Technical Deep Dive: Open Science Requirements in SNWG Solutions

Open science practices are fundamental to SNWG solutions, ensuring reproducibility, transparency, and accessibility across federal agencies. This guide outlines our requirements and best practices.

Open Science Principles

Open Science is guided by the FAIR principles—Findable, Accessible, Interoperable, and Reusable. These principles ensure that data and research outputs are effectively shared and utilized, enabling global collaboration. For more information, see the European Commission's Open Science resources.

Accessibility

All SNWG solutions must be accessible to authorized federal agencies and their contractors.

Requirements:

• Public repositories when possible
• Clear access documentation
• Version-controlled releases

Reproducibility

Solutions must be reproducible across different environments and systems.

Requirements:

• Environment specifications
• Dependency management
• Build instructions

Versioning

Clear version control and change tracking for all solution components.

Requirements:

• Semantic versioning
• Release documentation
• Change logs

Documentation

Comprehensive documentation covering all aspects of the solution.

Requirements:

• API documentation
• User guides
• Technical specifications

Code Sharing Best Practices

Repository Structure

project-root/
├── src/
│   ├── core/
│   └── utils/
├── tests/
├── docs/
├── examples/
├── LICENSE
├── README.md
├── CONTRIBUTING.md
└── requirements.txt

Key Requirements:

• Clear folder structure
• Comprehensive README
• License specification
• Contribution guidelines

Code Style & Quality

• Follow language-specific style guides (PEP 8 for Python, etc.)
• Use consistent naming conventions
• Include inline documentation
• Implement automated testing

Documentation Requirements

API Documentation

```python def process_data(data: np.ndarray, threshold: float = 0.5) -> np.ndarray: """ Process input data using specified threshold. Args: data (np.ndarray): Input data array threshold (float, optional): Processing threshold. Defaults to 0.5. Returns: np.ndarray: Processed data array Raises: ValueError: If data is empty or threshold is invalid """ # Implementation pass ```

User Guides

Getting Started

• Installation instructions
• Basic configuration
• Quick start examples

Usage Examples

• Common use cases
• Configuration options
• Best practices

Data Management Plans

1

Data Collection

• Source documentation
• Collection methods
• Quality controls

2

Processing

• Processing workflows
• Version control
• Quality assurance

3

Storage

• Storage solutions
• Backup procedures
• Access controls

4

Distribution

• Access methods
• Usage tracking
• User support

Tools and Resources

Enhance your open science practices with these tools and organizations:

• Center for Open Science Framework
• European Commission Resources
• ROSiE Guidelines
• Digital Curation Centre

Documentation Tools

• Sphinx for Python
• JSDoc for JavaScript
• Doxygen for C++

Code Quality

• Linters and formatters
• Testing frameworks
• CI/CD tools

Data Management

• Version control systems
• Data validation tools
• Storage solutions