Bio x AI Hackathon
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        • The Complexity Slider - Finding Hypotheses at the Limits of Human Knowledge
        • [Hard Mode] Metadata Generation on datasets with No Manuscript or Code Associated
        • Inverse Reproducibility - Given Manuscript and Data, Make the Code
        • Atlas of Research Methods Formatted for Agentic Reuse
        • Utilizing Knowledge Graphs for the Detection of Potential Null Results
        • Creating an Iterative Publication Stack by Linking Together Existing Tooling
        • Longevity Atlas: Building a Decentralized Knowledge Network with Agentic Research Hypothesis Engine
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        • SpineDAO Chronos Project Spec
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        • Plug-ins for every piece of research tooling known to humankind
        • Reproducibility Assistant - Code Cleaning, Dockerization, etc
        • Finding and Differentiating Cardinal vs Supporting Assertions
        • [Easier Mode] Metadata Generation on Datasets Given the Manuscript and Code Repository
        • Sentiment Analysis on Existing Citations, Dissenting vs Confirming
        • Agentic Metadata Template Creation for Standard Lab Equipment
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Inverse Reproducibility - Given Manuscript and Data, Make the Code

Problem Statement:

Reproducibility is a cornerstone of scientific integrity. However, many published research manuscripts lack sufficient detail or accessible code to reproduce the reported findings. This creates a significant barrier to validating and building upon existing research.

Challenge:

Develop an intelligent agent that can automatically attempt to reproduce the code and analysis associated with a scientific manuscript and its corresponding dataset.

Detailed Description:

  • Input:

    • A scientific manuscript (PDF or text).

    • The dataset(s) used in the research (e.g., CSV, JSON, database dumps).

  • Agent Functionality:

    • Code Extraction: Attempt to extract code snippets or scripts from the manuscript (if present) or generate code based on the described methodology.

    • Environment Recreation: Create a virtual environment (e.g., using Docker, Conda) that matches the software dependencies mentioned in the manuscript.

    • Data Processing: Process the provided dataset(s) according to the described analysis steps.

    • Result Generation: Execute the code and generate the reported results (e.g., tables, figures, statistical values).

    • Comparison and Evaluation: Compare the generated results with the results reported in the manuscript.

    • Confidence Scoring: Assign a confidence level to the reproducibility attempt, reflecting the likelihood of successful reproduction.

    • Failure Point Identification: Identify and document potential points of failure or ambiguity in the reproduction process.

    • Reporting: Generate a detailed report summarizing the reproducibility attempt, including:

      • Successful reproduction steps.

      • Code used for reproduction.

      • Confidence scores for each step.

      • Identified failure points and potential solutions.

      • Any software versioning that was able to be identified.

  • Output:

    • A structured report (e.g., JSON, Markdown) detailing the reproducibility attempt.

    • The generated results (e.g., tables, figures, data files).

    • A virtual environment file that can be used to reproduce the environment.

Suggestions and Enhancements:

  • Version Control Integration: If the manuscript mentions specific software versions or libraries, attempt to retrieve and use those versions.

  • Automated Dependency Resolution: Develop a system that can automatically identify and install the required software dependencies.

  • Interactive Debugging: Provide an interactive interface that allows users to step through the reproduction process and debug any errors.

  • Visualization of Reproduction Steps: Generate visualizations that illustrate the data flow and analysis steps.

  • Handling of Ambiguous Instructions: Implement strategies for handling ambiguous or incomplete instructions in the manuscript.

  • Machine Learning for Code Generation: Explore the use of machine learning models to generate code based on natural language descriptions of the methodology.

  • Integration with Code Repositories: If the manuscript links to a code repository, leverage the code from the repository to improve the reproducibility attempt.

  • Containerization: Output the reproducible environment as a container, to make it as easy as possible to reproduce the work.

  • Evaluation Metrics:

    • Percentage of successfully reproduced results.

    • Accuracy of generated results.

    • Confidence scores for reproducibility.

    • Completeness of the reproducibility report.

    • Time to reproduce.

    • Amount of human interaction required.

Potential Technologies:

  • Natural Language Processing (NLP) for code extraction and analysis.

  • Scripting languages (e.g., Python, R) for data processing and analysis.

  • Virtualization and containerization technologies (e.g., Docker, Conda).

  • Machine learning models for code generation and dependency resolution.

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Last updated 1 month ago