---
license: mit
---

# NeurIPS Papers Dataset

This dataset contains information about NeurIPS conference paper submissions including peer reviews, author rebuttals, and decision outcomes across multiple years.

## Files

- `dataset.csv`: Main dataset file containing all paper submission data

## Dataset Structure

The CSV file contains the following columns:

- `title`: Paper title
- `paper_decision`: Decision outcome (Accept/Reject with specific categories)
- `review_1`, `review_2`, etc.: Peer reviews from different reviewers
- `rebuttals_1`, `rebuttals_2`, etc.: Author rebuttals responding to reviews
- `global_rebuttals`: Overall author responses
- `dataset_source`: Source of the data
- `conference_year`: Year of the conference

## Usage

```python
import pandas as pd

# Load the dataset
df = pd.read_csv('merged_neurips_dataset.csv')

# Example: Print first paper title
print(df['title'].iloc[0])

# Example: Filter accepted papers
accepted_papers = df[df['paper_decision'].str.contains('Accept', na=False)]
print(f"Number of accepted papers: {len(accepted_papers)}")

# Example: Analyze decision distribution
decision_counts = df['paper_decision'].value_counts()
print(decision_counts)
```

## Sample Data Structure

Each row represents a paper submission with associated reviews and rebuttals:

```
title: "Stress-Testing Capability Elicitation With Password-Locked Models"
paper_decision: "Accept (poster)"
review_1: "Summary: The paper studies whether fine-tuning can elicit..."
rebuttals_1: "Rebuttal 1: Thanks for the review! We are glad you found..."
...
```

## Data Statistics

- **File size**: ~287MB
- **Format**: CSV with comma-separated values
- **Encoding**: UTF-8
- **Contains**: Paper reviews, rebuttals, and metadata from NeurIPS conferences

## Use Cases

This dataset is valuable for:

- **Peer review analysis**: Study patterns in academic peer review
- **Natural language processing**: Train models on academic text
- **Research evaluation**: Analyze correlation between reviews and acceptance
- **Academic writing**: Understand successful paper characteristics
- **Sentiment analysis**: Analyze reviewer sentiment and author responses

## Citation

If you use this dataset in your research, please cite appropriately and ensure compliance with NeurIPS terms of service.

## License

This dataset is released under the MIT License. Please ensure you have appropriate permissions to use this data and comply with NeurIPS's terms of service.