Track important_citations.csv with Git LFS

.gitattributes

@@ -57,3 +57,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+important_citations.csv filter=lfs diff=lfs merge=lfs -text

README.md

@@ -23,7 +23,7 @@ A comprehensive dataset of academic papers with extracted related works sections
 
 ## 📊 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.
+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
 
@@ -35,10 +35,9 @@ This dataset contains **67 academic papers** from ArXiv with their related works
 
 ## 📁 Dataset Structure
 
-### 1. `papers_with_related_works.csv` (67 papers)
+### 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") |
@@ -50,11 +49,12 @@ Contains academic papers with extracted related works sections in multiple forma
 | `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. `citations_with_recovered_res.csv` (1663 citations)
+### 2. `recovered_citations.csv` (1630 citations)
 
 Contains individual citations with recovered metadata:
 
@@ -75,6 +75,7 @@ Contains individual citations with recovered metadata:
 | `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 |

important_citations.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5cb48008cdaa202abc4ed2e6689357f1858791b5fe0d586b33f9b447797114cf
+size 16641640

lotus_deep_research.py

@@ -0,0 +1,397 @@
+"""
+Lotus Deep Research Dataset: Academic papers with extracted related works sections and recovered citations.
+
+This dataset contains academic papers from ArXiv with their related works sections and recovered citations,
+providing a rich resource for research generation and citation analysis tasks.
+"""
+
+import csv
+import datasets
+from typing import Dict, List, Any, Optional
+
+
+# Dataset URLs - these would typically point to hosted files
+_DESCRIPTION = """\
+A comprehensive dataset of academic papers with extracted related works sections and recovered citations, 
+designed for training and evaluating research generation systems.
+
+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.
+"""
+
+_CITATION = """\
+@misc{patel2025deepscholarbenchlivebenchmarkautomated,
+      title={DeepScholar-Bench: A Live Benchmark and Automated Evaluation for Generative Research Synthesis}, 
+      author={Liana Patel and Negar Arabzadeh and Harshit Gupta and Ankita Sundar and Ion Stoica and Matei Zaharia and Carlos Guestrin},
+      year={2025},
+      eprint={2508.20033},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2508.20033}, 
+}
+"""
+
+_HOMEPAGE = "https://github.com/guestrin-lab/deepscholar-bench"
+_LICENSE = "MIT"
+
+# URLs to the dataset files
+_URLS = {
+    "papers": "papers_with_related_works.csv",
+    "citations": "recovered_citations.csv",
+    "important_citations": "important_citations.csv",
+}
+
+
+class LotusDeepResearchConfig(datasets.BuilderConfig):
+    """BuilderConfig for LotusDeepResearch dataset."""
+
+    def __init__(self, name: str, description: str, **kwargs):
+        """BuilderConfig for LotusDeepResearch.
+        
+        Args:
+            name: Configuration name
+            description: Description of this configuration
+            **kwargs: Additional keyword arguments
+        """
+        super(LotusDeepResearchConfig, self).__init__(
+            name=name,
+            description=description,
+            version=datasets.Version("1.0.0"),
+            **kwargs
+        )
+
+
+class LotusDeepResearch(datasets.GeneratorBasedBuilder):
+    """Lotus Deep Research dataset."""
+
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIGS = [
+        LotusDeepResearchConfig(
+            name="papers",
+            description="Academic papers with extracted related works sections (63 papers)",
+        ),
+        LotusDeepResearchConfig(
+            name="citations", 
+            description="Recovered citations with metadata (1630 citations)",
+        ),
+        LotusDeepResearchConfig(
+            name="important_citations",
+            description="Important citations with metadata (1050 citations)",
+        ),
+        LotusDeepResearchConfig(
+            name="full",
+            description="Complete dataset with both papers and citations",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "full"
+
+    def _info(self) -> datasets.DatasetInfo:
+        """Return the dataset info."""
+        
+        if self.config.name == "papers":
+            features = datasets.Features({
+                "arxiv_id": datasets.Value("string"),
+                "title": datasets.Value("string"),
+                "authors": datasets.Value("string"),
+                "abstract": datasets.Value("string"),
+                "categories": datasets.Value("string"),
+                "published_date": datasets.Value("string"),
+                "updated_date": datasets.Value("string"),
+                "abs_url": datasets.Value("string"),
+                "arxiv_link": datasets.Value("string"),
+                "publication_date": datasets.Value("string"),
+                "raw_latex_related_works": datasets.Value("string"),
+                "clean_latex_related_works": datasets.Value("string"),
