Datasets:

fairdataihub
/

poster-sentry-training-data

Error code:   StreamingRowsError
Exception:    ValueError
Message:      Invalid string class label poster-sentry-training-data@f7182d0c2dc2be99f5c5f268bc7e5ccd0b3d3117
Traceback:    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 2240, in __iter__
                  example = _apply_feature_types_on_example(
                            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2157, in _apply_feature_types_on_example
                  encoded_example = features.encode_example(example)
                                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 2152, in encode_example
                  return encode_nested_example(self, example)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1437, in encode_nested_example
                  {k: encode_nested_example(schema[k], obj.get(k), level=level + 1) for k in schema}
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1460, in encode_nested_example
                  return schema.encode_example(obj) if obj is not None else None
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1143, in encode_example
                  example_data = self.str2int(example_data)
                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1080, in str2int
                  output = [self._strval2int(value) for value in values]
                            ^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1101, in _strval2int
                  raise ValueError(f"Invalid string class label {value}")
              ValueError: Invalid string class label poster-sentry-training-data@f7182d0c2dc2be99f5c5f268bc7e5ccd0b3d3117

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PosterSentry Training Data

Training dataset for PosterSentry — the multimodal scientific poster classifier used in the posters.science quality control pipeline.

Developed by the FAIR Data Innovations Hub at the California Medical Innovations Institute (CalMI²).

Dataset Description

Text extracted from real scientific poster PDFs and real non-poster documents — zero synthetic data. Every sample comes from an actual PDF downloaded from Zenodo or Figshare as part of the posters.science corpus.

This is a balanced dataset: 1,803 poster samples and 1,803 non-poster samples, drawn from the source corpus described below.

Source Corpus

Sampled from a collection of 30,000+ scientific PDFs scraped from Zenodo and Figshare:

Category	Count	Selection Method
Repository-labeled posters	~28,000	Records tagged as "poster" in Zenodo/Figshare metadata
Manually confirmed non-posters	2,036	Flagged by structural classifier, then human-reviewed
Corrupt/unreadable	58	—

Non-posters include multi-page papers, conference proceedings, abstract books, newsletters, project proposals, and other documents mislabeled as "posters" in repository metadata.

Note on poster labels: The poster class is drawn from repository records self-described as posters by their uploaders. These were not individually verified by human reviewers. When PosterSentry was later applied to the full 30K corpus, approximately 20% of repository-labeled "posters" were reclassified as non-posters, suggesting meaningful label noise in the broader corpus. The balanced training subset published here was randomly sampled from the repository-labeled poster pool.

Files

File	Description	Samples
`poster_sentry_train.ndjson`	Balanced training data (text + labels)	3,606

Format

NDJSON (newline-delimited JSON) with text and label fields:

{"text": "TITLE: Effects of Temperature on Enzyme Kinetics\nAUTHORS: A. Smith...", "label": "poster"}
{"text": "Abstract. We present a novel approach to distributed computing...", "label": "non_poster"}

Label Distribution

Label	Count	Description
`poster`	1,803	Text from first page of repository-labeled single-page scientific posters
`non_poster`	1,803	Text from first page of manually confirmed non-poster documents

Classes are perfectly balanced (1:1 ratio).

Data Collection Methodology

Corpus assembly: 30K+ PDFs scraped from Zenodo and Figshare using the poster-repo-scraper, selecting records whose metadata indicated "poster"
Non-poster identification: A structural classifier using PDF features (page count, dimensions, file size) flagged 2,036 candidate non-posters, which were then manually reviewed and confirmed
Text extraction: First-page text extracted from each PDF using PyMuPDF, cleaned (whitespace normalization) and truncated to 4,000 characters
Balanced sampling: 1,803 samples randomly drawn from each class (limited by the smaller non-poster pool after feature extraction filtering)

Related Resources

Resource	Link
PosterSentry model	fairdataihub/poster-sentry
poster-sentry	GitHub
poster-sentry-training	GitHub
Llama-3.1-8B-Poster-Extraction	fairdataihub/Llama-3.1-8B-Poster-Extraction
poster2json library	PyPI · GitHub
poster-json-schema	GitHub
Platform	posters.science

Usage

Train PosterSentry from this data

pip install poster-sentry
python scripts/train_poster_sentry.py --n-per-class 2000

Load directly with HuggingFace datasets

from datasets import load_dataset

ds = load_dataset("fairdataihub/poster-sentry-training-data")
print(ds["train"][0])
# {"text": "TITLE: ...", "label": "poster"}

Use for PubGuard doc_type training

The poster texts in this dataset are also used by PubGuard to train its poster document-type classification head.

Citation

@dataset{poster_sentry_data_2026,
  title = {PosterSentry Training Data: Scientific Poster Text Corpus},
  author = {O'Neill, James and Soundarajan, Sanjay and Portillo, Dorian and Patel, Bhavesh},
  year = {2026},
  url = {https://huggingface.co/datasets/fairdataihub/poster-sentry-training-data},
  note = {Part of the posters.science initiative}
}

License

MIT License — See LICENSE for details.

Acknowledgments

FAIR Data Innovations Hub at California Medical Innovations Institute (CalMI²)
posters.science platform
HuggingFace for dataset hosting infrastructure
Funded by The Navigation Fund (10.71707/rk36-9x79) — "Poster Sharing and Discovery Made Easy"

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