README.md

---
dataset_name: lit2vec-subfield-classifier-dataset
pretty_name: Lit2Vec Subfield Classifier Dataset
tags:
  - scientific-papers
  - classification
  - embeddings
  - semantic-scholar
  - scientific-literature
license: cc-by-4.0
task_categories:
  - text-classification
language:
  - en
size_categories:
  - 10K<n<100K
---

# Lit2Vec Subfield Classifier Dataset

## Summary

The **Lit2Vec Subfield Classifier Dataset** is a curated and preprocessed collection of scientific research metadata designed for **text classification** and **embedding-based machine learning** tasks.  
It includes over 39,900 chemistry abstract and tldr text annotated with **domain subfields**, **dense text embeddings**, and **structured metadata**, making it suitable for:

* Scientific **document classification**
* **Subfield prediction** and semantic tagging
* **Embedding-based retrieval and similarity search**
* **Representation learning** and transfer learning on scholarly text

All records are sourced from **CC-BY licensed** publications in the Semantic Scholar corpus.

---

## Dataset Structure

### Splits

* **Train**: \~80% of records  
* **Validation**: \~10% of records  
* **Test**: \~10% of records  

Each entry is a flat JSON object (1 per line) in `.jsonl` format.

### Example Record

```json
{
  "corpus_id": 105403827,
  "doi": "10.31031/PPS.2018.02.000549",
  "title": "Modeling of Chemical Reacting Transport Phenomena in a PEM Fuel Cell using Finite Volume Method",
  "authors": ["Mohammed Jourdani", "H. Mounir", "A. Marjani"],
  "author_ids": [null, "31345194", "30962775"],
  "venue": "Progress in Petrochemical Science",
  "year": 2018,
  "fields_of_study": ["Engineering", "Chemistry", "Environmental Science", "Materials Science"],
  "publication_date": "2018-09-19",
  "journal_name": "Progress in Petrochemical Science",
  "license_publisher": "Crimson Publishers",
  "license": "cc-by",
  "oa_url": "http://crimsonpublishers.com/pps/pdf/PPS.000549.pdf",
  "text": "A two-dimensional transient model using finite volume method enhances PEMFC design by simulating gas flow and solving movement and energy equations.",
  "field_classification": ["Energy Chemistry", "Chemical Engineering"],
  "text_type": "summary",
  "embedding": [-0.0148, -0.0213, 0.0076 , -0.0111, 0.0357, 0.0141],
  "label": [9, 11]
}
````

---

## Features

| Field Name             | Type           | Description                                         |
| ---------------------- | -------------- | --------------------------------------------------- |
| `corpus_id`            | `int`          | Semantic Scholar ID                                 |
| `doi`                  | `string`       | DOI identifier                                      |
| `title`                | `string`       | Title of the publication                            |
| `authors`              | `list[string]` | Author names                                        |
| `author_ids`           | `list[string]` | Semantic Scholar author IDs                         |
| `venue`                | `string`       | Journal or conference name                          |
| `year`                 | `int`          | Year of publication                                 |
| `fields_of_study`      | `list[string]` | Top-level categories (e.g., Engineering, Chemistry) |
| `publication_date`     | `string`       | ISO 8601 date                                       |
| `journal_name`         | `string`       | Full journal name                                   |
| `license_publisher`    | `string`       | OA license publisher                                |
| `license`              | `string`       | License type (e.g., cc-by)                          |
| `oa_url`               | `string`       | Open access link to full text                       |
| `text`                 | `string`       | Abstract or summary                                 |
| `text_type`            | `string`       | Indicates type of `text` field (e.g., summary)      |
| `field_classification` | `list[string]` | Expert-curated subfield labels                      |
| `embedding`            | `list[float]`  | Dense vector embedding of the text                  |
| `label`                | `list[int]`    | Numeric labels for classification                   |

