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: CastError
Message: Couldn't cast
scenario_id: string
scenario_label: string
industry: string
disruption_id: string
service_level_target: double
horizon_days: int64
budget_limit: double
description: string
nodes: list<item: struct<node_id: string, name: string, node_type: string, region: string, capacity: int64, (... 117 chars omitted)
child 0, item: struct<node_id: string, name: string, node_type: string, region: string, capacity: int64, fixed_cost (... 105 chars omitted)
child 0, node_id: string
child 1, name: string
child 2, node_type: string
child 3, region: string
child 4, capacity: int64
child 5, fixed_cost: int64
child 6, unit_cost: double
child 7, latitude: double
child 8, longitude: double
child 9, is_critical: bool
child 10, availability: double
arcs: list<item: struct<arc_id: string, source_id: string, target_id: string, product_id: string, unit_cos (... 71 chars omitted)
child 0, item: struct<arc_id: string, source_id: string, target_id: string, product_id: string, unit_cost: double, (... 59 chars omitted)
child 0, arc_id: string
child 1, source_id: string
child 2, target_id: string
child 3, product_id: string
child 4, unit_cost: double
child 5, lead_time_days: double
child 6, capacity: double
child 7, is_primary: bool
products: list<item: struct<product_id: string, name: string, category: string, demand_mean: double, demand_st (... 53 chars omitted)
child 0, item: struct<produc
...
uct<type: stri (... 351 chars omitted)
child 0, type: string
child 1, required: list<item: string>
child 0, item: string
child 2, properties: struct<scenario_id: struct<type: string>, label: struct<type: string>, probability: struct<type: str (... 287 chars omitted)
child 0, scenario_id: struct<type: string>
child 0, type: string
child 1, label: struct<type: string>
child 0, type: string
child 2, probability: struct<type: string>
child 0, type: string
child 3, demand_factor: struct<type: string>
child 0, type: string
child 4, capacity_factor: struct<type: string>
child 0, type: string
child 5, transport_factor: struct<type: string>
child 0, type: string
child 6, cost_factor: struct<type: string>
child 0, type: string
child 7, blocked_nodes: struct<type: string, items: struct<type: string>>
child 0, type: string
child 1, items: struct<type: string>
child 0, type: string
child 8, blocked_arcs: struct<type: string, items: struct<type: string>>
child 0, type: string
child 1, items: struct<type: string>
child 0, type: string
required: list<item: string>
child 0, item: string
to
{'$schema': Value('string'), 'title': Value('string'), 'type': Value('string'), 'required': List(Value('string')), 'properties': {'scenario_id': {'type': Value('string')}, 'scenario_label': {'type': Value('string')}, 'industry': {'type': Value('string')}, 'disruption_id': {'type': Value('string')}, 'service_level_target': {'type': Value('string'), 'minimum': Value('int64'), 'maximum': Value('int64')}, 'horizon_days': {'type': Value('string')}, 'budget_limit': {'type': List(Value('string'))}, 'nodes': {'type': Value('string'), 'items': {'type': Value('string'), 'required': List(Value('string')), 'properties': {'node_id': {'type': Value('string')}, 'name': {'type': Value('string')}, 'node_type': {'enum': List(Value('string'))}, 'region': {'type': Value('string')}, 'capacity': {'type': Value('string')}, 'unit_cost': {'type': Value('string')}, 'is_critical': {'type': Value('string')}}}}, 'arcs': {'type': Value('string'), 'items': {'type': Value('string'), 'required': List(Value('string')), 'properties': {'arc_id': {'type': Value('string')}, 'source_id': {'type': Value('string')}, 'target_id': {'type': Value('string')}, 'product_id': {'type': Value('string')}, 'unit_cost': {'type': Value('string')}, 'lead_time_days': {'type': Value('string')}, 'capacity': {'type': Value('string')}}}}, 'products': {'type': Value('string'), 'items': {'type': Value('string'), 'required': List(Value('string')), 'properties': {'product_id': {'type': Value('string')}, 'name': {'type': Value('string')}, 'category': {'type': Value('string')}, 'demand_mean': {'type': Value('string')}, 'demand_std': {'type': Value('string')}, 'unit_value': {'type': Value('string')}}}}, 'scenarios': {'type': Value('string'), 'items': {'type': Value('string'), 'required': List(Value('string')), 'properties': {'scenario_id': {'type': Value('string')}, 'label': {'type': Value('string')}, 'probability': {'type': Value('string')}, 'demand_factor': {'type': Value('string')}, 'capacity_factor': {'type': Value('string')}, 'transport_factor': {'type': Value('string')}, 'cost_factor': {'type': Value('string')}, 'blocked_nodes': {'type': Value('string'), 'items': {'type': Value('string')}}, 'blocked_arcs': {'type': Value('string'), 'items': {'type': Value('string')}}}}}}}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 149, 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 129, 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 489, 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 2297, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
scenario_id: string
scenario_label: string
industry: string
disruption_id: string
service_level_target: double
horizon_days: int64
budget_limit: double
description: string
nodes: list<item: struct<node_id: string, name: string, node_type: string, region: string, capacity: int64, (... 117 chars omitted)
child 0, item: struct<node_id: string, name: string, node_type: string, region: string, capacity: int64, fixed_cost (... 105 chars omitted)
child 0, node_id: string
child 1, name: string
child 2, node_type: string
child 3, region: string
child 4, capacity: int64
child 5, fixed_cost: int64
child 6, unit_cost: double
child 7, latitude: double
child 8, longitude: double
child 9, is_critical: bool
child 10, availability: double
arcs: list<item: struct<arc_id: string, source_id: string, target_id: string, product_id: string, unit_cos (... 71 chars omitted)
child 0, item: struct<arc_id: string, source_id: string, target_id: string, product_id: string, unit_cost: double, (... 59 chars omitted)
child 0, arc_id: string
child 1, source_id: string
child 2, target_id: string
child 3, product_id: string
child 4, unit_cost: double
child 5, lead_time_days: double
child 6, capacity: double
child 7, is_primary: bool
products: list<item: struct<product_id: string, name: string, category: string, demand_mean: double, demand_st (... 53 chars omitted)
child 0, item: struct<produc
...
