Datasets:

wfzimmerman
/

us-submarine-war-patrol-reports

Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      JSON parse error: Invalid value. in row 0
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 324, in _generate_tables
                  df = pandas_read_json(f)
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 38, in pandas_read_json
                  return pd.read_json(path_or_buf, **kwargs)
                         ~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 815, in read_json
                  return json_reader.read()
                         ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1014, in read
                  obj = self._get_object_parser(self.data)
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1040, in _get_object_parser
                  obj = FrameParser(json, **kwargs).parse()
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1176, in parse
                  self._parse()
                  ~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1392, in _parse
                  ujson_loads(json, precise_float=self.precise_float), dtype=None
                  ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
              ValueError: Expected object or value
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 243, 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 4379, 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 2661, in _head
                  return next(iter(self.iter(batch_size=n)))
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2839, in iter
                  for key, pa_table in ex_iterable.iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2377, in _iter_arrow
                  yield from 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 327, in _generate_tables
                  raise e
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 290, in _generate_tables
                  pa_table = paj.read_json(
                      io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
                  )
                File "pyarrow/_json.pyx", line 342, in pyarrow._json.read_json
                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.ArrowInvalid: JSON parse error: Invalid value. in row 0

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US Submarine War Patrol Reports - WWII

Combined Hugging Face package for declassified World War II US Navy submarine war-patrol reports. It includes the raw page-level OCR corpus and derived datasets-in-disguise candidate tables extracted from that corpus.

This staged release contains:

52,886 raw OCR page rows across 251 boats.
52,886 page signal rows over the same OCR pages.
472 patrol-period candidate rows extracted from report headers and battle-star summaries.
94,551 operational event candidate rows extracted from source-grounded snippets.

The source OCR corpus is complete for the current local source inventory: 251 real unique submarine hulls after excluding the local SS-999_FICTITIOUS fixture. That is a scoped coverage claim, not an unqualified "all boats" claim. Appendix OCR files are excluded from this page-level per-boat dataset.

Dataset structure

This dataset has four Hugging Face configs:

pages (pages.jsonl) - the raw evidence layer, one record per retained OCR page. Fields are hull_number, boat_name, part, page, source_pdf, ocr_text, ocr_chars, ocr_model, and source.
page_signals (page_signals.csv) - one row per OCR page, with page provenance plus counts/scores for dates, times, coordinates, contact language, attack terms, damage terms, torpedo terms, naval vocabulary, and a patrol-log-specific structuredness verdict.
patrol_periods (patrol_periods.csv) - candidate patrol-number/date/area records extracted from patrol report headers and battle-star summary lines. Date values remain as source text to avoid false precision from OCR variants.
event_candidates (event_candidates.csv, mirrored as event_candidates.jsonl) - candidate contact, attack, sinking, gun action, depth-charge, damage, rescue, and mine-warfare snippets. Each row has an event_id, page provenance, tags, extracted date/time/coordinate/target strings when present, an evidence snippet, and a confidence score.

The extraction is deterministic and source-grounded. It is designed for triage, retrieval, and downstream adjudication; it does not claim to resolve duplicate events, normalize target identities, or replace a human-verified attack ledger.

Files

pages.jsonl - page-level OCR corpus.
page_signals.csv - page-level structured-prose and naval/action signals.
patrol_periods.csv - patrol number/date/area candidates.
event_candidates.csv and event_candidates.jsonl - operational event candidates with evidence snippets.
SCHEMA.md - extraction notes and field semantics.
dataset_stats.json - generation totals and signal distributions.

Source

Raw OCR source: NARA scans hosted via maritime.org, assembled from the local per-boat OCR inventory. OCR model provenance is recorded in pages.jsonl and preserved in page_signals.csv.

Related B5K series catalog: https://bigfivekiller.online/series/submarine-patrol-logs/

That series page links to the 48 B5K catalog pages for print or scheduled editions. Those print titles are a catalog subset, not this derived dataset's coverage denominator.

Published Hugging Face dataset: https://huggingface.co/datasets/wfzimmerman/us-submarine-war-patrol-reports

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