opendata-tool 0.22.29

Scientific metadata preparation and packaging tool

OpenData Tool

Version: 0.22.23 | Status: Alpha | License: MIT

A browser-centric, AI-assisted tool for preparing scientific metadata and packaging research projects for the RODBUK repository.

Key Features

• Browser-Based UI: Powered by NiceGUI for a modern, responsive experience.
• Desktop Control Anchor: A non-intimidating "Control Window" for process monitoring and simple shutdowns.
• Interactive Chat Loop: An agent-driven Q&A interface with stop control and transparent status reporting.
• High-Performance Explorer: A scalable File Explorer for managing massive datasets without UI lag.
• Domain Meta-Learning: Automatically learns and stores "Field Protocols" based on user interaction.
• Secure & Read-Only: Strictly analyzes project directories without modification.
• Frictionless AI: Uses OAuth2 (Sign-in with Google) for Gemini access; no manual API keys required.

Installation

Option 1: Download Pre-built Binaries (Recommended for Users)

Visit the Releases page and download the binary for your platform:

• Linux: opendata-linux
• Windows: opendata-win.exe
• macOS (Apple Silicon): opendata-macos-arm

Simply run the binary - no installation required!

Option 2: Install from PyPI (Lightweight, for Developers)

Prerequisites: Python 3.11+

Using uvx (Recommended - Fastest, Cross-Platform)

uv is an extremely fast Python package manager written in Rust. It works on Windows, macOS, and Linux.

# Install uv (one-time setup)
curl -LsSf https://astral.sh/uv/install.sh | sh  # macOS/Linux
powershell -c "irm https://astral.sh/uv/install.ps1 | iex"  # Windows

# Run OpenData Tool instantly (no manual installation needed)
uvx opendata-tool

# Or install permanently
uv tool install opendata-tool
uvx opendata-tool

Using pipx (Alternative - Cross-Platform)

# Install pipx (one-time setup)
python -m pip install --user pipx
python -m pipx ensurepath

# Install and run OpenData Tool
pipx install opendata-tool
opendata-tool

Using pip (Traditional)

# Create virtual environment (recommended)
python -m venv venv
source venv/bin/activate  # Linux/macOS
venv\Scripts\activate     # Windows

# Install
pip install opendata-tool

# Run
opendata-tool

Option 3: Development Installation

git clone https://github.com/jochym/opendata.git
cd opendata
pip install -e ".[dev]"
python src/opendata/main.py

Architecture

The tool uses a modular Agentic Backend and a component-based NiceGUI Frontend.

• Frontend (src/opendata/ui/): Decoupled into functional components (components/) and session-managed via AppContext.
• Backend (src/opendata/agents/): Agentic reasoning loop with specialized modules for parsing and tool execution.
• Data Safety: Strictly read-only policy for research data. All persistence happens in ~/.opendata_tool/.

Contributing

See AGENTS.md for detailed coding standards and the agentic workflow. See docs/ for comprehensive manuals.

Testing

Quick Start

# Run all CI/CD safe tests (automated)
./tests/run_all_tests.sh

# Or run specific categories
pytest                          # CI/CD safe tests (79+ tests, ~4s)
pytest -m ai_interaction        # AI tests (local only, needs app running)
./tests/run_e2e_tests.sh        # Full E2E suite (needs Xvfb)

Test Coverage

• Unit Tests: 53+ tests (~2s)
• Integration Tests: 8+ tests (~2s)
• AI Tests: 10+ tests (~3s, local only)
• E2E Tests: 30+ tests (~90s)

Total: 101+ tests, all automated

Documentation

• Testing Guide - Complete testing guide
• Testing Quickstart - Quick start guide
• AI Testing Guide - AI testing specifics

Documentation

For Users

• Testing Guide - How to run tests and verify installation
• AI Setup Guide - Configure AI providers (Google GenAI, OpenAI)
• Tester Manual - Complete QA and manual testing workflows
• Supported Platforms - OS and Python version compatibility

For Developers

• Developer Manual - Core development guide
• API Reference - Internal REST API documentation
• Test Infrastructure - Testing architecture deep dive
• Test Results - Coverage analysis and metrics
• Field Protocol Design - Hierarchical protocol system
• Prompt Architecture - AI prompt system design

Project Status

• Accomplishments - Historical development log
• Roadmap - Future plans and upcoming features
• Changelog - Version history and release notes

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