The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: TypeError
Message: Couldn't cast array of type string to null
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 2840, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, 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 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2312, in cast_table_to_schema
cast_array_to_feature(
~~~~~~~~~~~~~~~~~~~~~^
table[name] if name in table_column_names else pa.array([None] * len(table), type=schema.field(name).type),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
feature,
^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1861, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
~~~~^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2068, in cast_array_to_feature
_c(array.field(name) if name in array_fields else null_array, subfeature)
~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1863, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2152, in cast_array_to_feature
return array_cast(
array,
...<2 lines>...
allow_decimal_to_str=allow_decimal_to_str,
)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1863, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2014, in array_cast
raise TypeError(f"Couldn't cast array of type {_short_str(array.type)} to {_short_str(pa_type)}")
TypeError: Couldn't cast array of type string to nullNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
scvd.store x402 endpoint readiness corpus scvd.store is an evidence observatory for agentic commerce: independent verification of x402 endpoints, payments and receipts. Before an agent pays an x402 endpoint, we check that it can be paid. After it pays, we check the signed receipt. Over time we watch endpoints and publish a dated, signed corpus. Sellers use it to prove a door works; buyers use it before spending. Every artifact is signed, expires, and names what we did not see. Not escrow, not a rating, not a guarantee.
This dataset is that corpus: one snapshot per weekly round of the public x402 discovery list, recording which hosts were listed, which answered, and what a single conformance probe saw at that moment. Hash-chained, ed25519-signed, each digest submitted to OpenTimestamps for Bitcoin anchoring. Dated observations of moments, never scores on operators.
Files File What it is corpus.json The index: schema.org Dataset metadata, every entry, the chain check, and the verification steps. 1.json … N.json One signed round each: snapshot, digest, signature, public_key, ots (the OpenTimestamps proof). tiers.json The per-host tier fractions, alphabetical, with the rule, the denominator and the rows beside each. A new numbered file is added each signed round. The live copy is always at https://scvd.store/corpus.json, and every artifact verifies free at https://scvd.store/api/verify/{id}.
Denominators Every fraction in this corpus is published with its rule, its denominator and its rows. Coverage caveats ride inside each round verbatim: which hosts were listed, which were walked, and the percentage between them. Nothing here is a ranking of one host against another.
Update cadence One round per week, taken on the Sunday walk, appended as a new numbered file and a new version on Zenodo. A week the walk did not run is a missing number, said so, never backfilled.
Limitations One probe per host per round, at indexer cadence: a door that was down for the minute of the probe reads as unreachable for the week. The population is the public x402 discovery list plus hosts the store has met; a door not listed anywhere is not here. The probe reads the 402 challenge and the signed offer; it does not pay, so it says whether a door can be paid, not whether it delivers. Tiers and fractions are derived and published with their rule, denominator and rows; they are not scores of operators and must not be read as rankings. Verification Recompute any snapshot's sha256, check the signature against the key at https://scvd.store/.well-known/scvd-signing-key, walk the previous_digest chain back to the first entry, and run ots verify on the Bitcoin-anchored proof. The exact steps, field order included, are printed on corpus.json itself.
Citation Concept DOI (all versions): https://doi.org/10.5281/zenodo.22284887
Record Creative Co. LLC (2026). scvd.store x402 endpoint readiness corpus. Zenodo. https://doi.org/10.5281/zenodo.22284887 Licence CC BY 4.0. Reuse names the original source: https://scvd.store/corpus
- Downloads last month
- 49