## 🙋‍♀️ 🙋‍♂️ Community Contributions

> 👋 **IMPORTANT** – Environment Setup
>
> - If you haven't already, follow the instructions in the [README](../../README.md) to install the necessary dependencies.
>
> - You may need to restart your notebook's kernel after setting up the environment.
>
> - In these notebooks, we assume you have a self-hosted instance of Data Designer up and running.
>
> - For deployment instructions, see the [Installation Options](https://docs.nvidia.com/nemo/microservices/latest/design-synthetic-data-from-scratch-or-seeds/index.html#installation-options) section of the [NeMo Data Designer documentation](https://docs.nvidia.com/nemo/microservices/latest/design-synthetic-data-from-scratch-or-seeds/index.html).

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| Notebook                                          | Domain              | Description                                                     |
|---------------------------------------------------|---------------------|-----------------------------------------------------------------|
| [person-sampler-tutorial.ipynb](./person-samplers/person-sampler-tutorial.ipynb) | Persona Samplers    | Generate realistic personas using the person sampler |
| [clinical-trials.ipynb](./healthcare-datasets/clinical-trials.ipynb) | Healthcare          | Build synthetic clinical trial datasets with realistic PII for testing data protection |
| [insurance-claims.ipynb](./healthcare-datasets/insurance-claims.ipynb) | Healthcare          | Create synthetic insurance claims datasets with realistic claim data and processing information |
| [physician-notes-with-realistic-personal-details.ipynb](./healthcare-datasets/physician-notes-with-realistic-personal-details.ipynb) | Healthcare          | Generate realistic patient data and physician notes with embedded personal information |
| [w2-dataset.ipynb](./forms/w2-dataset.ipynb) | Forms & Documents   | Generate synthetic W-2 tax form datasets with realistic employee and employer information |
| [multi-turn-conversation.ipynb](./multi-turn-chat/multi-turn-conversation.ipynb) | Conversational AI   | Build synthetic conversational data with realistic person details and multi-turn dialogues |
| [visual-question-answering-using-vlm.ipynb](./multimodal/visual-question-answering-using-vlm.ipynb) | Multimodal          | Create visual question answering datasets using Vision Language Models |
| [product-question-answer-generator.ipynb](./qa-generation/product-question-answer-generator.ipynb) | Q&A Generation      | Build product information datasets with corresponding questions and answers |
| [generate-rag-evaluation-dataset.ipynb](./rag-examples/generate-rag-evaluation-dataset.ipynb) | RAG & Retrieval     | Generate diverse RAG evaluation datasets for testing retrieval-augmented generation systems |
| [reasoning-traces.ipynb](./reasoning/reasoning-traces.ipynb) | Reasoning           | Build synthetic reasoning traces to demonstrate step-by-step problem-solving processes |
| [text-to-python.ipynb](./text-to-code/text-to-python.ipynb) | Text-to-Code        | Generate Python code from natural language instructions with validation and evaluation |
| [text-to-python-evol.ipynb](./text-to-code/text-to-python-evol.ipynb) | Text-to-Code        | Build advanced Python code generation with evolutionary improvements and iterative refinement |
| [text-to-sql.ipynb](./text-to-code/text-to-sql.ipynb) | Text-to-Code        | Create SQL queries from natural language descriptions with validation and testing |
| [japanese_commonsense_qa_data_generator_nemotron_persona_jp_seed.ipynb](./nemotron-persona-jp/japanese_commonsense_qa_data_generator_nemotron_persona_jp_seed.ipynb) | Q&A Generation         | A workflow that extracts personas from the Nemotron-Personas-Japan dataset and generates questions and answers in the JCommonsenseQA format. |
