Datasets:
The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: ArrowInvalid
Message: Float value 5.500000 was truncated converting to int64
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 2303, 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 1852, 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 2143, 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 1854, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2006, in array_cast
return array.cast(pa_type)
~~~~~~~~~~^^^^^^^^^
File "pyarrow/array.pxi", line 1147, in pyarrow.lib.Array.cast
File "/usr/local/lib/python3.14/site-packages/pyarrow/compute.py", line 412, in cast
return call_function("cast", [arr], options, memory_pool)
File "pyarrow/_compute.pyx", line 604, in pyarrow._compute.call_function
File "pyarrow/_compute.pyx", line 399, in pyarrow._compute.Function.call
result = GetResultValue(
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
raise convert_status(status)
pyarrow.lib.ArrowInvalid: Float value 5.500000 was truncated converting to int64Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Earthquakes Daily — M4.5+ global snapshot
A daily snapshot of every magnitude 4.5+ earthquake in the live USGS feed, exported
through Dynamic Feed — a live, verifiable data API whose every
response is Ed25519-signed. One file per day (data/YYYY-MM-DD.jsonl), one JSON
object per earthquake per line.
- Live source: https://dynamicfeed.ai (tool:
earthquakes, endpointPOST /v1/batch) — keyless, no signup - Upstream source: USGS Earthquake Hazards Program
- Licence: US public domain (USGS, 17 USC §105) — the snapshot inherits it
- Update cadence: daily
- Row counts vary daily with global seismicity (typically 10–60 M4.5+ events/day)
File format
The first line of every file is a _meta record ({"_meta": true, ...}) carrying the
export date, the API call, upstream source + licence, freshness info, and the Ed25519
signature of the live batch response the rows were extracted from. All following lines
are earthquake rows:
| field | description |
|---|---|
magnitude, mag_type |
event magnitude and magnitude type (mww, ml, ...) |
place |
human-readable location |
latitude, longitude, depth_km |
event geometry |
alert |
USGS PAGER impact level (green→red), distinct from magnitude |
tsunami |
tsunami flag |
felt_reports |
number of "Did You Feel It?" reports |
significance |
USGS significance score (magnitude + felt + impact) |
time / measured_at |
event time (UTC, ISO 8601) |
url |
USGS event page |
retrieved_at, source, source_url, licence |
provenance |
Signature verification
Every Dynamic Feed API response is signed with Ed25519 (key id
df-ed25519-4cb32e72f333, canonicalization json-sorted-compact):
- Public key: https://dynamicfeed.ai/.well-known/keys
- Signing spec: https://dynamicfeed.ai/standard
- In-browser verifier (paste any live response, tamper-test it): https://dynamicfeed.ai/proof
The _meta line of each snapshot stores the signature object exactly as returned by the
live API, plus the SHA-256 of the raw signed response bytes, so each day's export is
anchored to a verifiable live response. To verify end-to-end, fetch the live API yourself
(snippet below) and check the signature per the spec.
Use the live API (keyless)
import json, urllib.request
req = urllib.request.Request(
"https://dynamicfeed.ai/v1/batch",
data=json.dumps({"calls": [
{"tool": "earthquakes", "args": {"min_magnitude": 4.5, "limit": 200}}
]}).encode(),
headers={"Content-Type": "application/json"},
)
resp = json.loads(urllib.request.urlopen(req).read())
quakes = resp["results"][0]["data"]["results"]
signature = resp["signature"] # Ed25519, key df-ed25519-4cb32e72f333
print(len(quakes), "earthquakes;", "signed by", signature["key_id"])
Load this dataset
import json
rows = []
with open("data/2026-06-10.jsonl") as fh:
meta = json.loads(next(fh)) # first line is the _meta record
rows = [json.loads(line) for line in fh]
print(meta["upstream_source"], "-", len(rows), "earthquakes")
(If you use datasets.load_dataset("json", ...), filter out rows where _meta is true.)
About Dynamic Feed
Dynamic Feed serves 56 live data tools (weather, CVEs, markets, space, hazards, AI model pricing, ...) with a freshness + provenance envelope on every datapoint and an Ed25519 signature on every response. Keyless MCP + REST. Source catalog: https://dynamicfeed.ai/sources
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