The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: ValueError
Message: Expected object or value
Traceback: Traceback (most recent call last):
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: Column() changed from object to string in row 0
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, 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 127, 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 483, 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 2840, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, 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 304, in _generate_tables
batch = json_encode_fields_in_json_lines(original_batch, json_field_paths)
File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 111, in json_encode_fields_in_json_lines
examples = [ujson_loads(line) for line in original_batch.splitlines()]
~~~~~~~~~~~^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 20, in ujson_loads
return pd.io.json.ujson_loads(*args, **kwargs)
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
ValueError: Expected object or valueNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Open-Source Scientific Documents
This dataset contains approximately 100,000 open scientific PDF documents packaged as a shared retrieval corpus. Train, validation, and test query sets are expected to reference document_id values from this single corpus rather than using separate document splits.
Sources
ACL: 20,845 PDFsBiology: 22,000 PDFsEngineering: 22,000 PDFsMedicine: 22,000 PDFsPhysics: 21,667 PDFs
The source folders preserve the project corpus identities: ACL computational linguistics papers, arXiv Physics papers, arXiv Engineering papers, PMC OA Biology papers, and PMC OA Medical/Clinical Research papers. The selection and license filtering are performed by the existing project download pipeline before packaging.
Licensing
All documents are licensed with CC0 or CC-BY, These terms permit redistribution and scientific use of the documents, with CC BY additionally requiring appropriate attribution.
Format
PDFs are stored in uncompressed TAR shards under data/<SOURCE>/shard-xxxxx.tar using a WebDataset-compatible layout. Each example contains:
DOCUMENT_ID.pdf
DOCUMENT_ID.json
The JSON sidecar includes document_id, source, original filename/path, size, SHA-256 checksum, shard location, and available bibliographic metadata such as title, year, DOI, and document-level license. The global metadata.parquet has one row per packaged PDF with:
document_id, source, original_filename, original_relative_path, shard, member_path, size_bytes, sha256, license, title, year, doi
duplicates.parquet records exact duplicate content by SHA-256. Duplicate files are preserved in the shards; duplicate rows identify the canonical and duplicate document IDs. This build records 108,512 PDFs, 108,512 unique file contents, and 0 duplicate rows.
Streaming Shards
from datasets import load_dataset
dataset = load_dataset(
"webdataset",
data_files={
"ACL": "hf://datasets/kasys/open-source-scientific-documents/data/ACL/*.tar",
},
split="ACL",
streaming=True,
)
for example in dataset:
pdf_bytes = example["pdf"]
metadata = example["json"]
break
Locate One PDF
import io
import tarfile
import pandas as pd
from huggingface_hub import hf_hub_download
repo_id = "kasys/open-source-scientific-documents"
document_id = "ACL_..."
metadata = pd.read_parquet("hf://datasets/" + repo_id + "/metadata.parquet")
row = metadata.loc[metadata.document_id == document_id].iloc[0]
shard_path = hf_hub_download(repo_id=repo_id, repo_type="dataset", filename=row.shard)
with tarfile.open(shard_path, "r") as tar:
pdf_bytes = tar.extractfile(row.member_path).read()
Limitations
Some source records have incomplete bibliographic metadata. Missing license, title, year, DOI, or author fields are left null rather than inferred. TAR shards are uncompressed because PDF files are already compressed.
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