+                "pdf_related_works": datasets.Value("string"),
+            })
+        elif self.config.name == "citations":
+            features = datasets.Features({
+                "parent_paper_title": datasets.Value("string"),
+                "parent_paper_arxiv_id": datasets.Value("string"),
+                "citation_shorthand": datasets.Value("string"),
+                "raw_citation_text": datasets.Value("string"),
+                "cited_paper_title": datasets.Value("string"),
+                "cited_paper_arxiv_link": datasets.Value("string"),
+                "cited_paper_abstract": datasets.Value("string"),
+                "has_metadata": datasets.Value("bool"),
+                "is_arxiv_paper": datasets.Value("bool"),
+                "bib_paper_authors": datasets.Value("string"),
+                "bib_paper_year": datasets.Value("float32"),
+                "bib_paper_month": datasets.Value("string"),
+                "bib_paper_url": datasets.Value("string"),
+                "bib_paper_doi": datasets.Value("string"),
+                "bib_paper_journal": datasets.Value("string"),
+                "original_title": datasets.Value("string"),
+                "search_res_title": datasets.Value("string"),
+                "search_res_url": datasets.Value("string"),
+                "search_res_content": datasets.Value("string"),
+            })
+        elif self.config.name == "important_citations":
+            features = datasets.Features({
+                "parent_paper_title": datasets.Value("string"),
+                "parent_paper_arxiv_id": datasets.Value("string"),
+                "citation_shorthand": datasets.Value("string"),
+                "raw_citation_text": datasets.Value("string"),
+                "cited_paper_title": datasets.Value("string"),
+                "cited_paper_arxiv_link": datasets.Value("string"),
+                "cited_paper_abstract": datasets.Value("string"),
+                "has_metadata": datasets.Value("bool"),
+                "is_arxiv_paper": datasets.Value("bool"),
+                "cited_paper_authors": datasets.Value("string"),
+                "bib_paper_year": datasets.Value("float32"),
+                "bib_paper_month": datasets.Value("string"),
+                "bib_paper_url": datasets.Value("string"),
+                "bib_paper_doi": datasets.Value("string"),
+                "bib_paper_journal": datasets.Value("string"),
+                "original_title": datasets.Value("string"),
+                "search_res_title": datasets.Value("string"),
+                "search_res_url": datasets.Value("string"),
+                "search_res_content": datasets.Value("string"),
+                "arxiv_id": datasets.Value("string"),
+                "arxiv_link": datasets.Value("string"),
+                "publication_date": datasets.Value("string"),
+                "title": datasets.Value("string"),
+                "abstract": datasets.Value("string"),
+                "raw_latex_related_works": datasets.Value("string"),
+                "related_work_section": datasets.Value("string"),
+                "pdf_related_works": datasets.Value("string"),
+                "cited_paper_content": datasets.Value("string"),
+            })
+        else:  # full config
+            features = datasets.Features({
+                # Papers features
+                "papers": datasets.Sequence({
+                    "arxiv_id": datasets.Value("string"),
+                    "title": datasets.Value("string"),
+                    "authors": datasets.Value("string"),
+                    "abstract": datasets.Value("string"),
+                    "categories": datasets.Value("string"),
+                    "published_date": datasets.Value("string"),
+                    "updated_date": datasets.Value("string"),
+                    "abs_url": datasets.Value("string"),
+                    "arxiv_link": datasets.Value("string"),
+                    "publication_date": datasets.Value("string"),
+                    "raw_latex_related_works": datasets.Value("string"),
+                    "clean_latex_related_works": datasets.Value("string"),
+                    "pdf_related_works": datasets.Value("string"),
+                }),
+                # Citations features
+                "citations": datasets.Sequence({
+                    "parent_paper_title": datasets.Value("string"),
+                    "parent_paper_arxiv_id": datasets.Value("string"),
+                    "citation_shorthand": datasets.Value("string"),
+                    "raw_citation_text": datasets.Value("string"),
+                    "cited_paper_title": datasets.Value("string"),
+                    "cited_paper_arxiv_link": datasets.Value("string"),
+                    "cited_paper_abstract": datasets.Value("string"),
+                    "has_metadata": datasets.Value("bool"),
+                    "is_arxiv_paper": datasets.Value("bool"),
+                    "bib_paper_authors": datasets.Value("string"),
+                    "bib_paper_year": datasets.Value("float32"),
+                    "bib_paper_month": datasets.Value("string"),
