Dataset Viewer
The dataset viewer is not available for this subset.
Cannot get the split names for the config 'default' of the dataset.
Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                                         ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/tsfile/tsfile.py", line 271, in _split_generators
                  scan = self._scan_metadata(all_files)
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/tsfile/tsfile.py", line 318, in _scan_metadata
                  with self._open_reader(file) as reader:
                       ~~~~~~~~~~~~~~~~~^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/tsfile/tsfile.py", line 742, in _open_reader
                  return TsFileReader(file)
                File "tsfile/tsfile_reader.pyx", line 364, in tsfile.tsfile_reader.TsFileReaderPy.__init__
                File "tsfile/tsfile_reader.pyx", line 367, in tsfile.tsfile_reader.TsFileReaderPy.init_reader
                File "tsfile/tsfile_py_cpp.pyx", line 789, in tsfile.tsfile_py_cpp.tsfile_reader_new_c
                File "tsfile/tsfile_py_cpp.pyx", line 55, in tsfile.tsfile_py_cpp.check_error
              tsfile.exceptions.FileOpenError: 28: Failed to open file
              
              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/split_names.py", line 68, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                               ~~~~~~~~~~~~~~~~~~~~~~~^
                      path=dataset,
                      ^^^^^^^^^^^^^
                      config_name=config,
                      ^^^^^^^^^^^^^^^^^^^
                      token=hf_token,
                      ^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
                  info = get_dataset_config_info(
                      path,
                  ...<6 lines>...
                      **config_kwargs,
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
                  raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
              datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.

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.

HLT-010 Synthetic Hospital Resource Usage Dataset — OR + Staffing + Equipment + Capacity (Sample Preview) (TsFile)

Apache TsFile version of xpertsystems/hlt010-sample.

Overview

A fully synthetic hospital operations dataset combining operating room schedules, staffing/workforce records, biomedical equipment utilization, daily capacity metrics, and facility master data across mixed facility types (academic / large community / medium community / critical access). Calibrated to AHA Annual Survey 2023, AORN benchmarks, NSI nursing data, ECRI Institute equipment data, and CMS Conditions of Participation.

  • Tables: 5 TsFile table(s), 35,255 rows in total.
    • equipment: 17,461 rows, 2023-01-01 00:00:00 → 2023-01-14 00:00:01.238000 (UTC)
    • facilities: 3 rows, 1970-01-01 00:00:00 → 1970-01-01 00:00:00.002000 (UTC)
    • hospital_resources: 42 rows, 2023-01-01 00:00:00 → 2023-01-14 00:00:00.002000 (UTC)
    • or_schedule: 4,226 rows, 2023-01-01 00:00:00 → 2023-01-14 00:00:00.189000 (UTC)
    • staffing: 13,523 rows, 2023-01-01 00:00:00 → 2023-01-14 00:00:00.605000 (UTC)

Schema (TsFile structure)

equipment.tsfile

  • Time (INT64, milliseconds) — the source record_date column.
  • TAG: none — the table is stored as a single device.
  • FIELD (13): asset_id (string), facility_id (string), equipment_class (string), equipment_age_yrs (double), utilization_rate (double), in_service_hours (double), downtime_hours (double), unplanned_downtime_flag (int64), downtime_cause (string), last_maintenance_date (string), next_maintenance_due (string), failure_flag (int64), repair_cost_usd (double)

facilities.tsfile

  • Time (INT64, milliseconds) — synthetic row index over the stable source order; this table has no clock column.
  • TAG: none — the table is stored as a single device.
  • FIELD (11): facility_id (string), facility_type (string), teaching_status (int64), trauma_level (string), bed_count (int64), icu_beds (int64), or_suite_count (int64), pacu_bays (int64), state (string), region (string), daily_or_capacity (double)

hospital_resources.tsfile

  • Time (INT64, milliseconds) — the source census_date column.
  • TAG: none — the table is stored as a single device.
  • FIELD (40): facility_id (string), day_of_week (string), is_weekend (int64), total_beds (int64), occupied_beds (int64), occupancy_rate (double), icu_beds_x (int64), icu_occupied (int64), icu_occupancy_rate (double), pacu_bays_x (int64), pacu_patients (int64), pacu_utilization_rate (double), ed_boarding_hours (double), diversion_flag (int64), diversion_hours (double), capacity_breach_flag (int64), surge_day_flag (int64), or_utilization_rate (double), surgical_cases_scheduled (int64), or_revenue_usd (double), or_cost_per_min_usd (double), total_or_minutes (int64), contribution_margin_usd (double), block_release_efficiency (double), staffing_adequacy_score (double), operational_efficiency_index (double), surgical_site_infection_flag (int64), near_miss_flag (int64), consent_timeout_completed (int64), equipment_safety_check_flag (int64), facility_type (string), teaching_status (int64), trauma_level (string), bed_count (int64), icu_beds_y (int64), or_suite_count (int64), pacu_bays_y (int64), state (string), region (string), daily_or_capacity (double)

or_schedule.tsfile

  • Time (INT64, milliseconds) — the source case_date column.
  • TAG: none — the table is stored as a single device.
  • FIELD (17): case_id (string), facility_id (string), or_id (string), case_type (string), surgeon_id (string), is_emergency (int64), scheduled_start_min (int64), actual_start_min (double), start_delay_min (double), first_case_ontime_flag (string), scheduled_duration_min (int64), actual_duration_min (double), turnover_time_min (double), cancellation_flag (int64), cancellation_reason (string), block_owner (string), add_on_flag (int64)

staffing.tsfile

  • Time (INT64, milliseconds) — the source shift_date column.
  • TAG: none — the table is stored as a single device.
  • FIELD (10): shift_id (string), facility_id (string), staff_id (string), staff_role (string), shift_type (string), hours_worked (int64), overtime_flag (int64), float_pool_flag (int64), agency_flag (int64), staff_to_patient_ratio (double)

Conversion notes

  • One TsFile per source CSV (5 tables), each stored as a single device: the facility id, asset id, staff id and surgeon id stay ordinary FIELDs.
  • Time = the daily reporting date of each table (record_date, census_date, case_date, shift_date) as INT64 milliseconds at 00:00 UTC. Several rows share a day, so they are ordered by a 1 ms step in stable source order; the calendar day is unaffected.
  • facilities is a 3-row facility master with no date column, so its Time is a synthetic row index. No column is dropped.

Usage

Install the Apache TsFile Python SDK (pip install tsfile) and read a converted file:

from pathlib import Path
from tsfile import TsFileReader

path = Path("equipment.tsfile")
with TsFileReader(str(path)) as reader:
    schemas = reader.get_all_table_schemas()
    print("tables:", list(schemas))
    table_name = next(iter(schemas))
    table = schemas[table_name]
    columns = [column.get_column_name() for column in table.get_columns()]
    print("columns:", columns)
    field_names = [
        column.get_column_name()
        for column in table.get_columns()
        if column.get_column_name() not in {"Time", "time"}
    ]
    if field_names:
        with reader.query_table(table_name, field_names[:3], batch_size=1024) as result:
            batch = result.read_arrow_batch()
            if batch is not None:
                print(batch.to_pandas().head())

Source & license

  • Original dataset: xpertsystems/hlt010-sample
  • Source card title / publisher: HLT-010 Synthetic Hospital Resource Usage Dataset — OR + Staffing + Equipment + Capacity (Sample Preview)
  • License: cc-by-nc-4.0 (as declared by the original dataset).
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