---
license: cc-by-4.0
task_categories:
  - image-to-text
  - visual-question-answering
language:
  - en
tags:
  - medical
  - biology
  - rare-disease
  - PEX10
  - peroxisome
  - Zellweger
  - peroxisome-biogenesis-disorder
  - multimodal
  - vision-language
  - LLaVA
  - figure-caption
pretty_name: PEX10 PubMed Central Multimodal Dataset
size_categories:
  - 1K<n<10K
---

# 🧬🔬 PEX10 PubMed Central Multimodal Dataset

**A vision–language dataset of peroxisome-biology figures paired with their published captions — for training and evaluating multimodal models on PEX10 and peroxisome-biogenesis-disorder literature.**

Figure ↔ caption pairs in **LLaVA format**, extracted from **PEX10-related PubMed Central Open Access (CC-BY)** articles. **2,985 figure images** become **~9,700 multimodal training records** across four complementary task formats — plain captioning, context-grounded description, visual Q&A, and multi-figure article summaries.

> 🔬 Built to give vision-language models *eyes* for peroxisome biology: cryo-EM structures, RING-finger ubiquitin-ligase schematics, protein-import assay panels, patient-variant figures, and the rest of the visual vocabulary of PEX10 research.

---

## 📦 What's Inside

| Record type | File | What the model learns |
| ----------- | ---- | --------------------- |
| **Caption** | `figure_caption.json` | `<image>` → its published caption |
| **Detailed** | `figure_detailed.json` | `<image>` + article title/abstract context → caption |
| **Visual Q&A** | `figure_qa.json` | multi-turn: figure type → description → source article |
| **Multi-figure** | `article_multiimage.json` | several figures from one paper → combined analysis |
| **Combined** | `combined_training.json` | shuffled union of all of the above *(main training file)* |

---

## 📊 Dataset Statistics

| Metric | Value |
| ------ | ----- |
| **Figure images** | 2,985 |
| **Caption pairs** | 2,985 |
| **Detailed (context) pairs** | 2,985 |
| **Visual Q&A items** | 2,985 |
| **Multi-figure items** | 724 |
| **Combined training records** | 9,679 |
| **Format** | LLaVA (conversations JSON + images) |
| **Source** | PubMed Central Open Access subset (CC-BY) |
| **Domain** | PEX10 / peroxisome biogenesis disorders |
| **Language** | English |
| **License** | CC-BY-4.0 |

---

## 🎯 Purpose

This dataset teaches multimodal models to **read and reason about peroxisome-biology figures** — the structures, assays, pathways, and patient data that define PEX10 research.

Use it to:

- **Fine-tune vision-language models** (LLaVA-style and compatible architectures) on scientific figure understanding in a focused biomedical domain.
- **Build visual question-answering** over peroxisome / PEX10 literature — "what does this figure show?", "what type of assay is this?", "which paper is this from?".
- **Ground multimodal rare-disease tools** (e.g. ARIA-style variant-analysis assistants) so they can interpret figures, not just text.
- **Benchmark** a model's grasp of cryo-EM panels, RING-finger ligase schematics, import-assay readouts, and variant figures.

It pairs naturally with the text-side STXBP1/PEX10 RAG resources for retrieval-augmented multimodal pipelines.

---

## 🧾 Record Format

Standard **LLaVA conversation schema**. Single-image example:

```json
{
  "id": "PMC4022878_f1",
  "image": "images/PMC4022878-f1.png",
  "conversations": [
    {"from": "human", "value": "<image>\nDescribe this scientific figure in detail."},
    {"from": "gpt",   "value": "Figure 1. Cryo-EM structure of the ligase complex ..."}
  ]
}
```

Multi-figure records use an `"image"` **list** with one `<image>` tag per figure. Image files
live in `images/` and are referenced by each record's `"image"` field (`{pmcid}-f{N}.png`).

---

## 🚀 Quick Start

```python
import json

# Load the main training file
data = json.load(open("combined_training.json", encoding="utf-8"))
print(f"{len(data):,} records")

rec = data[0]
print("id:   ", rec["id"])
print("image:", rec["image"])
print("caption:", rec["conversations"][-1]["value"][:200])

# Or load a single task split, e.g. plain captioning:
captions = json.load(open("figure_caption.json", encoding="utf-8"))
```

For LLaVA-style fine-tuning, point your data loader at `combined_training.json` and your
image root at `images/`.

---

## 📁 Files

| File | Description |
| ---- | ----------- |
| `figure_caption.json` | image → caption |
| `figure_detailed.json` | image + title/abstract context → caption |
| `figure_qa.json` | multi-turn visual Q&A |
| `article_multiimage.json` | multi-figure article summaries |
| `combined_training.json` | shuffled union *(main training file)* |
| `training_metadata.json` | generation config + record counts |
| `images/` | figure images, `{pmcid}-f{N}.png` |

---

## 📜 License & Attribution

Figures are drawn from the **PubMed Central Open Access subset** under **Creative Commons
Attribution (CC-BY)** licenses; this compilation is released under **CC-BY-4.0**. Every
figure traces back to its source article through the **PMC ID** embedded in its filename
(`{pmcid}` in `{pmcid}-f{N}.png`) — please **credit the original authors and publications**
when you use their figures. Only open-access, CC-BY (or compatible) content is included.

---

## 🔗 Related Resources

| Resource | Link |
| -------- | ---- |
| **STXBP1 Multimodal Dataset** | https://huggingface.co/datasets/SkyWhal3/STXBP1_PubMed_Central_Multimodal_Dataset |
| **STXBP1 RAG (Nemotron)** | https://huggingface.co/datasets/SkyWhal3/STXBP1-RAG-Nemotron |
| **STXBP1 RAG (BGE, CPU)** | https://huggingface.co/datasets/SkyWhal3/STXBP1-RAG-Database |

---

## 🙏 Acknowledgments

Built for the peroxisome / PEX10 rare-disease research community — and for the families and
researchers working toward treatments. Figures © their respective authors, shared under
CC-BY via the PubMed Central Open Access subset.

---

*Built for rare-disease multimodal research.*