Coding

Overview

This Hackathon window invites participants develop free/open-source codes for analyzing public-domain data to get new insights on some relevant aspect of biodiversity, habitats, or ecosystems.

Practical Implementation:

Documentation:

Each product must include a well-structured README file.

The readme should provide a clear project description, setup instructions, and a usage guide.

Please ensure the instructions are detailed enough for others to replicate your environment and run the scripts successfully.

Code Quality:

Follow language-specific style guides (e.g., PEP8 for Python, StandardJS for JavaScript, etc.).

Use consistent naming conventions for variables, functions, and classes.

Ensure comments are added where necessary, especially for complex logic or algorithms, to enhance the code’s readability and maintainability.

Originality:

The code submitted must be your original work created during the hackathon.

You are encouraged to use open-source libraries and frameworks, but make sure to properly attribute any external resources or tools used.

Scalability:

In your documentation, include a section on known limitations and potential areas for further development.

This will provide future contributors with an understanding of the current scope and direction, facilitating smoother maintenance and future scalability of the project.

Suggested Languages for Biodiversity Data Analytics

Python: A top choice for data analytics due to its vast array of libraries like pandas, NumPy, SciPy, and scikit-learn. It's especially powerful for machine learning and statistical modeling.

R: Well-suited for statistical analysis, data visualization, and biological data. R provides specialized packages such as vegan for ecological analysis and ggplot2 for advanced visualization.

JavaScript: Useful for web-based data visualizations and dashboards, especially with libraries like D3.js and Leaflet for mapping biodiversity data.

Julia: Known for high-performance computing and numerical analysis, Julia is great for handling large biodiversity datasets efficiently.

SQL: Important for querying large biodiversity databases and integrating various datasets for analytics.

Suggested Platforms for Code Submission

GitHub: Widely used for hosting open-source projects with excellent documentation features.

GitLab: Offers advanced CI/CD features and is another popular platform for open-source code.

Bitbucket: Good for hosting Git repositories, also supports private repositories for free.

Google Earth Engine: Good for accessing and analyzing a range of global datasets

When submitting your project, ensure that your repository is public, properly documented, and follows the guidelines above. Happy coding, and we look forward to your innovative solutions for biodiversity data analytics!