fix path

DeepScholarBench.py

@@ -176,12 +176,8 @@ class DeepScholarBench(datasets.GeneratorBasedBuilder):
     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")
+            data_file = dl_manager.download_and_extract(_URLS["papers"])
             return [
                 datasets.SplitGenerator(
                     name=datasets.Split.TRAIN,
@@ -192,7 +188,7 @@ class DeepScholarBench(datasets.GeneratorBasedBuilder):
                 ),
             ]
         elif self.config.name == "citations":
-            data_file = os.path.join(current_dir, "recovered_citations.csv")
+            data_file = dl_manager.download_and_extract(_URLS["citations"])
             return [
                 datasets.SplitGenerator(
                     name=datasets.Split.TRAIN,
@@ -203,7 +199,7 @@ class DeepScholarBench(datasets.GeneratorBasedBuilder):
                 ),
             ]
         elif self.config.name == "important_citations":
-            data_file = os.path.join(current_dir, "important_citations.csv")
+            data_file = dl_manager.download_and_extract(_URLS["important_citations"])
             return [
                 datasets.SplitGenerator(
                     name=datasets.Split.TRAIN,
@@ -214,9 +210,9 @@ class DeepScholarBench(datasets.GeneratorBasedBuilder):
                 ),
             ]
         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")
+            papers_file = dl_manager.download_and_extract(_URLS["papers"])
+            citations_file = dl_manager.download_and_extract(_URLS["citations"])
+            important_citations_file = dl_manager.download_and_extract(_URLS["important_citations"])
             return [
                 datasets.SplitGenerator(
                     name="papers",

README.md

@@ -141,15 +141,15 @@ Contains enhanced citations with full paper metadata and content:
 from datasets import load_dataset
 
 # Load papers dataset
-papers = load_dataset("deepscholar-bench/DeepScholarBench", name="papers")["train"]
+papers = load_dataset("deepscholar-bench/DeepScholarBench", name="papers", trust_remote_code=True)["train"]
 print(f"Loaded {len(papers)} papers")
 
 # Load citations dataset  
-citations = load_dataset("deepscholar-bench/DeepScholarBench", name="citations")["train"]
+citations = load_dataset("deepscholar-bench/DeepScholarBench", name="citations", trust_remote_code=True)["train"]
 print(f"Loaded {len(citations)} citations")
 
 # Load important citations with enhanced metadata
-important_citations = load_dataset("deepscholar-bench/DeepScholarBench", name="important_citations")["train"]
+important_citations = load_dataset("deepscholar-bench/DeepScholarBench", name="important_citations", trust_remote_code=True)["train"]
 print(f"Loaded {len(important_citations)} important citations")
 
 # Convert to pandas for analysis
@@ -157,6 +157,26 @@ papers_df = papers.to_pandas()
 citations_df = citations.to_pandas()
 ```
 
+**Note**: This dataset uses a custom loading script, so you need to include `trust_remote_code=True` when loading.
+
+### Alternative: Direct CSV Loading
+
+```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('recovered_citations.csv')
+print(f"Loaded {len(citations_df)} citations")
+
+# Load important citations
+important_citations_df = pd.read_csv('important_citations.csv')
+print(f"Loaded {len(important_citations_df)} important citations")
+```
+
 ### Example: Extract Related Works for a Paper
 
 ```python
@@ -170,6 +190,25 @@ paper_citations = citations_df[citations_df['parent_paper_arxiv_id'] == '2506.02
 print(f"Number of citations: {len(paper_citations)}")
 ```
 
+### Example: Working with Important Citations
+
+```python
+# Load important citations (enhanced with paper metadata)
+important_citations = load_dataset("deepscholar-bench/DeepScholarBench", name="important_citations", trust_remote_code=True)["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
@@ -179,7 +218,7 @@ print(f"Number of citations: {len(paper_citations)}")
 
 ## 🔧 Data Collection Process
 
-This dataset was created using the [Lotus Deep Research](https://github.com/guestrin-lab/deepscholar-bench) pipeline:
+This dataset was created using the [DeepScholarBench](https://github.com/guestrin-lab/deepscholar-bench) pipeline:
 
 1. **ArXiv Scraping**: Collected papers by category and date range
 2. **Author Filtering**: Focused on high-impact researchers (h-index ≥ 25)