Hugging Face's logo

Datasets:
alirezaaminzadeh
/
hospital-operations-scenarios

Tasks:
Other
Size:
1K<n<10K
Tags:
healthcare
operations-research
hospital
scheduling
Synthetic
License:
Dataset card Files
xet
 Community
Dataset Viewer
Duplicate
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
name: string
description: string
version: string
scenarios: list<item: string>
  child 0, item: string
sizes: list<item: string>
  child 0, item: string
instance_count: int64
license: string
generated_at: string
seed: int64
nurses: list<item: struct<nurse_id: string, name: string, skills: list<item: string>, shift_start: int64, sh (... 37 chars omitted)
  child 0, item: struct<nurse_id: string, name: string, skills: list<item: string>, shift_start: int64, shift_end: in (... 25 chars omitted)
      child 0, nurse_id: string
      child 1, name: string
      child 2, skills: list<item: string>
          child 0, item: string
      child 3, shift_start: int64
      child 4, shift_end: int64
      child 5, hourly_cost: double
scenario_id: string
size: string
horizon_minutes: int64
scenario_label: string
surgeries: list<item: struct<case_id: string, specialty: string, surgeon_id: string, duration_mean: int64, dura (... 323 chars omitted)
  child 0, item: struct<case_id: string, specialty: string, surgeon_id: string, duration_mean: int64, duration_p50: i (... 311 chars omitted)
      child 0, case_id: string
      child 1, specialty: string
      child 2, surgeon_id: string
      child 3, duration_mean: int64
      child 4, duration_p50: int64
      child 5, duration_p80: int64
      child 6, duration_p95: int64
      child 7, cancellation_prob: double
      child 8, icu_prob: double
      child 9, los_days_mean: double
      child 10, no_show_prob: double
      child 11, priority: int64
      child 12, emergency: bool
      child 13, asa_score: int64
      child 14, patient_age: int64
      child 15, required_skills: list<item: string>
          child 0, item: string
      child 16, turnover_min: int64
      child 17, earliest_start: int64
      child 18, latest_start: int64
beds: list<item: struct<unit_id: string, unit_type: string, capacity: int64, occupied: int64>>
  child 0, item: struct<unit_id: string, unit_type: string, capacity: int64, occupied: int64>
      child 0, unit_id: string
      child 1, unit_type: string
      child 2, capacity: int64
      child 3, occupied: int64
or_rooms: list<item: struct<room_id: string, name: string, specialty_affinity: list<item: string>, available_f (... 36 chars omitted)
  child 0, item: struct<room_id: string, name: string, specialty_affinity: list<item: string>, available_from: int64, (... 24 chars omitted)
      child 0, room_id: string
      child 1, name: string
      child 2, specialty_affinity: list<item: string>
          child 0, item: string
      child 3, available_from: int64
      child 4, available_until: int64
instance_id: string
to
{'instance_id': Value('string'), 'scenario_id': Value('string'), 'scenario_label': Value('string'), 'horizon_minutes': Value('int64'), 'seed': Value('int64'), 'size': Value('string'), 'or_rooms': List({'room_id': Value('string'), 'name': Value('string'), 'specialty_affinity': List(Value('string')), 'available_from': Value('int64'), 'available_until': Value('int64')}), 'surgeries': List({'case_id': Value('string'), 'specialty': Value('string'), 'surgeon_id': Value('string'), 'duration_mean': Value('int64'), 'duration_p50': Value('int64'), 'duration_p80': Value('int64'), 'duration_p95': Value('int64'), 'cancellation_prob': Value('float64'), 'icu_prob': Value('float64'), 'los_days_mean': Value('float64'), 'no_show_prob': Value('float64'), 'priority': Value('int64'), 'emergency': Value('bool'), 'asa_score': Value('int64'), 'patient_age': Value('int64'), 'required_skills': List(Value('string')), 'turnover_min': Value('int64'), 'earliest_start': Value('int64'), 'latest_start': Value('int64')}), 'nurses': List({'nurse_id': Value('string'), 'name': Value('string'), 'skills': List(Value('string')), 'shift_start': Value('int64'), 'shift_end': Value('int64'), 'hourly_cost': Value('float64')}), 'beds': List({'unit_id': Value('string'), 'unit_type': Value('string'), 'capacity': Value('int64'), 'occupied': Value('int64')})}
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
              name: string
              description: string
              version: string
              scenarios: list<item: string>
                child 0, item: string
              sizes: list<item: string>
                child 0, item: string
              instance_count: int64
              license: string
              generated_at: string
              seed: int64
              nurses: list<item: struct<nurse_id: string, name: string, skills: list<item: string>, shift_start: int64, sh (... 37 chars omitted)