uct<type: stri (... 351 chars omitted)
child 0, type: string
child 1, required: list<item: string>
child 0, item: string
child 2, properties: struct<scenario_id: struct<type: string>, label: struct<type: string>, probability: struct<type: str (... 287 chars omitted)
child 0, scenario_id: struct<type: string>
child 0, type: string
child 1, label: struct<type: string>
child 0, type: string
child 2, probability: struct<type: string>
child 0, type: string
child 3, demand_factor: struct<type: string>
child 0, type: string
child 4, capacity_factor: struct<type: string>
child 0, type: string
child 5, transport_factor: struct<type: string>
child 0, type: string
child 6, cost_factor: struct<type: string>
child 0, type: string
child 7, blocked_nodes: struct<type: string, items: struct<type: string>>
child 0, type: string
child 1, items: struct<type: string>
child 0, type: string
child 8, blocked_arcs: struct<type: string, items: struct<type: string>>
child 0, type: string
child 1, items: struct<type: string>
child 0, type: string
required: list<item: string>
child 0, item: string
to
{'$schema': Value('string'), 'title': Value('string'), 'type': Value('string'), 'required': List(Value('string')), 'properties': {'scenario_id': {'type': Value('string')}, 'scenario_label': {'type': Value('string')}, 'industry': {'type': Value('string')}, 'disruption_id': {'type': Value('string')}, 'service_level_target': {'type': Value('string'), 'minimum': Value('int64'), 'maximum': Value('int64')}, 'horizon_days': {'type': Value('string')}, 'budget_limit': {'type': List(Value('string'))}, 'nodes': {'type': Value('string'), 'items': {'type': Value('string'), 'required': List(Value('string')), 'properties': {'node_id': {'type': Value('string')}, 'name': {'type': Value('string')}, 'node_type': {'enum': List(Value('string'))}, 'region': {'type': Value('string')}, 'capacity': {'type': Value('string')}, 'unit_cost': {'type': Value('string')}, 'is_critical': {'type': Value('string')}}}}, 'arcs': {'type': Value('string'), 'items': {'type': Value('string'), 'required': List(Value('string')), 'properties': {'arc_id': {'type': Value('string')}, 'source_id': {'type': Value('string')}, 'target_id': {'type': Value('string')}, 'product_id': {'type': Value('string')}, 'unit_cost': {'type': Value('string')}, 'lead_time_days': {'type': Value('string')}, 'capacity': {'type': Value('string')}}}}, 'products': {'type': Value('string'), 'items': {'type': Value('string'), 'required': List(Value('string')), 'properties': {'product_id': {'type': Value('string')}, 'name': {'type': Value('string')}, 'category': {'type': Value('string')}, 'demand_mean': {'type': Value('string')}, 'demand_std': {'type': Value('string')}, 'unit_value': {'type': Value('string')}}}}, 'scenarios': {'type': Value('string'), 'items': {'type': Value('string'), 'required': List(Value('string')), 'properties': {'scenario_id': {'type': Value('string')}, 'label': {'type': Value('string')}, 'probability': {'type': Value('string')}, 'demand_factor': {'type': Value('string')}, 'capacity_factor': {'type': Value('string')}, 'transport_factor': {'type': Value('string')}, 'cost_factor': {'type': Value('string')}, 'blocked_nodes': {'type': Value('string'), 'items': {'type': Value('string')}}, 'blocked_arcs': {'type': Value('string'), 'items': {'type': Value('string')}}}}}}}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
ResilOpt Sample Supply Chain Networks
Synthetic multi-echelon supply chain network instances for the ResilOpt — Resilient Supply Chain Optimizer demo by Aria AI.
Contents
| File | Description |
|---|---|
network_schema.json |
JSON schema for network problem instances |
sample_manufacturing.json |
Manufacturing industry network (5 products, 12 nodes) |
sample_pharma.json |
Pharmaceutical supply chain with cold-chain constraints |
Network Structure
Suppliers → Plants → Distribution Centers → Customers
Each instance includes:
- Nodes (suppliers, plants, DCs, customers) with capacity and cost
- Arcs with unit cost, lead time, and flow capacity
- Products with demand distributions
- 9 disruption scenarios with probabilities
Usage
import json
with open("sample_manufacturing.json") as f:
network = json.load(f)
Links
License
Apache 2.0
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