Update README.md

README.md

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----
-license: cc-by-4.0
----
+---
+license: cc-by-4.0
+task_categories:
+- image-to-text
+- visual-question-answering
+- text-generation
+- question-answering
+language:
+- en
+tags:
+- multimodal
+- vision-language
+- medical
+- biomedical
+- neuroscience
+- epilepsy
+- rare-disease
+- STXBP1
+- STXBP2
+- Munc18
+- Munc18-1
+- syntaxin-binding-protein
+- CRISPR
+- CRISPR-Cas9
+- gene-therapy
+- ene-editing
+- base-editing
+- prime-editing
+- AAV
+- PubMed
+- PMC
+- full-text
+- research
+- encephalopathy
+- developmental-delay
+- LLaVA
+- scientific-figures
+pretty_name: STXBP1 PubMed Central Multimodal Dataset
+size_categories:
+- 10K<n<100K
+---
+
+## Dataset Description
+
+A comprehensive **multimodal** collection of ~31,500 scientific articles from PubMed Central (PMC) with **175,000+ figures and images**. This dataset pairs full-text articles with their associated scientific figures, making it one of the most complete publicly available multimodal resources for STXBP1 and gene therapy research.
+
+### Why Multimodal Matters
+
+Scientific understanding often depends on visual data—protein structures, experimental results, pathway diagrams, microscopy images. This dataset preserves the connection between article text and figures, enabling:
+
+- **Vision-language model training** on scientific content
+- **Figure-to-caption learning** for scientific image understanding
+- **Multimodal RAG systems** that can reason over both text and images
+- **Automated scientific figure analysis**
+
+### About STXBP1
+
+STXBP1 (also known as Munc18-1) encodes a protein essential for neurotransmitter release. Mutations cause **STXBP1 Encephalopathy**, a rare neurological disorder (~1 in 30,000 births) characterized by early-onset epilepsy, developmental delays, and movement disorders.
+
+---
+
+## Dataset Statistics
+
+| Metric                          | Count |
+|---------------------------------|-------|
+| **Total Articles**              | 31,585 |
+| **Articles with Images**        | 30,139 (95.4%) |
+| **Total Images**                | 175,404 |
+| **Average Images per Article**  | 5.5 |
+| **Image References in Text**    | 3,253,705 |
+| **Image-Text Match Rate**       | 100.00% |
+| **Total Dataset Size**          | ~52 GB |
+
+---
+
+## Dataset Structure
+
+```
+stxbp1-pubmed-multimodal/
+├── multimodal_data/           # Article JSONs with image references
+│   ├── PMC10000387_multimodal.json
+│   ├── PMC10002385_multimodal.json
+│   └── ... (31,585 files)
+├── images/                    # All figure images
+│   ├── PMC10000387-Fig1.png
+│   ├── PMC10000387-Fig2.png
+│   └── ... (175,404 files)
+├── training_llava.json        # LLaVA format training data
+├── training_conversational.json   # Chat format training data
+├── training_simple.json       # Simple text format
+└── README.md
+```
+
+---
+
+## Data Formats
+
+### Multimodal JSON Structure
+
+Each article JSON contains:
+
+```json
+{
+  "pmc_id": "PMC24456",
+  "title": "Article title...",
+  "abstract": "Abstract text...",
+  "main_text": "Full text with <image>PMC24456-F1.png</image> inline references...",
+  "images": [
+    "PMC24456-F1.png",
+    "PMC24456-F2.png"
+  ],
+  "image_mapping": {
+    "Figure 1": "PMC24456-F1.png",
+    "Fig 1": "PMC24456-F1.png",
+    "F1": "PMC24456-F1.png"
+  },
+  "metadata": {
+    "authors": ["Author One", "Author Two"],
+    "journal": "Journal Name",
+    "doi": "10.xxxx/xxxxx",
+    "pmid": "12345678",
+    "publication_date": "2024 Jan 15",
+    "stxbp1_count": 5,
+    "munc18_count": 2
+  }
+}
+```
+
+### Pre-formatted Training Files
+
+Ready-to-use training files are included:
+
+#### 1. LLaVA Format (`training_llava.json`)
+For vision-language models (LLaVA, BLIP-2, etc.)
+
+```json
+{
+  "id": "PMC10000387",
+  "image": ["PMC10000387-Fig1.png", "PMC10000387-Fig2.png"],
+  "conversations": [
+    {"from": "human", "value": "<image>\nWhat does this scientific article discuss?"},
+    {"from": "gpt", "value": "Article title and full text..."}
+  ]
+}
+```
+
+#### 2. Conversational Format (`training_conversational.json`)
+For chat models (Llama, Mistral, etc.)
+
+```json
+{
+  "conversations": [
+    {"from": "human", "value": "Analyze this scientific article about Neurology."},
+    {"from": "assistant", "value": "Article title and full text..."}
+  ]
+}
+```
+
+#### 3. Simple Format (`training_simple.json`)
+For base LLM training or embeddings
+
+```json
+{
+  "text": "Article title and full text..."
