| --- |
| license: mit |
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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 |
| - `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 |
|
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| ## Usage |
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| ```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) |
| ``` |
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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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| ``` |
| 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..." |
| ... |
| ``` |
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| ## Data Statistics |
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| - **File size**: ~287MB |
| - **Format**: CSV with comma-separated values |
| - **Encoding**: UTF-8 |
| - **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 |
| - **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 |
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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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