README.md

---
license: cc-by-4.0
task_categories:
- text-classification
- sentence-similarity
- feature-extraction
language:
- en
tags:
- biomedical
- scientific-literature
- pubmed
- pmc
- embeddings
- soda-vec
size_categories:
- 10M<n<100M
dataset_info:
  features:
  - name: pmid
    dtype: string
  - name: title
    dtype: string
  - name: abstract
    dtype: string
  - name: doi
    dtype: string
  splits:
  - name: train
    num_bytes: 39688553174
    num_examples: 26473900
  - name: validation
    num_bytes: 74918135
    num_examples: 50000
  - name: test
    num_bytes: 74931494
    num_examples: 50000
  download_size: 23525241784
  dataset_size: 39838402803
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
  - split: test
    path: data/test-*
---

# SODA-VEC Clean Dataset

This is a **cleaned and filtered version** of the SODA-VEC dataset, containing high-quality biomedical title-abstract pairs from PubMed Central (PMC) articles.

## Dataset Overview

- **Total examples**: 26,573,900
- **Training set**: 26,473,900 examples (99.6%)
- **Validation set**: 50,000 examples (0.2%)
- **Test set**: 50,000 examples (0.2%)

## Quality Filtering Applied

This dataset has been processed with the following quality filters:

### Abstract Length Filtering
- **Minimum length**: 128 characters
- **Maximum length**: 6,000 characters
- **Rationale**: Removes fragments and overly long texts while preserving scientific abstracts

### Retention Statistics
- **Original dataset**: ~26.6M examples
- **After filtering**: 26,573,900 examples
- **Retention rate**: ~99.7%

### Content Statistics (sample):
- **Title length**: ~100 ± 50 chars
- **Abstract length**: ~1,300 ± 600 chars
- **Title range**: 10-500 chars
- **Abstract range**: 128-6,000 chars

## Length Distributions

![Length Distributions](length_distributions.png)

The dataset shows well-balanced length distributions:
- **Title lengths**: Centered around 100 characters with good variance
- **Abstract lengths**: Normally distributed around 1,300 characters
- **Quality filtering**: Clearly removes outliers while preserving natural variation

![Length Statistics](length_statistics.png)

## Data Fields

Each example contains:

- **`title`** (string): The title of the scientific article
- **`abstract`** (string): The abstract of the scientific article
- **`pmcid`** (string): PubMed Central ID for the article

## Intended Use

This dataset is designed for:

### Primary Applications
- **Scientific text embeddings**: Training domain-specific embedding models
- **Biomedical NLP**: Fine-tuning language models on scientific literature
- **Semantic similarity**: Learning representations for scientific text matching
- **Information retrieval**: Building search systems for biomedical literature

### Research Applications
- Representation learning for scientific texts
- Cross-modal learning (title-abstract relationships)
- Domain adaptation for biomedical language models
- Evaluation of scientific text understanding systems

## Data Source & Methodology

### Original Dataset
Based on the SODA-VEC dataset from PubMed Central articles.

### Processing Pipeline
1. **Data loading**: Combined train/validation/test splits from original dataset
2. **Quality filtering**: Applied length-based filters to ensure high-quality pairs
3. **Split creation**: Created new balanced train/validation/test splits
4. **Validation**: Verified data integrity and distribution balance

### Quality Assurance
- Length distribution analysis
- Duplicate detection and removal
- Content quality validation
- Statistical validation of splits

## Usage Example

```python
from datasets import load_dataset

# Load the dataset
dataset = load_dataset("EMBO/soda-vec-data-full_pmc_title_abstract")

# Access different splits
train_data = dataset["train"]
val_data = dataset["validation"] 
test_data = dataset["test"]

# Example usage
for example in train_data.take(1):
    print(f"Title: {example['title']}")
    print(f"Abstract: {example['abstract']}")
    print(f"PMC ID: {example['pmcid']}")
```

## Citation

If you use this dataset in your research, please cite the original SODA-VEC paper:

```bibtex
@article{soda-vec-2024,
  title={SODA-VEC: Training Vector Representations of Scientific Literature},
  author={...},
  journal={...},
  year={2024}
}
```

## License

This dataset is released under the **CC-BY-4.0** license, consistent with PubMed Central's open access requirements.

## Contact

For questions about this dataset, please contact the EMBO team or open an issue in the dataset repository.

---

*Dataset processed with quality filters and balanced splits for optimal training performance.*