                child 0, item: struct<nurse_id: string, name: string, skills: list<item: string>, shift_start: int64, shift_end: in (... 25 chars omitted)
                    child 0, nurse_id: string
                    child 1, name: string
                    child 2, skills: list<item: string>
                        child 0, item: string
                    child 3, shift_start: int64
                    child 4, shift_end: int64
                    child 5, hourly_cost: double
              scenario_id: string
              size: string
              horizon_minutes: int64
              scenario_label: string
              surgeries: list<item: struct<case_id: string, specialty: string, surgeon_id: string, duration_mean: int64, dura (... 323 chars omitted)
                child 0, item: struct<case_id: string, specialty: string, surgeon_id: string, duration_mean: int64, duration_p50: i (... 311 chars omitted)
                    child 0, case_id: string
                    child 1, specialty: string
                    child 2, surgeon_id: string
                    child 3, duration_mean: int64
                    child 4, duration_p50: int64
                    child 5, duration_p80: int64
                    child 6, duration_p95: int64
                    child 7, cancellation_prob: double
                    child 8, icu_prob: double
                    child 9, los_days_mean: double
                    child 10, no_show_prob: double
                    child 11, priority: int64
                    child 12, emergency: bool
                    child 13, asa_score: int64
                    child 14, patient_age: int64
                    child 15, required_skills: list<item: string>
                        child 0, item: string
                    child 16, turnover_min: int64
                    child 17, earliest_start: int64
                    child 18, latest_start: int64
              beds: list<item: struct<unit_id: string, unit_type: string, capacity: int64, occupied: int64>>
                child 0, item: struct<unit_id: string, unit_type: string, capacity: int64, occupied: int64>
                    child 0, unit_id: string
                    child 1, unit_type: string
                    child 2, capacity: int64
                    child 3, occupied: int64
              or_rooms: list<item: struct<room_id: string, name: string, specialty_affinity: list<item: string>, available_f (... 36 chars omitted)
                child 0, item: struct<room_id: string, name: string, specialty_affinity: list<item: string>, available_from: int64, (... 24 chars omitted)
                    child 0, room_id: string
                    child 1, name: string
                    child 2, specialty_affinity: list<item: string>
                        child 0, item: string
                    child 3, available_from: int64
                    child 4, available_until: int64
              instance_id: string
              to
              {'instance_id': Value('string'), 'scenario_id': Value('string'), 'scenario_label': Value('string'), 'horizon_minutes': Value('int64'), 'seed': Value('int64'), 'size': Value('string'), 'or_rooms': List({'room_id': Value('string'), 'name': Value('string'), 'specialty_affinity': List(Value('string')), 'available_from': Value('int64'), 'available_until': Value('int64')}), 'surgeries': List({'case_id': Value('string'), 'specialty': Value('string'), 'surgeon_id': Value('string'), 'duration_mean': Value('int64'), 'duration_p50': Value('int64'), 'duration_p80': Value('int64'), 'duration_p95': Value('int64'), 'cancellation_prob': Value('float64'), 'icu_prob': Value('float64'), 'los_days_mean': Value('float64'), 'no_show_prob': Value('float64'), 'priority': Value('int64'), 'emergency': Value('bool'), 'asa_score': Value('int64'), 'patient_age': Value('int64'), 'required_skills': List(Value('string')), 'turnover_min': Value('int64'), 'earliest_start': Value('int64'), 'latest_start': Value('int64')}), 'nurses': List({'nurse_id': Value('string'), 'name': Value('string'), 'skills': List(Value('string')), 'shift_start': Value('int64'), 'shift_end': Value('int64'), 'hourly_cost': Value('float64')}), 'beds': List({'unit_id': Value('string'), 'unit_type': Value('string'), 'capacity': Value('int64'), 'occupied': Value('int64')})}
              because column names don't match

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.

Hospital Operations — Scenario Dataset

Synthetic hospital operations instances covering OR scheduling, bed capacity, nurse rostering, and emergency surge.

Version: 1.0.0
Scenarios: or_daily, or_weekly, icu_beds, nurse_roster, emergency_surge
Downloads last month
31
Total file size:
359 kB

Space using alirezaaminzadeh/hospital-operations-scenarios 1

Collection including alirezaaminzadeh/hospital-operations-scenarios