Dataset Viewer
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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:    ArrowInvalid
Message:      JSON parse error: Column(/abstract_inverted_index/The) was specified twice in row 3
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 280, in _generate_tables
                  df = pandas_read_json(f)
                       ^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 34, in pandas_read_json
                  return pd.read_json(path_or_buf, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/pandas/io/json/_json.py", line 815, in read_json
                  return json_reader.read()
                         ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/pandas/io/json/_json.py", line 1014, in read
                  obj = self._get_object_parser(self.data)
                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/pandas/io/json/_json.py", line 1040, in _get_object_parser
                  obj = FrameParser(json, **kwargs).parse()
                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/pandas/io/json/_json.py", line 1176, in parse
                  self._parse()
                File "/usr/local/lib/python3.12/site-packages/pandas/io/json/_json.py", line 1392, in _parse
                  ujson_loads(json, precise_float=self.precise_float), dtype=None
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
              ValueError: Trailing data
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                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 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2227, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2251, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 494, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 384, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 283, in _generate_tables
                  raise e
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 246, in _generate_tables
                  pa_table = paj.read_json(
                             ^^^^^^^^^^^^^^
                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
              pyarrow.lib.ArrowInvalid: JSON parse error: Column(/abstract_inverted_index/The) was specified twice in row 3

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.

OpenAlex Snapshot

Mirror of the OpenAlex scholarly metadata snapshot — a free, open catalogue of 250M+ scholarly works, 100M+ authors, and related entities.

Hosted on HuggingFace via Xet for content-addressable deduplication.

Source: s3://openalex (public, anonymous S3 bucket)

Dataset subsets

Each entity type is a separate subset (config):

Config Description Shards
works Scholarly works (papers, datasets, etc.) 2,127
authors Authors of scholarly works 738
institutions Universities, research orgs 61
publishers Academic publishers 51
sources Journals, repositories, conferences 42
awards Grant/funding awards 20
concepts Legacy concept taxonomy (Wikidata) 3
topics Topic taxonomy 1
domains Top-level topic domains 1
fields Topic fields 1
subfields Topic subfields 1
funders Funding organisations 1

Data format

Each shard is a gzip-compressed JSON Lines file at:

data/{entity}/updated_date=YYYY-MM-DD/part_XXXX.jsonl.gz

The .jsonl.gz extension allows the HuggingFace dataset viewer to detect the inner format automatically. On S3, files are named part_XXXX.gz; the download pipeline renames them on save.

Each line is a JSON object representing one entity record. Fields vary by entity type. See the OpenAlex data model for field definitions.

Example: Work record fields

id, doi, title, display_name, publication_year, type, language, authorships, concepts, topics, keywords, cited_by_count, referenced_works, related_works, locations, open_access, funders, awards, mesh, sustainable_development_goals, counts_by_year, updated_date, and more.

Sync and extraction pipeline

The sync/ directory contains a Python pipeline for downloading from S3 and extracting relationship tables to Parquet:

  1. Download: python3 -m sync.download sync [--entity X] — syncs from s3://openalex (public, anonymous)
  2. Extract: python3 -m sync.extract extract [--entity X] --workers 6 — converts .jsonl.gz to nested parquet sub-tables

Extraction supports --slice-index N --slice-total M to partition work across machines.

Adding new entity types

OpenAlex occasionally adds new entities. To support a new one:

  1. Download: python3 -m sync.download sync --entity {entity}
  2. Schema: Add entity + relationship schemas to sync/schemas.py
  3. Extraction: Add dispatch entry to _ENTITY_DISPATCH in sync/extract.py
  4. Nesting: Add singular→plural entry to _ENTITY_SINGULAR_TO_PLURAL in sync/common.py
  5. Commit: Stage and push

License

OpenAlex data is released under CC0 1.0 Universal. See the OpenAlex terms for details.

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