ0FHRFAyX19kZWZpbml0aW9uLnhtbHV4CwABBAAAAAAEAAAAAFVUDQAH97h(...TRUNCATED) | ./CAGDP2 | "hf://datasets/dragonlimited/Dragon-Finance-Macro@d7c3c4053060c8ea7e8b064d941e53aece050538/bea/regio(...TRUNCATED) |
"UEsDBBQACAAIAKpCLlwAAAAAAAAAAAAAAAAfACAAQ0FJTkMxX19BTExfQVJFQVNfMTk2OV8yMDI0LmNzdnV4CwABBAAAAAAEAAA(...TRUNCATED) | ./CAINC1 | "hf://datasets/dragonlimited/Dragon-Finance-Macro@d7c3c4053060c8ea7e8b064d941e53aece050538/bea/regio(...TRUNCATED) |
"UEsDBBQACAAIAPBJLlwAAAAAAAAAAAAAAAAgACAAQ0FJTkMzMF9fQUxMX0FSRUFTXzE5NjlfMjAyNC5jc3Z1eAsAAQQAAAAABAA(...TRUNCATED) | ./CAINC30 | "hf://datasets/dragonlimited/Dragon-Finance-Macro@d7c3c4053060c8ea7e8b064d941e53aece050538/bea/regio(...TRUNCATED) |
"UEsDBBQACAAIACpDLlwAAAAAAAAAAAAAAAAfACAAQ0FJTkM0X19BTExfQVJFQVNfMTk2OV8yMDI0LmNzdnV4CwABBAAAAAAEAAA(...TRUNCATED) | ./CAINC4 | "hf://datasets/dragonlimited/Dragon-Finance-Macro@d7c3c4053060c8ea7e8b064d941e53aece050538/bea/regio(...TRUNCATED) |
null | ./bd | "hf://datasets/dragonlimited/Dragon-Finance-Macro@d7c3c4053060c8ea7e8b064d941e53aece050538/bls/flat-(...TRUNCATED) |
null | ./cu | "hf://datasets/dragonlimited/Dragon-Finance-Macro@d7c3c4053060c8ea7e8b064d941e53aece050538/bls/flat-(...TRUNCATED) |
End of preview.
Dragon Finance — macro
stored: 294.65 GB · shards: 267 · class: macro · sources: US + global official
US and world economic statistics — prices, output, employment, income, trade and household wealth, drawn from national agencies and global institutions.
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
| Prices | CPI, PPI (BLS) |
| Activity | GDP, income, employment by state and county (BEA) |
| Labour | QCEW employment and wages by industry and county, 1986–present |
| Trade | Census international trade by commodity and country |
| Business dynamics | firm entry, exit and job creation |
| Rates and money | FRED series |
| Wealth | Fed distributional accounts, SCF |
| Tax | IRS Statistics of Income, Form 990 |
| Utterances | Federal Reserve speeches, FOMC, press |
| World official | World Bank indicator catalogue, Eurostat datasets, ECB flows, IMF/BIS/OECD series |
Size
| metric | value |
|---|---|
| stored | 294.65 GB |
| shards | 267 |
| training-ready tokens | 5.5 M (0.05% of the chunked corpus) |
Training-ready text
Measured 2026-11-05 10:38 UTC over 53 of 53 shard(s). 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 |
|---|---|---|---|---|
census |
29,918 | 0.017 GB | 4.3 M | 0.04% |
federalreserve |
3,763 | 0.004 GB | 1.1 M | 0.01% |
fred |
1,015 | 0.001 GB | 0.1 M | 0.00% |
bls |
3 | 0.000 GB | 0.0 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 |
|---|---|---|---|
| BEA | US | regional accounts | public |
| BLS | US | QCEW employment and wages | public |
| US Census | US | trade, housing, business dynamics | public |
| BLS | US | CPI / PPI flat files | public |
| US Census | US | economic census, ACS, CBP | public |
| FRED | US | rates + macro series | CC-BY-4.0 |
| ECB | EU | official euro reference rates | ECB terms |
| Eurostat | EU | European statistics (SDMX datasets) | public |
| Federal Reserve | US | Z.1 accounts, SCF, DFA | public |
| FRED (St. Louis Fed) | US | rates + macro series | CC-BY-4.0 |
| gleif | n/a | n/a | n/a |
| IMF | global | world economic indicators | IMF terms |
| IRS | US | Statistics of Income / Form 990 | US public record |
| progress.png | n/a | n/a | n/a |
| World Bank | global | world development indicators (catalogue) | CC-BY-4.0 |
What you can do with it
- Build macro panels from official series
- Benchmark regional activity and wages
- Analyse household wealth distribution
- Study firm dynamics by industry
How this class was gathered
- FRED series as CSV (rates, money, prices, activity, labour, housing, trade) - no key, CC-BY-4.0, attribution required.
- BLS flat files: CPI, PPI, QCEW employment and wages, and the full time-series tree, including series files that carry no extension.
- US Census - American Community Survey, County Business Patterns, the Economic Census and Business Dynamics Statistics; BEA national and regional accounts.
- Also: IRS Statistics of Income and Form 990 e-file data, Federal Reserve speeches and press material, 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
- FRED CSV is two columns,
observation_dateand the series id, with.for a missing observation - read as string, not float, unless you handle that. - BLS flat files are tab-delimited with a series-id column; periods encode frequency (monthly
M01-M12, annualA01), so parse the period code rather than trusting row order. - Census JSON is a list of lists whose first row is the header - convert to records before anything else. Geographies are FIPS codes (state and county).
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 |
9.0 KB |
bea/national/0000.tar |
71.7 KB |
bea/regional/0000.tar |
75.33 MB |
bls/flat-files/0000.tar |
1.46 GB |
bls/qcew/0000.tar |
260.61 MB |
bls/qcew/0001.tar |
323.02 MB |
Showing 6 of 267 shards.
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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