The Bonney Lab

Research Documentation Principles

2 min read

Research Documentation Principles

Summary

This note outlines core principles for effective research documentation and knowledge management in scientific work. These principles aim to improve research quality, reproducibility, and collaboration.

Core Principles

1. Comprehensive Documentation

Thorough documentation of methods, data, and analyses is essential for reproducibility and knowledge transfer.

Application: Create detailed protocols, document code with comments, and maintain clear records of experimental parameters.

2. Version Control

Tracking changes to research materials over time preserves the history of development and enables collaboration.

Application: Use version control systems like Git for code, protocols, and documentation.

3. Structured Organization

Logical organization of research materials makes information retrieval efficient and reduces cognitive load.

Application: Implement consistent file naming conventions, directory structures, and metadata standards.

4. Interconnected Knowledge

Research insights often emerge from connections between different concepts, experiments, or fields.

Application: Create cross-references between related documents, use tags to categorize content, and maintain a network of linked notes.

5. Public Sharing

Open science principles encourage sharing research processes and outputs when appropriate.

Application: Publish preprints, share code repositories, and document methods publicly while respecting privacy and intellectual property considerations.

Implementation Guidelines

For effective research documentation:

  1. Start early: Begin documenting from the inception of a project rather than retrospectively
  2. Be consistent: Establish and follow conventions for formatting, organization, and metadata
  3. Focus on clarity: Write for your future self and collaborators who may not have your current context
  4. Integrate with workflow: Choose documentation tools that enhance rather than impede your research process
  5. Regular maintenance: Schedule time to review, update, and refine documentation

Applications in Computational Biology

In computational biology and bioinformatics, effective documentation includes:

  • Well-commented code with clear explanations of algorithms and parameters
  • Comprehensive environment specifications (e.g., using containers or environment files)
  • Data provenance tracking and preprocessing documentation
  • Visualization of analysis workflows
  • Thorough documentation of model architectures and training procedures
  • Reproducible Research
  • Open Science Practices
  • Electronic Lab Notebooks
  • Literate Programming
  • FAIR Data Principles (Findable, Accessible, Interoperable, Reusable)

This note outlines principles for effective research documentation that I apply in my work and continuously refine based on experience and community best practices.

Graph View

Backlinks