fairdataihub/poster-sentry-training-data

PosterSentry Training Data

Human-validated 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).

Version

Version	Date	Notes
1.0.0	2026-08-18	Human-validated labels: every document was independently rated by three reviewers (Krippendorff's alpha 0.79) and the 439 contested documents were settled in a blinded adjudication review. Rows now carry document identifiers, DOIs, and label provenance. This is the corpus the published PosterSentry model is trained on.

The earlier unversioned release (April 2026) used heuristic repository labels and is superseded; it remains available in the repository history.

Dataset Description

Text extracted from real scientific documents, zero synthetic data. Every sample comes from an actual PDF downloaded from Zenodo or Figshare as part of the posters.science corpus, and every label was validated by people.

The corpus holds 3,381 documents: 1,686 posters and 1,695 non-posters (near-balanced by construction of the candidate pools, not by resampling).

Labeling

1. Survey: Three reviewers independently classified all 3,570 candidate documents (poster, non-poster, or unsure) at survey.posters.science, casting 10,710 votes with an inter-rater Krippendorff's alpha of 0.79.
2. Adjudication: The 439 documents without a unanimous panel (377 decided two to one, 62 exact ties) were adjudicated in a blinded review: each document image was re-examined in randomized order without access to the panel votes or any model output.
3. Deduplication: 182 near-duplicate documents (matching normalized 300-character text prefixes) and 7 documents with unavailable PDFs were removed, leaving the 3,381-document training corpus published here.

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

Files

File	Description	Rows
poster_sentry_train.ndjson	Human-validated training corpus (text, labels, identifiers)	3,381

Format

NDJSON (newline-delimited JSON), one document per row:

{"id": "fxd4ylwf0byrtj307b5k3kpm", "doi": "10.5281/zenodo.1234567", "source": "zenodo", "text": "TITLE: Effects of Temperature on Enzyme Kinetics ...", "label": "poster", "label_source": "unanimous_panel"}

Field	Description
id	Survey document identifier (matches the paper's supplementary files)
doi	DOI of the source repository record (Zenodo or Figshare)
source	zenodo or figshare
text	First-page text extracted with PyMuPDF, whitespace-normalized, truncated to 4,000 characters
label	poster or non_poster (human-validated)
label_source	unanimous_panel (all non-unsure votes identical) or adjudicated (settled in the blinded review)

Label Distribution

Label	Count
poster	1,686
non_poster	1,695

By provenance: 2,949 documents carry a unanimous panel label and 432 an adjudicated label (7 of the 439 adjudicated documents fell to deduplication or unavailable PDFs). Every row carries a DOI.

Related Resources

Resource	Link
PosterSentry model	fairdataihub/poster-sentry
poster-sentry	GitHub
poster-sentry-training	GitHub
poster-sentry-evaluation-paper-code	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

Load directly with HuggingFace datasets

from datasets import load_dataset

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

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, Jamey and Portillo, Dorian and Zeinali, Nahid and Soundarajan, Sanjay and Blake, Gerard and Sarin, Parth and Buttrick, Adam and Patel, Bhavesh},
  year = {2026},
  version = {1.0.0},
  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"