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README.md

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+---
+license: mit
+task_categories:
+- feature-extraction
+- question-answering
+language:
+- en
+tags:
+- code
+pretty_name: LOTUS Scraped Data (2025/06/07)
+size_categories:
+- 1K<n<10K
+---
+# Lotus Deep Research Dataset
+
+[![Dataset](https://img.shields.io/badge/Dataset-lotus--data%2Fscraped__related__works__20250607__180022-blue)](https://huggingface.co/datasets/lotus-data/scraped_related_works_20250607_180022)
+[![GitHub](https://img.shields.io/badge/GitHub-lotus--deep--research-green)](https://github.com/harshitgupta412/lotus-deep-research)
+[![License](https://img.shields.io/badge/License-MIT-yellow)](https://github.com/harshitgupta412/lotus-deep-research/blob/main/LICENSE)
+
+---
+
+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 **67 academic papers** from ArXiv with their related works sections and **1663 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` (67 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 |
+| `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. `citations_with_recovered_res.csv` (1663 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 |
+| `search_res_title` | Title from search results |
+| `search_res_url` | URL from search results |
+| `search_res_content` | Content snippet from search results |
+
+## 🚀 Quick Start
+
+### Loading the Dataset
+
+```python
+import pandas as pd
+
+# Load papers dataset
+papers_df = pd.read_csv('papers_with_related_works.csv')
+print(f"Loaded {len(papers_df)} papers")
+
+# Load citations dataset
+citations_df = pd.read_csv('citations_with_recovered_res.csv')
+print(f"Loaded {len(citations_df)} citations")
+```
+
+
+
+### Example: Extract Related Works for a Paper
+
+```python
+# 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)}")
+```
+
+## 📈 Dataset Statistics
+
+- **Total Papers**: 67
+- **Total Citations**: 1663
+- **Date Range**: 2024-2025 (recent papers)
+
+## 🔧 Data Collection Process
+
+This dataset was created using the [Lotus Deep Research](https://github.com/harshitgupta412/lotus-deep-research) pipeline:
+
+1. **ArXiv Scraping**: Collected papers by category and date range
+2. **Author Filtering**: Focused on high-impact researchers (h-index ≥ 25)
+3. **LaTeX Extraction**: Extracted related works sections from LaTeX source
+4. **Citation Recovery**: Resolved citations and recovered metadata
+5. **Quality Filtering**: Ensured data quality and completeness
+
+## 📚 Related Resources
+
+- **[GitHub Repository](https://github.com/harshitgupta412/lotus-deep-research)**: Full source code and documentation
+- **[Data Pipeline](https://github.com/harshitgupta412/lotus-deep-research/tree/main/data_pipeline)**: Tools for collecting similar datasets
+- **[Evaluation Framework](https://github.com/harshitgupta412/lotus-deep-research/tree/main/eval)**: Framework for evaluating research generation systems
+
+## 🤝 Contributing
+
+We welcome contributions to improve this dataset! Please see the [main repository](https://github.com/harshitgupta412/lotus-deep-research) for contribution guidelines.
+
+## 📄 License
+
+This dataset is released under the MIT License. See the [LICENSE](https://github.com/harshitgupta412/lotus-deep-research/blob/main/LICENSE) file for details.
+
+---
+
+**Note**: This dataset is actively maintained and updated. Check the GitHub repository for the latest version and additional resources.