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An Automated Research Intelligence Assistant

aria 최신버젼 다운로드

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# ARIA - Automated Research Intelligence Assistant

**🚀 Transform months of research into minutes of insight**

English | [中文](./README_zh.md)

ARIA is an automated research assistant framework for scientific data analysis, visualization, and report generation.

## Project Structure

```
aria/
├── .claude/commands/           # Claude AI command files
│   ├── academic/               # Academic workflow commands
│   ├── git/                    # Git operation commands
│   └── python/                 # Python environment commands
├── data/
│   ├── raw/                   # Original data
│   ├── processed/              # Preprocessed data
│   └── output/                 # Experiment outputs
│       ├── models/             # Trained models
│       ├── results/            # Experimental results
│       ├── figures/            # Visualization charts
│       └── logs/               # Execution logs
├── docs/                       # Project documentation
├── src/                        # Source code
├── scripts/                    # Execution scripts
├── GETTING_STARTED.md          # Quick start guide
├── INSTALL.md                  # Installation guide
└── README.md                   # This file
```

## Workflow

### 1. Data Preparation

- Place raw data in the `data/raw/` folder
- Manually create `docs/01-basic-information.md` to describe project background, objectives, and data overview

### 2. Data Analysis

- **Raw Data Analysis**: Use `@raw-data-analysis.md` command to analyze raw data, generating `docs/02-raw-data-analysis.md`
- **Data Preprocessing**: Use `@preprocess.md` command to design preprocessing plan (`docs/03-preprocess-plan.md`), execute preprocessing, analyze processed data (`docs/04-processed-data-analysis.md`)

### 3. Research Design

- **Research Plan**: Use `@research-plan.md` command to develop research plan including feature engineering, model selection, evaluation metrics, generating `docs/05-research-plan.md`

### 4. Code Implementation

- **Code Development**: Use `@code-implementation.md` command to implement research plan, creating necessary Python modules
- Generate `docs/06-implementation-docs.md` (implementation documentation) and `docs/07-execution-instructions.md` (execution guide)
- Code quality check: Use mypy and ruff to ensure code quality

### 5. Experiment Execution

- **Run Experiments**: Use `@run-experiments.md` command to execute experiment scripts
- Outputs saved to corresponding subfolders in `output/` directory

### 6. Results Analysis

- **Results Analysis**: Use `@experiment-analysis.md` command to analyze experiment outputs
- Generate individual analyses in `docs/08-experiment-results/` directory
- Generate `docs/09-experiment-report.md` comprehensive experiment report

### 7. Paper Writing

- **Academic Paper**: Use `@research-report.md` command to generate high-impact journal format paper
- Generate `docs/10-manuscript.md` (main text), `docs/10-manuscript-supplement.md` (supplementary materials), `docs/10-cover-letter.md` (cover letter)

### 8. Model Deployment (Optional)

- **Gradio Interface**: If models are trained, use `@gradio-app.md` command to create model inference interface
- Generate `docs/11-model-deployment-guide.md` deployment guide

## Claude AI Commands

### Academic Commands

All academic command files are located in `.claude/commands/academic/` directory:

- `@raw-data-analysis.md` - Analyze raw data
- `@preprocess.md` - Data preprocessing
- `@research-plan.md` - Research plan design
- `@code-implementation.md` - Code implementation
- `@run-experiments.md` - Experiment execution
- `@experiment-analysis.md` - Results analysis
- `@research-report.md` - Academic paper generation
- `@gradio-app.md` - Model deployment interface
- `@convert2docs.md` - Convert markdown to DOCX (optional)

### Python Environment Commands

Python command files are located in `.claude/commands/python/` directory:

- `@setup-environment.md` - Automated environment setup
  - Checks and installs Git, Python 3.12+, and UV package manager
  - Sets up project dependencies with `uv sync`
  - Includes Tsinghua mirror fallback for network issues
  - Provides complete environment verification

### Git Commands

Git command files are located in `.claude/commands/git/` directory:

- `@git-commit.md` - Intelligent Git commits
  - Automatically creates structured commit messages
  - When there are many modified files, automatically commits in batches (max 10 files per commit)
  - Generates meaningful commit descriptions based on code changes

## Example Projects

Complete example projects demonstrating ARIA workflow across different research tasks:

- 🏠 [**aria-example-buston**](https://github.com/Biaoo/aria-example-buston) - Boston housing price prediction (regression) | [OpenML Dataset](https://www.openml.org/d/531)
- 💎 [**aria-example-diamonds**](https://github.com/Biaoo/aria-example-diamonds) - Diamond price prediction (regression) | [OpenML Dataset](https://www.openml.org/d/42225)
- 🐛 [**aria-example-kc1**](https://github.com/Biaoo/aria-example-kc1) - Software defect prediction (classification) | [OpenML Dataset](https://www.openml.org/d/1067)
- 🧩 [**aria-example-sat11**](https://github.com/Biaoo/aria-example-sat11) - SAT solver performance prediction (regression) | [OpenML Dataset](https://www.openml.org/d/41980)

All datasets are from [OpenML](https://www.openml.org/), an open machine learning platform. Each project includes complete documentation, production-ready code, trained models, and academic manuscripts.

## Quick Start

**New to ARIA? Start here: [Getting Started Guide](./GETTING_STARTED.md)** 📖

The complete guide covers:

- Installing Git and AI code editor (Cursor/VSCode/Lingma IDE)
- Automated environment setup with `@setup-environment.md`
- Step-by-step workflow from data to paper
- Troubleshooting and best practices

### For Experienced Users

1. **Setup Environment**: Use `@setup-environment.md` to configure Python and dependencies
2. **Prepare Data**: Place raw data in `data/raw/`
3. **Create Project Description**: Write `docs/01-basic-information.md`
4. **Execute Workflow**: Use Claude AI to execute academic commands in sequence
5. **Version Control**: Use `@git-commit.md` for intelligent commits

📚 **Full Documentation**: See [GETTING_STARTED.md](./GETTING_STARTED.md) and [INSTALL.md](./INSTALL.md)

## Dependency Management

Using UV for Python dependency management:

```bash
uv add <package-name>  # Add new dependency
uv sync                # Synchronize dependencies
```

## Code Quality

- Type checking: `mypy src/`
- Code style: `ruff check src/`
- Code formatting: `ruff format src/`

## Features

- 📊 **Data-Driven**: Complete workflow from raw data to academic paper
- 🤖 **AI-Assisted**: Claude AI commands automate each research phase
- 📝 **Academic Standards**: Generate Nature/Science standard papers
- 🎯 **Quality Assurance**: Integrated code quality checking tools
- 🚀 **Model Deployment**: Support for Gradio interface rapid deployment
- 📦 **Version Control**: Intelligent Git commit management

## License

This project is dual-licensed:

- **GNU AGPL-3.0** - for open source use (personal, research, education, open source projects)
- **Commercial License** - for proprietary/commercial use (contact for licensing)

See the [LICENSE](LICENSE) file for details.

## Contributing

Contributions are welcome! Please feel free to submit Issues and Pull Requests.

## Contact

For questions, suggestions, or collaboration opportunities:

- **GitHub Issues**: [Create an issue](https://github.com/Biaoo/aria/issues)
- **Email**: [biao00luo@gmail.com](mailto:biao00luo@gmail.com)
- **Project**: [ARIA on GitHub](https://github.com/Biaoo/aria)

### Join Our Community

<div align="center">
  <img src="assets/wechat-qr-20251021.png" alt="WeChat Group QR Code" width="200"/>
  <p>Scan to join WeChat discussion group</p>
</div>

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