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Datasets:
AriaAICompany
/
lab-flow-scenarios

Tasks:
Other
Languages:
English
Persian
Size:
n<1K
Tags:
operations-research
batch-scheduling
laboratory
document-analysis
Synthetic
License:
Dataset card Files
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The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      Schema at index 1 was different: 
machine_id: string
start_min: int64
end_min: int64
reason: string
scenario: string
vs
rule_id: string
statement: string
source: string
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 244, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 4408, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2679, in _head
                  return next(iter(self.iter(batch_size=n)))
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2861, in iter
                  for key, pa_table in ex_iterable.iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2395, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 580, in _iter_arrow
                  yield new_key, pa.Table.from_batches(chunks_buffer)
                                 ~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^
                File "pyarrow/table.pxi", line 5040, in pyarrow.lib.Table.from_batches
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                  return check_status(status)
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
                  raise convert_status(status)
              pyarrow.lib.ArrowInvalid: Schema at index 1 was different: 
              machine_id: string
              start_min: int64
              end_min: int64
              reason: string
              scenario: string
              vs
              rule_id: string
              statement: string
              source: string

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LabFlow industrial laboratory scenarios

Synthetic 48-sample / 5-machine QC day used by the live LabFlow demo and the organization card.

Generated data, seed 24. Times are minutes from 07:00 local lab open. Matrix is a compatibility group, not a chemical assay certificate. Intake raw_text is synthetic. Gold extraction fields are the confirmed solver inputs.

Files

  • data/samples.csv
  • data/machines.csv
  • data/tests.csv
  • data/calibration.csv
  • data/compatibility.csv
  • data/forms.csv
  • data/gold_extraction.csv
  • data/settings.csv
  • data/units.csv
  • data/sample_preview.json

Notes

calibration rows with scenario=calibration_hold apply only in that demo scenario. Sequential instruments add test minutes inside a batch; M04 is parallel (incubator). Extraction accuracy belongs in the gold-table report, not in the schedule KPIs.

License

CC-BY-4.0. Keep the synthetic-data label.
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