metadata

license: mit
task_categories:
  - feature-extraction
  - question-answering
language:
  - en
tags:
  - code
pretty_name: DeepScholarBench Dataset
size_categories:
  - 1K<n<10K
configs:
  - config_name: papers
    data_files: papers_with_related_works.csv
  - config_name: citations
    data_files: recovered_citations.csv
  - config_name: important_citations
    data_files: important_citations.csv
  - config_name: full
    data_files:
      - papers_with_related_works.csv
      - recovered_citations.csv
      - important_citations.csv

DeepScholarBench Dataset

A comprehensive dataset of academic papers with extracted related works sections and recovered citations, designed for training and evaluating research generation systems.

📊 Dataset Overview

This dataset contains 63 academic papers from ArXiv with their related works sections and 1630 recovered citations, providing a rich resource for research generation and citation analysis tasks.

🎯 Use Cases

Research Generation: Train models to generate related works sections
Citation Analysis: Study citation patterns and relationships
Academic NLP: Develop tools for academic text processing
Evaluation: Benchmark research generation systems
Knowledge Discovery: Analyze research trends and connections

📁 Dataset Structure

1. `papers_with_related_works.csv` (63 papers)

Contains academic papers with extracted related works sections in multiple formats:

Column	Description
`arxiv_id`	ArXiv identifier (e.g., "2506.02838v1")
`title`	Paper title
`authors`	Author names
`abstract`	Paper abstract
`categories`	ArXiv categories (e.g., "cs.AI, econ.GN")
`published_date`	Publication date
`updated_date`	Last update date
`abs_url`	ArXiv abstract URL
`arxiv_link`	Full ArXiv link
`publication_date`	Publication date
`raw_latex_related_works`	Raw LaTeX related works section
`clean_latex_related_works`	Cleaned LaTeX related works section
`pdf_related_works`	Related works extracted from PDF

2. `recovered_citations.csv` (1630 citations)

Contains individual citations with recovered metadata:

Column	Description
`parent_paper_title`	Title of the paper containing the citation
`parent_paper_arxiv_id`	ArXiv ID of the parent paper
`citation_shorthand`	Citation key (e.g., "NBERw21340")
`raw_citation_text`	Raw citation text from LaTeX
`cited_paper_title`	Title of the cited paper
`cited_paper_arxiv_link`	ArXiv link if available
`cited_paper_abstract`	Abstract of the cited paper
`has_metadata`	Whether metadata was successfully recovered
`is_arxiv_paper`	Whether the cited paper is from ArXiv
`bib_paper_authors`	Authors of the cited paper
`bib_paper_year`	Publication year
`bib_paper_month`	Publication month
`bib_paper_url`	URL of the cited paper
`bib_paper_doi`	DOI of the cited paper
`bib_paper_journal`	Journal name
`original_title`	Original title from citation metadata
`search_res_title`	Title from search results
`search_res_url`	URL from search results
`search_res_content`	Content snippet from search results

3. `important_citations.csv` (1,050 citations)

Contains enhanced citations with full paper metadata and content:

Column	Description
`parent_paper_title`	Title of the paper containing the citation
`parent_paper_arxiv_id`	ArXiv ID of the parent paper
`citation_shorthand`	Citation key (e.g., "NBERw21340")
`raw_citation_text`	Raw citation text from LaTeX
`cited_paper_title`	Title of the cited paper
`cited_paper_arxiv_link`	ArXiv link if available
`cited_paper_abstract`	Abstract of the cited paper
`has_metadata`	Whether metadata was successfully recovered
`is_arxiv_paper`	Whether the cited paper is from ArXiv
`cited_paper_authors`	Authors of the cited paper
`bib_paper_year`	Publication year
`bib_paper_month`	Publication month
`bib_paper_url`	URL of the cited paper
`bib_paper_doi`	DOI of the cited paper
`bib_paper_journal`	Journal name
`original_title`	Original title from citation metadata
`search_res_title`	Title from search results
`search_res_url`	URL from search results
`search_res_content`	Content snippet from search results
`arxiv_id`	ArXiv ID of the parent paper
`arxiv_link`	ArXiv link of the parent paper
`publication_date`	Publication date of the parent paper
`title`	Title of the parent paper
`abstract`	Abstract of the parent paper
`raw_latex_related_works`	Raw LaTeX related works section
`related_work_section`	Processed related works section
`pdf_related_works`	Related works extracted from PDF
`cited_paper_content`	Full content of the cited paper

