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DataStorehouse Contribution Guidelines

Thank you for your interest in contributing to DataStorehouse! We welcome contributions from the community to help grow and improve the dataset repository. Please take a moment to review the guidelines below before making a contribution.

How to Contribute

  1. Fork the DataStorehouse repository on GitHub.

  2. Clone your forked repository to your local machine:

git clone https://github.com/your-username/DataStorehouse.git
  1. Create a new branch for your contribution:
git checkout -b your-branch-name

  1. Add your datasets to the appropriate domain folder under the StoreHouse directory. Make sure to follow the naming conventions and provide a descriptive README file for each dataset.

  2. Commit your changes with a meaningful commit message:

git add .
git commit -m "Add datasets for <domain>"
  1. Push your branch to your forked repository:
git push origin your-branch-name

  1. Open a pull request from your branch to the main DataStorehouse repository.

  2. Wait for the repository maintainers to review your contribution. They may provide feedback or request changes before merging your pull request.

  3. Once your pull request is approved and merged, your datasets will be added to the DataStorehouse repository!

Guidelines for Dataset Contribution

  • Ensure that the datasets you contribute are relevant, accurate, and adhere to legal and ethical standards.

  • Include a detailed README file for each dataset, providing information such as description, data source, format, licensing, and any special instructions for usage.

  • If the dataset requires preprocessing or additional steps to be usable, provide clear instructions in the README file.

  • Include appropriate attribution and credits for the dataset, including the original source if applicable.

  • If you are contributing someone else's work or modifying an existing dataset, make sure you have the necessary permissions or rights to do so.

  • Avoid including sensitive or private information in the datasets. Respect privacy and data protection guidelines.

  • Follow best practices for data formatting, organization, and documentation to ensure the usability of the datasets.

  • Be open to feedback and collaboration from the community to improve the quality of the datasets.

Code of Conduct

Please note that by contributing to DataStorehouse, you are expected to follow the Code of Conduct to ensure a respectful and inclusive environment for all contributors.

We appreciate your contributions and look forward to building a comprehensive repository of high-quality datasets together!

If you have any questions or need further assistance, feel free to reach out to us at datashcontribution@gmail.com .

Happy contributing!