🍳 Dataverse Recipes

A collection of code recipes and examples for interacting with Dataverse using different programming languages and tools. This repository serves as a practical resource for developers who need to integrate with Dataverse in their applications.

Why this repository?

This repository serves as a collection of examples and recipes for working with Dataverse, covering common topics such as:

• 🔌 How to interact with Dataverse APIs
• 🔄 Common integration patterns
• ✅ Best practices for different programming languages
• 💡 Helpful solutions for typical use cases

Repository Structure

The repository is organized by programming language and tool:

• python/: 🐍 Python recipes
• shell/: 🐚 Shell recipes
• rust/: 🦀 Rust recipes
• dvcli/: 🛠️ DVCLI recipes

Each language directory contains specific recipes organized by functionality or use case.

📚 Available Recipes

In the following sections, you can find a list of available recipes for each language:

Python 🐍

• Create datasets from Excel files 📊
• Create Croissant from the client side

Shell 🐚

• Upload files using S3 direct upload

🤝 Contributing

We welcome contributions! To add a new recipe:

1. Choose the appropriate language directory for your recipe 📂

2. Create a new directory for your specific recipe ➕

3. Include the following files:

   • README.md with:
     • Description of the recipe 📝
     • Prerequisites ✔️
     • Installation instructions 🔧
     • Usage examples 💻
     • Dependencies 📦
   • Source code files 👨‍💻
   • requirements.txt (for Python) or equivalent dependency file 📋
   • Example configuration files if needed ⚙️

4. Submit your contribution via a Pull Request 🚀

✨ Guidelines

• Keep recipes focused and well-documented 📖
• Include error handling and best practices 🛡️
• Test your code before submitting ✅
• Update the main README if adding new categories 📝
• Follow the existing directory structure 🏗️
• Suggest a way to get in touch

Naming conventions

• File names should be in lowercase and use underscores to separate words (e.g. create_dataset.py).
• Directory names should be in lowercase and use underscores to separate words (e.g. create_dataset).

Note: If a language convention requires it, use camel/pascal case, but make sure to align with the existing naming conventions. Exceptions are:

• JavaScript: Use camel/kebab case (e.g. create-dataset.js or createDataset.js).
• Java: Use camel case (e.g. CreateDataset.java).

💬 Support

For issues and questions, please open an issue in this repository or discuss on Zulip! 🐙