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license: mit |
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--- |
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# NeurIPS Papers Dataset |
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This dataset contains information about NeurIPS conference paper submissions including peer reviews, author rebuttals, and decision outcomes across multiple years. |
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## Files |
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- `dataset.csv`: Main dataset file containing all paper submission data |
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## Dataset Structure |
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The CSV file contains the following columns: |
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- `title`: Paper title |
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- `paper_decision`: Decision outcome (Accept/Reject with specific categories) |
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- `review_1`, `review_2`, etc.: Peer reviews from different reviewers |
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- `rebuttals_1`, `rebuttals_2`, etc.: Author rebuttals responding to reviews |
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- `global_rebuttals`: Overall author responses |
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- `dataset_source`: Source of the data |
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- `conference_year`: Year of the conference |
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## Usage |
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```python |
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import pandas as pd |
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# Load the dataset |
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df = pd.read_csv('merged_neurips_dataset.csv') |
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# Example: Print first paper title |
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print(df['title'].iloc[0]) |
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# Example: Filter accepted papers |
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accepted_papers = df[df['paper_decision'].str.contains('Accept', na=False)] |
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print(f"Number of accepted papers: {len(accepted_papers)}") |
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# Example: Analyze decision distribution |
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decision_counts = df['paper_decision'].value_counts() |
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print(decision_counts) |
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``` |
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## Sample Data Structure |
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Each row represents a paper submission with associated reviews and rebuttals: |
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``` |
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title: "Stress-Testing Capability Elicitation With Password-Locked Models" |
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paper_decision: "Accept (poster)" |
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review_1: "Summary: The paper studies whether fine-tuning can elicit..." |
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rebuttals_1: "Rebuttal 1: Thanks for the review! We are glad you found..." |
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... |
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``` |
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## Data Statistics |
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- **File size**: ~287MB |
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- **Format**: CSV with comma-separated values |
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- **Encoding**: UTF-8 |
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- **Contains**: Paper reviews, rebuttals, and metadata from NeurIPS conferences |
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## Use Cases |
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This dataset is valuable for: |
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- **Peer review analysis**: Study patterns in academic peer review |
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- **Natural language processing**: Train models on academic text |
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- **Research evaluation**: Analyze correlation between reviews and acceptance |
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- **Academic writing**: Understand successful paper characteristics |
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- **Sentiment analysis**: Analyze reviewer sentiment and author responses |
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## Citation |
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If you use this dataset in your research, please cite appropriately and ensure compliance with NeurIPS terms of service. |
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## License |
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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. |
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