+                    "bib_paper_url": datasets.Value("string"),
+                    "bib_paper_doi": datasets.Value("string"),
+                    "bib_paper_journal": datasets.Value("string"),
+                    "original_title": datasets.Value("string"),
+                    "search_res_title": datasets.Value("string"),
+                    "search_res_url": datasets.Value("string"),
+                    "search_res_content": datasets.Value("string"),
+                }),
+                "important_citations": datasets.Sequence({
+                    "parent_paper_title": datasets.Value("string"),
+                    "parent_paper_arxiv_id": datasets.Value("string"),
+                    "citation_shorthand": datasets.Value("string"),
+                    "raw_citation_text": datasets.Value("string"),
+                    "cited_paper_title": datasets.Value("string"),
+                    "cited_paper_arxiv_link": datasets.Value("string"),
+                    "cited_paper_abstract": datasets.Value("string"),
+                    "has_metadata": datasets.Value("bool"),
+                    "is_arxiv_paper": datasets.Value("bool"),
+                    "cited_paper_authors": datasets.Value("string"),
+                    "bib_paper_year": datasets.Value("float32"),
+                    "bib_paper_month": datasets.Value("string"),
+                    "bib_paper_url": datasets.Value("string"),
+                    "bib_paper_doi": datasets.Value("string"),
+                    "bib_paper_journal": datasets.Value("string"),
+                    "original_title": datasets.Value("string"),
+                    "search_res_title": datasets.Value("string"),
+                    "search_res_url": datasets.Value("string"),
+                    "search_res_content": datasets.Value("string"),
+                    "arxiv_id": datasets.Value("string"),
+                    "arxiv_link": datasets.Value("string"),
+                    "publication_date": datasets.Value("string"),
+                    "title": datasets.Value("string"),
+                    "abstract": datasets.Value("string"),
+                    "raw_latex_related_works": datasets.Value("string"),
+                    "related_work_section": datasets.Value("string"),
+                    "pdf_related_works": datasets.Value("string"),
+                    "cited_paper_content": datasets.Value("string"),
+                })
+            })
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        """Return the dataset splits."""
+        
+        # For local files, use the actual file paths
+        import os
+        current_dir = os.path.dirname(os.path.abspath(__file__))
+        
+        if self.config.name == "papers":
+            data_file = os.path.join(current_dir, "papers_with_related_works.csv")
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    gen_kwargs={
+                        "filepath": data_file,
+                        "split": "papers"
+                    },
+                ),
+            ]
+        elif self.config.name == "citations":
+            data_file = os.path.join(current_dir, "recovered_citations.csv")
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    gen_kwargs={
+                        "filepath": data_file,
+                        "split": "citations"
+                    },
+                ),
+            ]
+        elif self.config.name == "important_citations":
+            data_file = os.path.join(current_dir, "important_citations.csv")
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    gen_kwargs={
+                        "filepath": data_file,
+                        "split": "important_citations"
+                    },
+                ),
+            ]
+        else:  # full config
+            papers_file = os.path.join(current_dir, "papers_with_related_works.csv")
+            citations_file = os.path.join(current_dir, "recovered_citations.csv")
+            important_citations_file = os.path.join(current_dir, "important_citations.csv")
+            return [
+                datasets.SplitGenerator(
+                    name="papers",
+                    gen_kwargs={
+                        "filepath": papers_file,
+                        "split": "papers"
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name="citations", 
+                    gen_kwargs={
+                        "filepath": citations_file,
+                        "split": "citations"
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name="important_citations",
+                    gen_kwargs={
+                        "filepath": important_citations_file,
+                        "split": "important_citations"
+                    },
+                ),
+            ]
+
+    def _generate_examples(self, filepath: str, split: str):
+        """Generate examples from the dataset."""
+        
+        def _safe_bool_convert(value: str) -> bool:
+            """Safely convert string to boolean."""
+            if isinstance(value, str):
+                return value.lower() in ('true', 'yes', '1')
+            return bool(value)
+        