⚙️ Dataset Configurations

Configuration	Description	Files	Records	Use Case
`papers`	Academic papers only	`papers_with_related_works.csv`	63 papers	Research generation, content analysis
`citations`	Citations only	`recovered_citations.csv`	1,630 citations	Citation analysis, relationship mapping
`important_citations`	Enhanced citations with metadata	`important_citations.csv`	1,050 citations	Advanced citation analysis, paper-citation linking

🚀 Quick Start

Loading from Hugging Face Hub (Recommended)

from datasets import load_dataset

# Load papers dataset
papers = load_dataset("deepscholar-bench/DeepScholarBench", name="papers")["train"]
print(f"Loaded {len(papers)} papers")

# Load citations dataset  
citations = load_dataset("deepscholar-bench/DeepScholarBench", name="citations")["train"]
print(f"Loaded {len(citations)} citations")

# Load important citations with enhanced metadata
important_citations = load_dataset("deepscholar-bench/DeepScholarBench", name="important_citations")["train"]
print(f"Loaded {len(important_citations)} important citations")

# Convert to pandas for analysis
papers_df = papers.to_pandas()
citations_df = citations.to_pandas()
important_citations_df = important_citations.to_pandas()

Example: Extract Related Works for a Paper

# Get a specific paper
paper = papers_df[papers_df['arxiv_id'] == '2506.02838v1'].iloc[0]
print(f"Title: {paper['title']}")
print(f"Related Works:\n{paper['clean_latex_related_works']}")

# Get all citations for this paper
paper_citations = citations_df[citations_df['parent_paper_arxiv_id'] == '2506.02838v1']
print(f"Number of citations: {len(paper_citations)}")

Example: Working with Important Citations

# Load important citations (enhanced with paper metadata)
important_citations = load_dataset("deepscholar-bench/DeepScholarBench", name="important_citations")["train"]

# This configuration includes both citation data AND the parent paper information
sample = important_citations[0]
print(f"Citation: {sample['cited_paper_title']}")
print(f"Parent Paper: {sample['title']}")
print(f"Paper Abstract: {sample['abstract'][:200]}...")
print(f"Related Work Section: {sample['related_work_section'][:200]}...")

# Analyze citation patterns
important_df = important_citations.to_pandas()
print(f"Citations with full paper content: {important_df['cited_paper_content'].notna().sum()}")
print(f"Citations with related work sections: {important_df['related_work_section'].notna().sum()}")

📈 Dataset Statistics

Total Papers: 63
Total Citations: 1,630
Important Citations: 1,050
Date Range: 2024-2025 (recent papers)

🔧 Data Collection Process

This dataset was created using the DeepScholarBench pipeline:

ArXiv Scraping: Collected papers by category and date range
Author Filtering: Focused on high-impact researchers (h-index ≥ 25)
LaTeX Extraction: Extracted related works sections from LaTeX source
Citation Recovery: Resolved citations and recovered metadata
Quality Filtering: Ensured data quality and completeness

📚 Related Resources

GitHub Repository: Full source code and documentation
Data Pipeline: Tools for collecting similar datasets
Evaluation Framework: Framework for evaluating research generation systems

🏆 Leaderboard

We maintain a leaderboard to track the performance of various models on the DeepScholarBench evaluation tasks:

Official Leaderboard: Live rankings of model performance
Evaluation Metrics: Models are evaluated on relevance, coverage, and citation accuracy as described in the evaluation guide
Submission Process: Submit your results via this Form

🤝 Contributing

We welcome contributions to improve this dataset! Please see the main repository for contribution guidelines.

📄 License

This dataset is released under the MIT License. See the LICENSE file for details.

Note: This dataset is actively maintained and updated. Check the GitHub repository for the latest version and additional resources.