README.md

---
language: en
license: apache-2.0
pretty_name: NSU Academic Concept Paths Dataset
tags:
- academic-knowledge-extraction
- concept-path-mining
- openalex
- innovation-detection
- scholarly-data
---

# NSU Academic Concept Paths Dataset

This dataset supports the research presented in:  
**"Constraint-Driven Small Language Models Based on Agent and OpenAlex Knowledge Graph: Mining Conceptual Pathways and Discovering Innovation Points in Academic Papers"**  
by Ziye Xia and Sergei S. Ospichev (2025).

It contains curated academic data from **Novosibirsk State University (NSU)**, annotated with structured concept paths and innovation points grounded in the **OpenAlex knowledge graph**.

## 📁 Files

- `train.json`: Training set (structured instruction-tuning data for the 4-stage agent pipeline)
- `val.json`: Validation set
- `test.json`: Test set (includes human-annotated innovation points)
- `concept_paths.json`: Full list of 84,181 extracted concept paths
- `innovation_annotations.json`: 1,196 expert-validated innovation points

## 📊 Statistics

| Item | Count |
|------|-------|
| Papers | 7,960 |
| Unique OpenAlex Concepts | 11,446 |
| Concept Paths | 84,181 |
| Innovation Annotations | 1,196 |
| Concept Associations (validated) | 127,203 |

## 🧠 Data Format (Example from `train.json`)

Each sample follows an instruction-tuning format:

```json
{
  "instruction": "Extract concept pairs from the research methods section.",
  "input": "<research_methods>... text ...</research_methods>",
  "output": "[[\"Physics\", \"Superconductivity\"], [\"Materials Science\", \"High-Tc materials\"]]"
}