| --- |
| license: other |
| task_categories: |
| - text-classification |
| - sentence-similarity |
| - feature-extraction |
| language: |
| - en |
| tags: |
| - github |
| - issues |
| - semantic-search |
| - text-data |
| - nlp |
| size_categories: |
| - 10M<n<100M |
| pretty_name: GitHub Issues Dataset |
| --- |
| |
| # Dataset Card for GitHub Issues Dataset |
|
|
| ## Dataset Description |
| This dataset contains raw GitHub issue records prepared for natural language processing tasks. |
|
|
| The dataset was created from GitHub issue data and includes text fields such as title, body, and comments. No major cleaning or filtering has been applied. In particular: |
|
|
| - Pull requests are NOT removed |
| - Missing or incomplete records are NOT removed |
| - The dataset reflects real-world, unclean data |
|
|
| A column named `is_pull_request` is included to indicate whether each record corresponds to a pull request or a standard issue. |
|
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| The purpose of keeping the dataset in raw form is to allow preprocessing, cleaning, and feature engineering to be demonstrated as part of the analysis pipeline. |
|
|
| ## License |
|
|
| This dataset is derived from publicly available GitHub issue data. |
|
|
| The original content belongs to the respective repository owners and contributors. |
| This dataset is shared for educational and research purposes only. |
|
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| Users must comply with GitHub Terms of Service and the licenses of the original repositories. |
|
|
| ## Dataset Structure |
|
|
| Each record represents one GitHub issue or pull request, including metadata, user information, and discussion content. |
|
|
| ### Data Fields |
|
|
| The dataset includes the following key fields: |
|
|
| #### Core Issue Information |
| - `id`: Unique identifier of the issue |
| - `number`: Issue number in the repository |
| - `title`: Title of the issue |
| - `body`: Main description text (may be empty) |
| - `state`: Status of the issue (open/closed) |
| - `locked`: Indicates if the issue is locked |
| - `comments`: List of comments associated with the issue |
|
|
| #### URLs and References |
| - `url`, `html_url`: API and web links to the issue |
| - `repository_url`: Link to the repository |
| - `comments_url`, `events_url`, `labels_url`: Related API endpoints |
|
|
| #### User Information |
| - `user`: Information about the issue creator (e.g., login, id) |
| - `assignee`: Assigned user (if available) |
| - `assignees`: List of assigned users |
| - `closed_by`: User who closed the issue |
|
|
| #### Labels and Tags |
| - `labels`: List of labels associated with the issue (name, color, description) |
|
|
| #### Time Information |
| - `created_at`: Issue creation timestamp |
| - `updated_at`: Last update timestamp |
| - `closed_at`: Issue closing timestamp |
|
|
| #### Pull Request Information |
| - `pull_request`: Contains pull request metadata if the record is a pull request |
| - `is_pull_request`: Boolean flag indicating whether the record is a pull request |
|
|
| #### Additional Metadata |
| - `milestone`: Milestone information (if available) |
| - `author_association`: Relationship of the author to the repository |
| - `reactions`: Reaction counts (likes, etc.) |
| - `timeline_url`: Timeline of issue events |
| - `state_reason`: Reason for closing (if available) |
|
|
| ## Data Preprocessing |
|
|
| This dataset is intentionally kept in raw format. |
|
|
| No major preprocessing steps such as removing missing values or filtering pull requests were applied. |
|
|
| However, a helper column (`is_pull_request`) was added to allow users to filter the dataset depending on their task. |
|
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| Users of this dataset are expected to perform their own preprocessing steps, such as: |
|
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| - Removing or handling missing values |
| - Filtering pull requests if needed |
| - Cleaning text data |
| - Combining fields (e.g., title + body + comments) |
|
|
| ## Intended Uses |
|
|
| This dataset is intended for educational and research purposes, particularly for: |
|
|
| - Natural Language Processing (NLP) |
| - Semantic search using embeddings (e.g., FAISS) |
| - Text similarity and clustering |
| - Feature extraction and data preprocessing exercises |
|
|
| The dataset is especially suitable for demonstrating data cleaning, feature engineering, and handling of raw, unstructured data. |
|
|
| ## Out-of-Scope Uses |
|
|
| This dataset should NOT be used for: |
|
|
| - Identifying or profiling individual GitHub users |
| - Making legal, financial, or employment decisions |
| - Training high-risk AI systems without proper data cleaning and validation |
| - Any use involving sensitive personal data without preprocessing and anonymisation |
|
|
| ## Limitations |
|
|
| - The dataset contains missing and incomplete text fields. |
| - Pull requests and issues are mixed together. |
| - Text data may include noise, informal language, or irrelevant content. |
| - Some records may not be useful without preprocessing. |
| - Public GitHub data may still contain unintended sensitive or personal information. |
|
|
| Because the dataset is raw, preprocessing is required before using it for machine learning tasks. |
|
|
| ## Ethical Considerations |
|
|
| Since this dataset is collected from public GitHub sources and kept in raw form: |
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| - It may contain usernames or references to individuals |
| - It may include unintended sensitive information in issue text |
| - No automatic anonymisation has been applied |
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| Users must review and clean the dataset before using it in any sensitive or production context. |
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| Personal or sensitive information should be removed or masked where not necessary, in line with organisational AI and data governance policies. |
|
|
| ## Data Classification |
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| This dataset is classified as **Internal/Public (educational use)**. |
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| Although the original data is publicly available, the processed dataset should still be handled responsibly. Data classification and handling should follow organisational rules for confidentiality, integrity, and access control |
