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: TypeError
Message: Couldn't cast array of type
struct<alarm: string, alarm_started_at: timestamp[s], alarm_cleared_at: timestamp[s], minimum_temperature_c: double, controller_setpoint_c: int64, minimum_c: int64, maximum_c: int64, setpoint_c: int64, instruction: string, location: string, status: string, carrier_note: string, request_id: string, accepted_at: timestamp[s], inspection_due_at: timestamp[s], inspected_at: timestamp[s], findings: string, recorded_at: timestamp[s]>
to
{'alarm': Value('string'), 'alarm_started_at': Value('timestamp[s]'), 'alarm_cleared_at': Value('timestamp[s]'), 'peak_temperature_c': Value('float64'), 'minimum_c': Value('int64'), 'maximum_c': Value('int64'), 'setpoint_c': Value('int64'), 'instruction': Value('string'), 'location': Value('string'), 'status': Value('string'), 'reported_power_event': Value('string'), 'request_id': Value('string'), 'accepted_at': Value('timestamp[s]'), 'inspection_due_at': Value('timestamp[s]'), 'inspected_at': Value('timestamp[s]'), 'findings': Value('string'), 'recorded_at': Value('timestamp[s]')}
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 2951, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2461, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2486, 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 547, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 430, 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 2118, in cast_array_to_feature
casted_array_values = _c(array.values, feature.feature)
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 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 2158, in cast_array_to_feature
raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")
TypeError: Couldn't cast array of type
struct<alarm: string, alarm_started_at: timestamp[s], alarm_cleared_at: timestamp[s], minimum_temperature_c: double, controller_setpoint_c: int64, minimum_c: int64, maximum_c: int64, setpoint_c: int64, instruction: string, location: string, status: string, carrier_note: string, request_id: string, accepted_at: timestamp[s], inspection_due_at: timestamp[s], inspected_at: timestamp[s], findings: string, recorded_at: timestamp[s]>
to
{'alarm': Value('string'), 'alarm_started_at': Value('timestamp[s]'), 'alarm_cleared_at': Value('timestamp[s]'), 'peak_temperature_c': Value('float64'), 'minimum_c': Value('int64'), 'maximum_c': Value('int64'), 'setpoint_c': Value('int64'), 'instruction': Value('string'), 'location': Value('string'), 'status': Value('string'), 'reported_power_event': Value('string'), 'request_id': Value('string'), 'accepted_at': Value('timestamp[s]'), 'inspection_due_at': Value('timestamp[s]'), 'inspected_at': Value('timestamp[s]'), 'findings': Value('string'), 'recorded_at': Value('timestamp[s]')}Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Reefer Container Temperature Anomaly Response Tool-Use Trajectory Dataset
Focused on temperature anomalies in reefer shipping, these trajectories record tool calls for reading temperature telemetry and alerts, checking cargo temperature requirements, querying transport node status, coordinating on-site inspections, and documenting response actions. They show how multiple information sources support anomaly assessment, excursion-duration calculation, cargo compliance decisions, and final disposition, with an emphasis on failure scenarios and the agent's decision-making and coordination process. The data is suited to multi-tool agent training, tool-use evaluation, and research into cold-chain incident response workflows.
Technical Specifications
| Field | Type | Description |
|---|---|---|
| tool_call_trace | object | Records tool names, call parameters, returned results, and call order throughout the response workflow. |
| response_actions | array | Lists, in execution order, the checks, notifications, inspections, and temperature-control actions coordinated or completed by the agent. |
| anomaly_assessment | string | Summarizes the temperature anomaly judgment and severity based on telemetry, alerts, and cargo requirements. |
| disposition_outcome | string | Records the final status of the response, recommended next steps, and whether further follow-up is required. |
| transport_node_status | object | Records transport nodes, container location, transport status, and information provided by the carrier. |
| cargo_compliance_result | string | States whether observed temperatures comply with cargo requirements and notes potential effects on the cargo. |
| inspection_and_event_records | object | Records on-site inspection requests, findings, response events, and related timestamps. |
| node_and_inspection_findings | string | Summarizes how transport status and on-site inspection findings inform anomaly localization and response. |
| cargo_temperature_requirements | object | Records the permitted temperature range, temperature-control instructions, and source of the requirements for the cargo. |
| temperature_telemetry_and_alerts | object | Records reefer container temperature readings, sampling times, equipment details, and related alerts. |
| temperature_excursion_duration_minutes | number | Records the calculated duration, based on telemetry and timestamps, for which the temperature was outside the cargo's permitted range. |
Compliance Statement
| Authorization Type | CC-BY-NC-SA 4.0 (Attribution–NonCommercial–ShareAlike) |
| Commercial Use | Requires exclusive subscription or authorization contract (monthly or per-invocation charging) |
| Privacy and Anonymization | No PII, no real company names, simulated scenarios follow industry standards |
| Compliance System | Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs |
Source & Contact
- Downloads last month
- -