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Error code: DatasetGenerationError
Exception: ArrowNotImplementedError
Message: Cannot write struct type 'style' with no child field to Parquet. Consider adding a dummy child field.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1868, in _prepare_split_single
num_examples, num_bytes = writer.finalize()
~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 803, in finalize
self.write_rows_on_file()
~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 663, in write_rows_on_file
self._write_table(table)
~~~~~~~~~~~~~~~~~^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 790, in _write_table
self._build_writer(inferred_schema=pa_table.schema)
~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 834, in _build_writer
self.pa_writer = pq.ParquetWriter(
~~~~~~~~~~~~~~~~^
self.stream,
^^^^^^^^^^^^
...<9 lines>...
},
^^
)
^
File "/usr/local/lib/python3.14/site-packages/pyarrow/parquet/core.py", line 1115, in __init__
self.writer = _parquet.ParquetWriter(
~~~~~~~~~~~~~~~~~~~~~~^
sink, schema,
^^^^^^^^^^^^^
...<20 lines>...
max_rows_per_page=max_rows_per_page,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
**options)
^^^^^^^^^^
File "pyarrow/_parquet.pyx", line 2430, in pyarrow._parquet.ParquetWriter.__cinit__
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.ArrowNotImplementedError: Cannot write struct type 'style' with no child field to Parquet. Consider adding a dummy child field.
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 1369, 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 948, 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 1694, 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 1879, 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.
nodes list | relationships list | style dict |
|---|---|---|
[
{
"id": "n1",
"position": {
"x": 1995.6869354330668,
"y": 2302.1066500938477
},
"caption": "EVENT_01",
"labels": [
"Certain Event",
"Relevant Event"
],
"properties": {
"START": "2021",
"END": "",
"t": "2021-02-01T00:00:00",
"TotalIndustry":... | [
{
"id": "n0",
"fromId": "n1",
"toId": "n2",
"type": "isPriorTo",
"properties": {
"2025-02-28": "1.0",
"2021-01-01": null,
"2024-07-01": null,
"2021-02-01": null,
"2025-03-20": null,
"2025-03-11": null,
"2025-06-19": null,
"2025-07-01": null
},
... | {
"font-family": "sans-serif",
"background-color": "#ffffff",
"background-image": "",
"background-size": "100%",
"node-color": "#ffffff",
"border-width": 4,
"border-color": "#000000",
"radius": 50,
"node-padding": 5,
"node-margin": 2,
"outside-position": "auto",
"node-icon-image": "",
"node-ba... |
AutoScenarioQA Dataset
t_reference_questions.csv is a question-answer dataset for reasoning over the automotive-market scenario graph t_scenario_graph. It contains 680 question records across three difficulty levels that target information-extraction and reasoning tasks on the scenario graph.
What the QA Is Used For
The QA records model questions that members of a strategic foresight team may ask while developing, reviewing, or challenging automotive-industry scenarios. They can be used to evaluate a graph-query system, knowledge-graph assistant, or graph-reasoning model against expected answers and supporting evidence.
The questions cover both scenario content and graph structure. Scenario-focused questions address possible policy, market, technology, energy, and supply-chain developments and their consequences. Structure-focused questions address direct relationships between events, node labels and properties, chronological paths, branches, cycles, isolated nodes, predecessor and successor nodes, and filtered node sets.
This makes the dataset useful for testing whether graph-based QA systems distinguish plausible narrative answers from answers grounded in graph topology and node attributes.
CSV Schema
| Column | Description |
|---|---|
user_group |
Intended user role, such as Scenario Team Member. |
reference_question |
Natural-language question about the scenario graph. |
category |
Question category: Attribute-Based Filtering, Graph Navigation, Node Properties, Node and Label Identification, Path and Sequence Analysis, Relationships and Connections, Scenario-Specific Questions, Temporal Reasoning, or Topological Queries. |
level |
Difficulty level: 1, 2, or 3. |
reference_answer |
Expected answer to the question. |
reference_evidence |
Supporting graph evidence, such as node IDs, edge direction, or property names. |
all_node_ids |
Relevant node IDs where provided. |
Graph Schema and Arrows.app Example
The scenario graph used by the questions is provided in t_scenario_graph.json. The accompanying Python scripts can generate the AutoScenarioQA dataset from the supplied graph, making the dataset adaptable to other scenario-graph realizations.
The graph contains 79 nodes and 109 relationships.
Schema Elements
The graph has three top-level elements:
| Element | Purpose |
|---|---|
nodes |
Scenario containers, regional splits, events, and effects. |
relationships |
Directed links between node IDs. |
style |
Default graph display settings. |
Node Schema
Node objects use the following fields:
| Field | Meaning |
|---|---|
id |
Unique n<number> identifier used by relationships. |
position |
Graph canvas coordinates: x and y. |
caption |
Human-readable name shown in the graph. |
labels |
One or more semantic categories for the node. |
properties |
Scenario attributes and vehicle sales volume where applicable. |
style |
Optional display overrides, including node-color, border-color, and caption-color. |
The node labels present in the published graph are:
ScenarioRegion-SplitCertain EventUncertain EventLess Certain EventRelevant EventLess Relevant EventEffect
Scenario labels a scenario branch, and Region-Split assigns it to a regional branch. Event nodes can combine one certainty label (Certain Event, Uncertain Event, or Less Certain Event) with one relevance label (Relevant Event or Less Relevant Event). Effect identifies an outcome node.
Node Properties
The published graph uses the following property keys where applicable:
| Property | Meaning |
|---|---|
START |
Start date or start period. |
END |
End date or end period. |
t |
Temporal value or period. |
BEV-SHARE |
Battery-electric-vehicle share. |
PremiumSegmentWithChinesePlayers |
Premium-segment vehicle sales volume involving Chinese competitors. |
TraditionalPremiumSegment |
Traditional premium-segment vehicle sales volume. |
TotalIndustry |
Total-industry vehicle sales volume. |
Relationship Schema
Each relationship uses id, fromId, toId, type, properties, and style.
fromId is the source node and toId is the target node. The relationship types present in the published graph are:
| Type | Meaning |
|---|---|
isIn |
Places a scenario in a regional split. |
hasEvent |
Connects a scenario to an event in that scenario. |
isPriorTo |
States that the source event occurs before the target event. |
hasEffect |
Connects an event to an effect node. |
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