Good Scientific Practice

Good Scientific Practice#

According to Wilson et al.1Wilson, G., et al. (2017). Good enough practices in scientific computing. PLoS Comp Bio. https://doi.org/10.1371/journal.pcbi.1005510. (condensed):

📂 Data Management

  • Preserve: Save and back up original raw data in multiple locations.

  • Structure: Create analysis-ready, tidy data with unique identifiers.

  • Traceability: Document all processing steps and archive in a DOI-issuing repository.

💻 Software

  • Clean Code: Use modular functions, meaningful names, and avoid duplication.

  • Dependencies: Use/test external libraries and make requirements explicit.

  • Documentation: Provide header comments, a test dataset, and a DOI for the code.

🤝 Collaboration & Org

  • Roadmap: Maintain a project overview and shared to-do list.

  • Sync: Define communication strategies and explicit licensing.

  • Citation: Ensure the project is citable for others.

🔄 Tracking Changes

  • Version Control: Use a VCS (like Git) for all human-created files.

  • Frequency: Share small, frequent changes rather than large, rare ones.

  • Logs: Maintain a CHANGELOG and mirror work to a remote server.

📝 Manuscripts

  • Format: Use plain text formats (like Markdown) to allow for version control.

  • Tools: Use platforms with rich formatting and automated reference management.