+}
+```
+
+---
+
+## Usage Examples
+
+### Loading with Hugging Face Datasets
+
+```python
+from datasets import load_dataset
+
+# Load the full dataset
+dataset = load_dataset("SkyWhal3/stxbp1-pubmed-multimodal")
+```
+
+### Loading Images with Articles
+
+```python
+import json
+from PIL import Image
+from pathlib import Path
+
+# Load an article
+with open("multimodal_data/PMC24456_multimodal.json") as f:
+    article = json.load(f)
+
+# Load associated images
+for img_name in article['images']:
+    img_path = Path("images") / img_name
+    if img_path.exists():
+        img = Image.open(img_path)
+        print(f"{img_name}: {img.size}")
+```
+
+### Filtering High-Relevance Articles
+
+```python
+import json
+from pathlib import Path
+
+# Find articles with 5+ STXBP1 mentions
+high_relevance = []
+for json_file in Path("multimodal_data").glob("*.json"):
+    with open(json_file) as f:
+        data = json.load(f)
+    if data.get('metadata', {}).get('stxbp1_count', 0) >= 5:
+        high_relevance.append(data)
+
+print(f"Found {len(high_relevance)} high-relevance articles")
+```
+
+### Fine-tuning LLaVA
+
+```python
+# The training_llava.json is ready to use with standard LLaVA fine-tuning
+# See: https://github.com/haotian-liu/LLaVA
+
+# Example training command:
+# python llava/train/train.py \
+#     --data_path training_llava.json \
+#     --image_folder images/ \
+#     --model_name_or_path liuhaotian/llava-v1.5-7b \
+#     ...
+```
+
+---
+
+## Search Terms Used
+
+This dataset was collected using comprehensive PMC full-text search:
+
+**STXBP Family Terms:**
+- `STXBP1`, `STXBP2`, `STXBP3`, `STXBP4`, `STXBP5`, `STXBP6`, `STXBP11`
+- `Munc18`, `Munc18-1`, `Munc-18`, `Munc 18`
+- `Munc18 syntaxin`, `syntaxin binding protein`
+
+**CRISPR/Gene Therapy Terms:**
+- `CRISPR Cas9`, `CRISPR-Cas9`, `CRISPR/Cas9`
+- `CRISPR Cas12`, `CRISPR-Cas12`, `CRISPR/Cas12`
+- `CRISPR Cas13`, `CRISPR-Cas13`
+- `base editing`, `prime editing`
+
+**Combination Searches:**
+- STXBP1/Munc18/syntaxin binding protein + CRISPR/gene therapy/AAV/antisense
+
+---
+
+## Data Quality
+
+### Image-Text Alignment Audit
+
+We performed comprehensive validation of image references:
+
+| Check | Result |
+|-------|--------|
+| Total image references in text | 3,253,824 |
+| Successfully matched to files | 3,253,705 |
+| Unmatched references | 119 (removed) |
+| **Match rate** | **99.996%** |
+
+The 119 mismatched references (from 3 articles) were removed to ensure clean training data.
+
+### Filtering Recommendations
+
+| Filter | Description | Use Case |
+|--------|-------------|----------|
+| `stxbp1_count >= 1` | Mentions STXBP1 at least once | General STXBP1 research |
+| `stxbp1_count >= 5` | Substantial STXBP1 discussion | Core STXBP1 papers |
+| `munc18_count >= 1` | Uses Munc18 nomenclature | Older literature |
+| `stxbp1_count == 0 AND munc18_count == 0` | No direct mentions | CRISPR methodology papers |
+
+---
+
+## Limitations
+
+### What This Dataset Contains
+- ✅ Full article text with inline image references
+- ✅ 175,000+ scientific figures (PNG format)
+- ✅ Structured image-to-text mapping
+- ✅ Pre-formatted training files (LLaVA, conversational, simple)
+- ✅ Rich metadata (authors, DOIs, journals, dates)
+- ✅ Relevance scoring (STXBP1/Munc18 mention counts)
+
+### What This Dataset Does NOT Contain
+- ❌ **Supplementary data files** (Excel, raw data, etc.)
+- ❌ **Video content** (some articles may reference videos)
+- ❌ **Interactive figures** (3D viewers, etc.)
+- ❌ **Table structure** (tables are linearized text)
+- ❌ **LaTeX equations** (flattened to text)
+
+### Image Considerations
+- All images are PNG format (converted from original TIF/JPG/etc.)
+- Some complex multi-panel figures may be single images
+- Figure quality varies by source journal
+- Inline `<image>` tags in text mark where figures appear
+
+---
+
+## Ethical Considerations
+
+- All articles are from the **PMC Open Access Subset** under various open licenses
+- This dataset is intended for **research and educational purposes**
+- Users should cite original articles when using specific findings
+- This dataset should not replace professional medical advice
+- Patient data in case studies has been de-identified by original authors
+
+---
+
+## Citation
+
+```bibtex
+@dataset{stxbp1_pubmed_multimodal_2025,
+  title={STXBP1 PubMed Central Multimodal Dataset},
+  author={SkyWhal3},
+  year={2025},
+  publisher={Hugging Face},
+  url={https://huggingface.co/datasets/SkyWhal3/stxbp1-pubmed-multimodal},
+  note={31,585 articles with 175,404 scientific figures}
+}
+```
+
+---
+
+## Related Datasets
+
+| Dataset | Description |
+|---------|-------------|
+| [SkyWhal3/stxbp1-pubmed-central-fulltext](https://huggingface.co/datasets/SkyWhal3/stxbp1-pubmed-central-fulltext) | Text-only version (1.3 GB) - faster to download |
+
+---
+
+## Acknowledgments
+
+This dataset was curated by a parent advocate in the STXBP1 community to accelerate research into treatments and potential cures for STXBP1-related disorders.
+
+Special thanks to:
+- The researchers whose work is represented in this dataset
+- PubMed Central for providing open access to scientific literature
+- The STXBP1 Foundation and patient advocacy community
+- ClaudeCode Opus 4.5
+
+---
+
+## Version History
+
+| Version | Date | Changes |
+|---------|------|---------|
+| 1.0.0 | 2025-06 | Initial release with 31,585 articles and 175,404 images |
+
+---
+
+## Contact
+
+For questions, corrections, or contributions, please open an issue on this repository.
+
+---
+
+*This dataset is dedicated to all children and families affected by STXBP1 Encephalopathy.*
+
+*"The cure is in the data."*