Reproducibility Assistant - Code Cleaning, Dockerization, etc

Likely a preliminary step on the way to Inverse Reproducibility - Given Manuscript and Data, Make the Code

Problem Statement:

Many researchers, especially those new to coding or reproducibility best practices, struggle to create and maintain code that is easily reproducible and preservable. Read Lack of Code Publishing for more information. This can lead to difficulties in sharing, validating, and building upon their work.

Challenge:

Develop a plug-in or tool that acts as a "Reproducibility Assistant," providing researchers with automated suggestions and tools for improving the reproducibility of their code repositories based on their research manuscript.

Detailed Description:

• Input:

  • A scientific manuscript (PDF or text).

  • A code repository (e.g., GitHub URL, local directory).

• Assistant Functionality:

  • Automated Analysis: Analyze the manuscript and code repository to identify potential reproducibility issues.

  • Suggestion Generation: Provide actionable suggestions for improving reproducibility, such as:

    • Adding clear documentation (README files, comments).

    • Specifying software dependencies (requirements.txt, environment.yml).

    • Structuring the code into modular functions.

    • Adding unit tests.

    • Using version control (Git).

    • Containerizing the code.

    • Adding data dictionaries.

    • Adding license information.

  • Automated Formatting: Automatically format the code and repository structure to adhere to reproducibility best practices.

  • Dependency Management: Automatically generate dependency files (e.g., requirements.txt, environment.yml) based on the code and manuscript.

  • Documentation Generation: Automatically generate basic documentation (e.g., README files) based on the manuscript and code.

  • Version Control Integration: Provide tools for initializing and managing Git repositories.

  • User-Friendly Interface: Design an intuitive interface that is easy to use for researchers with varying levels of coding experience.

• Output:

  • A modified code repository with improved reproducibility.

  • A report summarizing the changes made and the suggestions provided.

  • A containerized version of the code, if possible.

Key Features:

• Manuscript Awareness: The assistant should be able to understand the context of the research from the manuscript.

• Code Analysis: The assistant should be able to analyze the code to identify potential reproducibility issues.

• Automated Formatting: The assistant should be able to automatically format the code to adhere to reproducibility best practices.

• User-Friendly Interface: The assistant should be easy to use for researchers with varying levels of coding experience.

Potential Technologies:

• Natural Language Processing (NLP) for manuscript analysis.

• Code analysis tools (e.g., linters, static analyzers).

• Version control systems (Git).

• Dependency management tools (e.g., pip, conda).

• Containerization technologies (e.g., Docker).

• Plug-in development frameworks (e.g., for VS Code, Jupyter Notebooks).

Desired Outcomes:

• A functional Reproducibility Assistant plug-in or tool.

• A demonstration of the feasibility of automated reproducibility assistance.

• Tools that can be used by researchers to improve the reproducibility of their code.