language:
- en
license: mit
size_categories:
- 1K<n<10K
task_categories:
- text-generation
pretty_name: DeepScholarBench Dataset
tags:
- code
- scientific-research
- academic-papers
- citation-analysis
- retrieval-augmented-generation
- rag
- summarization
- llm-evaluation
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.
Abstract
The ability to research and synthesize knowledge is central to human expertise and progress. An emerging class of systems promises these exciting capabilities through generative research synthesis, performing retrieval over the live web and synthesizing discovered sources into long-form, cited summaries. However, evaluating such systems remains an open challenge: existing question-answering benchmarks focus on short-form factual responses, while expert-curated datasets risk staleness and data contamination. Both fail to capture the complexity and evolving nature of real research synthesis tasks. In this work, we introduce DeepScholar-bench, a live benchmark and holistic, automated evaluation framework designed to evaluate generative research synthesis. DeepScholar-bench draws queries from recent, high-quality ArXiv papers and focuses on a real research synthesis task: generating the related work sections of a paper by retrieving, synthesizing, and citing prior research. Our evaluation framework holistically assesses performance across three key dimensions, knowledge synthesis, retrieval quality, and verifiability. We also develop DeepScholar-base, a reference pipeline implemented efficiently using the LOTUS API. Using the DeepScholar-bench framework, we perform a systematic evaluation of prior open-source systems, search AI's, OpenAI's DeepResearch, and DeepScholar-base. We find that DeepScholar-base establishes a strong baseline, attaining competitive or higher performance than each other method. We also find that DeepScholar-bench remains far from saturated, with no system exceeding a score of $19%$ across all metrics. These results underscore the difficulty of DeepScholar-bench, as well as its importance for progress towards AI systems capable of generative research synthesis.
📊 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 |
🚀 Sample Usage
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:
{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.