| # 🧪 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. | |