Create README.md

README.md

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+---
+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\"]]"
+}