+        def _safe_float_convert(value: str) -> Optional[float]:
+            """Safely convert string to float."""
+            if not value or value.strip() == '' or value.lower() == 'nan':
+                return None
+            try:
+                return float(value)
+            except (ValueError, TypeError):
+                return None
+
+        if split == "papers":
+            with open(filepath, encoding="utf-8") as f:
+                reader = csv.DictReader(f)
+                for key, row in enumerate(reader):
+                    yield key, {
+                        "arxiv_id": row.get("arxiv_id", ""),
+                        "title": row.get("title", ""),
+                        "authors": row.get("authors", ""),
+                        "abstract": row.get("abstract", ""),
+                        "categories": row.get("categories", ""),
+                        "published_date": row.get("published_date", ""),
+                        "updated_date": row.get("updated_date", ""),
+                        "abs_url": row.get("abs_url", ""),
+                        "arxiv_link": row.get("arxiv_link", ""),
+                        "publication_date": row.get("publication_date", ""),
+                        "raw_latex_related_works": row.get("raw_latex_related_works", ""),
+                        "clean_latex_related_works": row.get("clean_latex_related_works", ""),
+                        "pdf_related_works": row.get("pdf_related_works", ""),
+                    }
+
+        elif split == "citations":
+            with open(filepath, encoding="utf-8") as f:
+                reader = csv.DictReader(f)
+                for key, row in enumerate(reader):
+                    yield key, {
+                        "parent_paper_title": row.get("parent_paper_title", ""),
+                        "parent_paper_arxiv_id": row.get("parent_paper_arxiv_id", ""),
+                        "citation_shorthand": row.get("citation_shorthand", ""),
+                        "raw_citation_text": row.get("raw_citation_text", ""),
+                        "cited_paper_title": row.get("cited_paper_title", ""),
+                        "cited_paper_arxiv_link": row.get("cited_paper_arxiv_link", ""),
+                        "cited_paper_abstract": row.get("cited_paper_abstract", ""),
+                        "has_metadata": _safe_bool_convert(row.get("has_metadata", "False")),
+                        "is_arxiv_paper": _safe_bool_convert(row.get("is_arxiv_paper", "False")),
+                        "bib_paper_authors": row.get("bib_paper_authors", ""),
+                        "bib_paper_year": _safe_float_convert(row.get("bib_paper_year", "")),
+                        "bib_paper_month": row.get("bib_paper_month", ""),
+                        "bib_paper_url": row.get("bib_paper_url", ""),
+                        "bib_paper_doi": row.get("bib_paper_doi", ""),
+                        "bib_paper_journal": row.get("bib_paper_journal", ""),
+                        "original_title": row.get("original_title", ""),
+                        "search_res_title": row.get("search_res_title", ""),
+                        "search_res_url": row.get("search_res_url", ""),
+                        "search_res_content": row.get("search_res_content", ""),
+                    }
+        elif split == "important_citations":
+            with open(filepath, encoding="utf-8") as f:
+                reader = csv.DictReader(f)
+                for key, row in enumerate(reader):
+                    yield key, {
+                        "parent_paper_title": row.get("parent_paper_title", ""),
+                        "parent_paper_arxiv_id": row.get("parent_paper_arxiv_id", ""),
+                        "citation_shorthand": row.get("citation_shorthand", ""),
+                        "raw_citation_text": row.get("raw_citation_text", ""),
+                        "cited_paper_title": row.get("cited_paper_title", ""),
+                        "cited_paper_arxiv_link": row.get("cited_paper_arxiv_link", ""),
+                        "cited_paper_abstract": row.get("cited_paper_abstract", ""),
+                        "has_metadata": _safe_bool_convert(row.get("has_metadata", "False")),
+                        "is_arxiv_paper": _safe_bool_convert(row.get("is_arxiv_paper", "False")),
+                        "bib_paper_authors": row.get("bib_paper_authors", ""),
+                        "bib_paper_year": _safe_float_convert(row.get("bib_paper_year", "")),
+                        "bib_paper_month": row.get("bib_paper_month", ""),
+                        "bib_paper_url": row.get("bib_paper_url", ""),
+                        "bib_paper_doi": row.get("bib_paper_doi", ""),
+                        "bib_paper_journal": row.get("bib_paper_journal", ""),
+                        "original_title": row.get("original_title", ""),
+                        "search_res_title": row.get("search_res_title", ""),
+                        "search_res_url": row.get("search_res_url", ""),
+                        "search_res_content": row.get("search_res_content", ""),
+                    }

