README.md

metadata

configs:
  - config_name: default
    data_files:
      - split: train
        path: cleaned_papers.csv
license: cc-by-sa-4.0
language:
  - en
tags:
  - academic
  - research
  - papers
  - abstracts
  - nlp
  - pubmed
  - machine-learning
  - text-mining
  - scientific-literature
task_categories:
  - text-classification
  - text-generation
  - feature-extraction
size_categories:
  - 1M<n<10M
extra_gated_prompt: >-
  By requesting access to this dataset, you agree to use the data responsibly
  and ethically, in compliance with the CC BY-SA 4.0 license. You also commit to
  giving proper credit to the original data sources (as listed below) and to the
  dataset maintainers in any work, project, or publication that uses this data.
extra_gated_fields:
  I agree to credit the original authors BVG-BVQ and respect the CC BYSA 4 license: checkbox
  I gave this dataset a like because sharing is caring: checkbox

Comprehensive Academic Papers Dataset: 3M+ Research Paper Titles and Abstracts

📋 Overview

This dataset is a comprehensive collection of over 3 million research paper titles and abstracts, curated and consolidated from multiple high-quality academic sources. The dataset provides a unified, clean, and standardized format for researchers, data scientists, and machine learning practitioners working on natural language processing, academic research analysis, and knowledge discovery tasks.

🎯 Key Features

• 3.6+ million scientific papers with titles and abstracts
• Multi-domain coverage: Physics, Mathematics, Computer Science, Biology, Medicine, and more
• Standardized format: Consistent title and abstract columns
• Quality assured: Validated using Pydantic models and cleaned of duplicates/null values
• Ready-to-use: Pre-processed and formatted for immediate analysis
• Format: CSV
• Language: English

📊 Dataset Statistics

Metric	Value
Total Records	~3,000,000+
Columns	2 (title, abstract)
File Size	4.15 GB
Format	CSV
Duplicates	Removed
Missing Values	Removed

🗂️ Dataset Structure

cleaned_papers.csv
├── title (string): Scientific paper title
└── abstract (string): Scientific paper abstract

🔄 Data Processing Pipeline

The dataset underwent a rigorous cleaning and standardization process:

1. Data Import: Automated import from multiple sources (Kaggle API, Hugging Face)
2. Column Standardization: Mapping various column names to consistent title and abstract format
3. Data Validation: Pydantic model validation ensuring data quality
4. Duplicate Removal: Advanced deduplication based on title and abstract similarity
5. Null Value Handling: Removal of records with missing titles or abstracts
6. Quality Assurance: Final validation and statistics generation

💡 Use Cases

This dataset is ideal for:

• Natural Language Processing: Text classification, sentiment analysis, topic modeling
• Scientific Literature Analysis: Trend analysis, domain classification, citation prediction
• Machine Learning Research: Training language models, text summarization, information extraction
• Academic Research: Bibliometric analysis, research trend identification
• Educational Applications: Building search engines, recommendation systems

🔗 Data Sources and Attribution

This dataset consolidates academic papers from the following sources:

Kaggle Datasets:

1. ArXiv Scientific Research Papers Dataset by @sumitm004
2. Cornell University ArXiv Dataset by @Cornell-University

Hugging Face Datasets:

3. ML-ArXiv-Papers by @CShorten
4. ArXiv Biology by @zeroshot
5. ArXiv Data Extended by @wrapper228
6. Stroke PubMed Abstracts by @Gaborandi
7. PubMed ArXiv Abstracts Data by @brainchalov
8. Abstracts Cleaned by @Eitanli

🔄 Update Schedule

This dataset represents a point-in-time consolidation. Future versions may include:

• Additional academic sources
• Extended fields (authors, publication dates, venues)
• Domain-specific subsets
• Enhanced metadata

📄 License and Usage

Please respect the individual licenses of the source datasets. This consolidated version is provided for research and educational purposes. When using this dataset:

1. Citation: Please cite this dataset and acknowledge the original data sources
2. Attribution: Credit the original dataset creators listed above
3. Compliance: Ensure compliance with individual dataset licenses
4. Academic Use: Primarily intended for non-commercial, academic, and research purposes

🙏 Acknowledgments

Special thanks to all the original dataset creators and the academic communities that make their research data publicly available. This work builds upon their valuable contributions to open science and knowledge sharing.

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Keywords: academic papers, research abstracts, NLP, machine learning, text mining, scientific literature, ArXiv, PubMed, natural language processing, research dataset