# 🧪 Lit2Vec Dataset

**Lit2Vec** is a large-scale, openly licensed dataset of **582,724 chemistry-specific research articles** derived from the Semantic Scholar Open Research Corpus (S2ORC), curated to support semantic search, retrieval-augmented generation (RAG), and scientific NLP tasks in the chemistry domain.

Each article is distributed as a **standalone JSON file** containing:

* Clean full text
* Paragraph-level embeddings (1024-D vectors)
* TL;DR summaries
* Subfield classification scores
* Structured metadata
* Licensing information from Unpaywall, Crossref, and OpenAlex

---

## 📁 Repository Structure

```
Lit2Vec-dataset/
├── data/         # Full dataset split into multiple tar.gz archives (~4–5 GB each)
│   ├── data_part_000.tar.gz
│   ├── ...
│
├── sample/       # Small sample subset (~500MB) for quick testing
│   └── data_part_000.tar.gz
│
├── validation/   # Lightweight validation set (~15–18 MB shards)
│   ├── data_part_000.tar.gz
│   ├── ...
│
├── .gitattributes
└── .gitignore
```

Each `.tar.gz` archive contains thousands of individual JSON files, named by their S2ORC `corpus_id`, for example:

```
12345678.json
12345679.json
...
```

---

## 📄 Data Record Format

Each JSON file contains a single research article, structured according to the Lit2Vec v1.0 schema.

### 🔑 Top-Level Fields

| Field                | Type                  | Description                                                  |
| -------------------- | --------------------- | ------------------------------------------------------------ |
| `schema_version`     | string                | Schema version (`"1.0"`)                                     |
| `corpus_id`          | integer               | S2ORC Corpus ID (used as filename)                           |
| `metadata`           | object                | Title, authors, year, venue, identifiers                     |
| `abstract`           | string                | Abstract text                                                |
| `fulltext`           | string                | Full article text                                            |
| `paragraphs`         | array\[string]        | Paragraph-level text units                                   |
| `embeddings`         | array\[array\[float]] | 1024-D float32 paragraph vectors (aligned with `paragraphs`) |
| `abstract_embedding` | array\[float]         | 1024-D float32 vector for abstract                           |
| `predicted_subfield` | object                | Map of subfield label → confidence score                     |
| `tldr`               | string                | Machine-generated two-sentence summary                       |
| `unpaywall_license`  | object \| null        | License data from Unpaywall                                  |
| `crossref_license`   | object \| null        | License data from Crossref                                   |
| `openalex_license`   | object \| null        | License data from OpenAlex                                   |

### 📦 Minimal Example

```json
{
  "schema_version": "1.0",
  "corpus_id": 37254803,
  "metadata": {
    "title": "Protective effect of EGCG...",
    "year": 2016,
    "externalids": { "DOI": "10.xxxx/xxxxx" },
    "url": "https://..."
  },
  "abstract": "Epigallocatechin gallate ...",
  "fulltext": "# Protective effect of ...",
  "paragraphs": ["37254803P0: passage: ...", "..."],
  "embeddings": [[0.0123, -0.0456, ...], ...],
  "abstract_embedding": [0.0345, -0.0789, ...],
  "predicted_subfield": {
    "Biochemistry": 0.997,
    "Medicinal Chemistry": 0.759
  },
  "tldr": "EGCG reduces lipid peroxidation ...",
  "unpaywall_license": {
    "best_oa_location": {
      "license": "cc-by"
    }
  },
  "crossref_license": {
    "license": "http://creativecommons.org/licenses/by/4.0/"
  },
  "openalex_license": null
}
```

---

## 🧠 Use Cases

Lit2Vec is optimized for:

* **Semantic search** — FAISS-style dense retrieval with paragraph embeddings
* **Retrieval-Augmented Generation (RAG)** — grounding LLMs on full-text chemistry papers
* **Summarization** — pre-generated TL;DRs for training and evaluation
* **Multi-label classification** — 18 chemistry subfields per article with confidence scores
* **Citation & licensing analysis** — metadata from Unpaywall, Crossref, OpenAlex
* **Knowledge graph construction** — integrate with PubChem, ChEMBL, patents, etc.

---

## 🚀 Quickstart

### Python (manual extraction)

```python
from datasets import load_dataset

dataset = load_dataset(
     "Bocklitz-Lab/Lit2Vec-dataset",
     data_files={"train": "data/*.tar.gz"},
     trust_remote_code=True,)

# Peek at one example
sample = dataset["train"][0]
print(sample["metadata"])  # Raw JSON string with title, authors, etc.
print(sample["predicted_subfield"])  # List of {label, score}

# Extract title from metadata
import json
metadata = json.loads(sample["metadata"])
print("Title:", metadata.get("title"))

# Display subfield predictions
print("Predicted subfields:")
for sub in sample["predicted_subfield"]:
    print(f"  {sub['label']}: {sub['score']:.3f}")

```

---

## ✅ Validation Subset

The `/validation/` folder contains `.tar.gz` archives to:

* Test schema compliance
* Integrate with pipelines
* Validate embeddings and metadata formats

---

## 🪪 Licensing

Lit2Vec is **fully open-access**, curated under FAIR principles. All included articles passed license validation via:

* ✅ **Unpaywall**
* ✅ **Crossref**
* ✅ **OpenAlex**

Permissible licenses include:

* **CC-BY**
* **CC-BY-SA**
* **CC-BY-NC**
* **CC-BY-NC-SA**
* **Public Domain**

---

## 📖 Citation

Please cite the following if you use this dataset:

```bibtex
@article{amiri2025lit2vec,
  title={Lit2Vec: A Large-Scale, Derivative-Permissive Chemistry Corpus for Retrieval-Augmented Generation and Semantic Search},
  author={Amiri, Mahmoud and Bocklitz, Thomas},
  journal={Preprint},
  year={2025},
  note={\url{https://huggingface.co/datasets/Bocklitz-Lab/Lit2Vec-dataset}}
}
```

---

## 🤝 Contributing

We welcome community contributions for:

* Dataset converters (e.g., Hugging Face `datasets` format)
* Example notebooks and pipelines
* Benchmarking scripts

Open a pull request or contact us!


---

**Lit2Vec** is developed at the **Leibniz Institute of Photonic Technology** and the **Institute of Physical Chemistry, Friedrich Schiller University Jena** to accelerate open science in chemistry and AI.