usage_example.py

@@ -0,0 +1,175 @@
+#!/usr/bin/env python3
+"""
+Usage example for the Lotus Deep Research dataset.
+
+This shows how to use the dataset builder directly, which is the recommended approach
+for local development and testing.
+"""
+
+import sys
+import os
+from pathlib import Path
+
+# Add the current directory to Python path
+sys.path.insert(0, str(Path(__file__).parent))
+
+from lotus_deep_research import LotusDeepResearch
+import pandas as pd
+
+
+def load_papers_dataset():
+    """Load the papers dataset."""
+    print("Loading papers dataset...")
+    
+    # Create dataset builder
+    builder = LotusDeepResearch(config_name="papers")
+    
+    # Mock download manager for local files
+    class MockDownloadManager:
+        def download_and_extract(self, url_or_path):
+            return url_or_path
+    
+    dl_manager = MockDownloadManager()
+    
+    # Get split generators and load data
+    split_generators = builder._split_generators(dl_manager)
+    split_gen = split_generators[0]  # Get train split
+    
+    # Collect all examples
+    papers_data = []
+    for key, example in builder._generate_examples(
+        split_gen.gen_kwargs["filepath"], 
+        split_gen.gen_kwargs["split"]
+    ):
+        papers_data.append(example)
+    
+    print(f"Loaded {len(papers_data)} papers")
+    return papers_data
+
+
+def load_citations_dataset():
+    """Load the citations dataset."""
+    print("Loading citations dataset...")
+    
+    # Create dataset builder
+    builder = LotusDeepResearch(config_name="citations")
+    
+    # Mock download manager for local files
+    class MockDownloadManager:
+        def download_and_extract(self, url_or_path):
+            return url_or_path
+    
+    dl_manager = MockDownloadManager()
+    
+    # Get split generators and load data
+    split_generators = builder._split_generators(dl_manager)
+    split_gen = split_generators[0]  # Get train split
+    
+    # Collect all examples
+    citations_data = []
+    for key, example in builder._generate_examples(
+        split_gen.gen_kwargs["filepath"], 
+        split_gen.gen_kwargs["split"]
+    ):
+        citations_data.append(example)
+    
+    print(f"Loaded {len(citations_data)} citations")
+    return citations_data
+
+
+def load_important_citations_dataset():
+    """Load the important citations dataset."""
+    print("Loading important citations dataset...")
+    
+    # Create dataset builder
+    builder = LotusDeepResearch(config_name="important_citations")
+    
+    # Mock download manager for local files
+    class MockDownloadManager:
+        def download_and_extract(self, url_or_path):
+            return url_or_path
+    
+    dl_manager = MockDownloadManager()
+    
+    # Get split generators and load data
+    split_generators = builder._split_generators(dl_manager)
+    split_gen = split_generators[0]  # Get train split
+    
+    # Collect all examples
+    important_citations_data = []
+    for key, example in builder._generate_examples(
+        split_gen.gen_kwargs["filepath"], 
+        split_gen.gen_kwargs["split"]
+    ):
+        important_citations_data.append(example)
+    
