README.md

metadata

license: mit
language:
  - en
tags:
  - text-classification
  - scientific-posters
  - poster-detection
  - poster-sentry
  - machine-actionable
  - FAIR-data
  - posters-science
  - quality-control
  - multimodal
size_categories:
  - 1K<n<10K
task_categories:
  - text-classification

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.

Source Corpus

Sampled from a curated collection of 30,000+ classified scientific PDFs:

Category	Count	Platforms
Verified scientific posters	28,111	Zenodo, Figshare
Verified non-posters	2,036	Zenodo, Figshare
Corrupt/unreadable	58	—
Total classified	30,205	—

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

Files

File	Description	Samples
poster_sentry_train.ndjson	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 verified single-page scientific posters
non_poster	1,803	Text from first page of verified multi-page documents

Classes are perfectly balanced (1:1 ratio).

Data Collection Methodology

1. Poster corpus assembly: 30K+ PDFs scraped from Zenodo and Figshare using the poster-repo-scraper
2. Classification: A Gradient Boosting classifier using PDF structural features (page count, physical dimensions, file size) separated posters from non-posters with F1 = 1.0 on held-out data
3. Separation: 2,036 non-posters moved to a separate directory; 28,111 verified posters retained
4. Text extraction: First page text extracted from each PDF using PyMuPDF (fitz), cleaned and truncated to 4,000 characters
5. Balanced sampling: 1,803 samples per class (limited by the smaller non-poster class)

Related Resources

Resource	Link
PosterSentry model	fairdataihub/poster-sentry
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: Real 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"