README.md

---
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
---

<div align="center">
  <img src="https://huggingface.co/fairdataihub/poster-sentry/resolve/main/PosterSentry.png" alt="PosterSentry Logo" width="300"/>
</div>

# PosterSentry Training Data

Training dataset for [**PosterSentry**](https://huggingface.co/fairdataihub/poster-sentry) — the multimodal scientific poster classifier used in the [posters.science](https://posters.science) quality control pipeline.

Developed by the [**FAIR Data Innovations Hub**](https://fairdataihub.org/) 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:

```json
{"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](https://github.com/fairdataihub/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](https://huggingface.co/fairdataihub/poster-sentry) |
| **Llama-3.1-8B-Poster-Extraction** | [fairdataihub/Llama-3.1-8B-Poster-Extraction](https://huggingface.co/fairdataihub/Llama-3.1-8B-Poster-Extraction) |
| **poster2json library** | [PyPI](https://pypi.org/project/poster2json/) · [GitHub](https://github.com/fairdataihub/poster2json) |
| **poster-json-schema** | [GitHub](https://github.com/fairdataihub/poster-json-schema) |
| **Platform** | [posters.science](https://posters.science) |

## Usage

### Train PosterSentry from this data

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

### Load directly with HuggingFace datasets

```python
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](https://huggingface.co/jimnoneill/pubguard-classifier) to train its `poster` document-type classification head.

## Citation

```bibtex
@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](https://opensource.org/licenses/MIT) for details.

## Acknowledgments

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