Datasets:

SkyWhal3
/

PEX10_PubMed_Central_MultiModal

---
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.*

	---
	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.