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README.md

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+---
+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" title="This image was generated by AI" 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.
+
+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:
+
+```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 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
+
+1. **Corpus assembly**: 30K+ PDFs scraped from Zenodo and Figshare using the [poster-repo-scraper](https://github.com/fairdataihub/poster-repo-scraper), selecting records whose metadata indicated "poster"
+2. **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
+3. **Text extraction**: First-page text extracted from each PDF using PyMuPDF, cleaned (whitespace normalization) and truncated to 4,000 characters
+4. **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](https://huggingface.co/fairdataihub/poster-sentry) |
+| **poster-sentry** | [GitHub](https://github.com/fairdataihub/poster-sentry) |
+| **poster-sentry-training** | [GitHub](https://github.com/fairdataihub/poster-sentry-training) |
+| **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: 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"