Datasets:

EMBO
/

soda-vec-data-full_pmc_title_abstract

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

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

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

Data loading: Combined train/validation/test splits from original dataset
Quality filtering: Applied length-based filters to ensure high-quality pairs
Split creation: Created new balanced train/validation/test splits
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

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:

@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.