---

## Label Mapping

The dataset includes a consistent mapping between **subfield names** and **numeric labels**:

| Subfield                           | Label |
| ---------------------------------- | ----- |
| Catalysis                          | 0     |
| Organic Chemistry                  | 1     |
| Polymer Chemistry                  | 2     |
| Inorganic Chemistry                | 3     |
| Materials Science                  | 4     |
| Analytical Chemistry               | 5     |
| Physical Chemistry                 | 6     |
| Biochemistry                       | 7     |
| Environmental Chemistry            | 8     |
| Energy Chemistry                   | 9     |
| Medicinal Chemistry                | 10    |
| Chemical Engineering               | 11    |
| Supramolecular Chemistry           | 12    |
| Radiochemistry & Nuclear Chemistry | 13    |
| Forensic & Legal Chemistry         | 14    |
| Food Chemistry                     | 15    |
| Chemical Education                 | 16    |
| Others                             | 17    |

For convenience, here is the `label_to_index` mapping in JSON format:

```json
{
    "label_to_index": {
        "Catalysis": 0,
        "Organic Chemistry": 1,
        "Polymer Chemistry": 2,
        "Inorganic Chemistry": 3,
        "Materials Science": 4,
        "Analytical Chemistry": 5,
        "Physical Chemistry": 6,
        "Biochemistry": 7,
        "Environmental Chemistry": 8,
        "Energy Chemistry": 9,
        "Medicinal Chemistry": 10,
        "Chemical Engineering": 11,
        "Supramolecular Chemistry": 12,
        "Radiochemistry & Nuclear Chemistry": 13,
        "Forensic & Legal Chemistry": 14,
        "Food Chemistry": 15,
        "Chemical Education": 16,
        "Others": 17
    },
    "index_to_label": {
        "0": "Catalysis",
        "1": "Organic Chemistry",
        "2": "Polymer Chemistry",
        "3": "Inorganic Chemistry",
        "4": "Materials Science",
        "5": "Analytical Chemistry",
        "6": "Physical Chemistry",
        "7": "Biochemistry",
        "8": "Environmental Chemistry",
        "9": "Energy Chemistry",
        "10": "Medicinal Chemistry",
        "11": "Chemical Engineering",
        "12": "Supramolecular Chemistry",
        "13": "Radiochemistry & Nuclear Chemistry",
        "14": "Forensic & Legal Chemistry",
        "15": "Food Chemistry",
        "16": "Chemical Education",
        "17": "Others"
    }
}
```

---

## Usage

```python
from datasets import load_dataset

dataset = load_dataset("Bocklitz-Lab/lit2vec-subfield-classifier-dataset")

print(dataset["train"][0]["title"])
print(dataset["train"][0]["field_classification"])
```

Each entry includes:

* A machine-readable **embedding**
* A list of **labels (IDs)**
* The **original text**, metadata, and license info

---

## Applications

* 🧠 **Text classification** using BERT, RoBERTa, etc.
* 🔍 **Semantic search** with sentence-transformer or FAISS
* 🧬 **Domain adaptation** for scientific NLP tasks
* 🧭 **Clustering** and unsupervised topic modeling
* 📈 **Benchmarking** embedding models on scientific literature

---

## Licensing

* All entries are sourced from **CC BY 4.0** licensed publications.
* Each entry includes original attribution via `license_publisher`, `oa_url`, and `doi`.
* You are free to reuse, modify, and distribute the dataset under the terms of **Creative Commons Attribution 4.0 International License**.

---

## Citation

If you use this dataset in your research, please cite:

```bibtex
@dataset{lit2vec_classifier_2025,
  author       = {Mahmoud Amiri, Thomas Bocklitz},
  title        = {Lit2Vec Subfield Classifier Dataset},
  year         = {2025},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/datasets/Bocklitz-Lab/lit2vec-subfield-classifier-dataset}},
  note         = {Submitted to Nature Scientific Data}
}
```

---

## Acknowledgements

* Built on top of **Semantic Scholar Open Research Corpus (S2ORC)**
* Flattened and cleaned using custom preprocessing by the **Bocklitz Lab**
* Embeddings generated from proprietary or publicly available models (details forthcoming in accompanying paper)