+    print(f"Loaded {len(important_citations_data)} important citations")
+    return important_citations_data
+
+
+def main():
+    """Main example function."""
+    print("Lotus Deep Research Dataset - Usage Example")
+    print("=" * 50)
+    
+    # Load datasets
+    papers = load_papers_dataset()
+    citations = load_citations_dataset()
+    important_citations = load_important_citations_dataset()
+    
+    print("\n📊 Dataset Statistics:")
+    print(f"  - Papers: {len(papers)}")
+    print(f"  - Citations: {len(citations)}")
+    print(f"  - Important Citations: {len(important_citations)}")
+    
+    # Show sample paper
+    if papers:
+        sample_paper = papers[0]
+        print(f"\n📄 Sample Paper:")
+        print(f"  - Title: {sample_paper['title']}")
+        print(f"  - ArXiv ID: {sample_paper['arxiv_id']}")
+        print(f"  - Authors: {sample_paper['authors'][:100]}...")
+        print(f"  - Abstract: {sample_paper['abstract'][:200]}...")
+    
+    # Show sample citation
+    if citations:
+        sample_citation = citations[0]
+        print(f"\n📚 Sample Citation:")
+        print(f"  - Parent Paper: {sample_citation['parent_paper_title']}")
+        print(f"  - Cited Paper: {sample_citation['cited_paper_title']}")
+        print(f"  - Has Metadata: {sample_citation['has_metadata']}")
+        print(f"  - Is ArXiv Paper: {sample_citation['is_arxiv_paper']}")
+    
+    # Show sample important citation
+    if important_citations:
+        sample_important_citation = important_citations[0]
+        print(f"\n⭐ Sample Important Citation:")
+        print(f"  - Parent Paper: {sample_important_citation['parent_paper_title']}")
+        print(f"  - Cited Paper: {sample_important_citation['cited_paper_title']}")
+        print(f"  - Has Metadata: {sample_important_citation['has_metadata']}")
+        print(f"  - Is ArXiv Paper: {sample_important_citation['is_arxiv_paper']}")
+    
+    # Convert to pandas for easier analysis
+    print(f"\n🐼 Converting to Pandas DataFrames...")
+    papers_df = pd.DataFrame(papers)
+    citations_df = pd.DataFrame(citations)
+    important_citations_df = pd.DataFrame(important_citations)
+    
+    print(f"  - Papers DataFrame: {papers_df.shape}")
+    print(f"  - Citations DataFrame: {citations_df.shape}")
+    print(f"  - Important Citations DataFrame: {important_citations_df.shape}")
+    
+    # Show some analysis
+    print(f"\n📈 Quick Analysis:")
+    print(f"  - Unique parent papers in citations: {citations_df['parent_paper_arxiv_id'].nunique()}")
+    print(f"  - Citations with metadata: {citations_df['has_metadata'].sum()}")
+    print(f"  - ArXiv citations: {citations_df['is_arxiv_paper'].sum()}")
+    print(f"  - Unique parent papers in important citations: {important_citations_df['parent_paper_arxiv_id'].nunique()}")
+    print(f"  - Important citations with metadata: {important_citations_df['has_metadata'].sum()}")
+    print(f"  - ArXiv important citations: {important_citations_df['is_arxiv_paper'].sum()}")
+    
+    return papers_df, citations_df, important_citations_df
+
+
+if __name__ == "__main__":
+    papers_df, citations_df, important_citations_df = main()