Atlas of Research Methods Formatted for Agentic Reuse

Problem Statement:

Scientific progress is fundamentally built upon robust methodologies and tools. While scientific literature primarily emphasizes concepts and findings, the detailed descriptions of underlying methodologies and associated tooling are often lacking, hindering reproducibility and efficient knowledge transfer. To enable agentic systems to autonomously generate methodological plugins, a structured and comprehensive mapping of research methods and tools is essential.

Challenge:

Develop a system that can automatically extract, classify, and map methodologies and associated tooling (physical or virtual) used in scientific research from existing literature, organized by field and study type. This mapping should be structured to enable an agentic resource to autonomously create methodological plugins for systems like Eliza.

Detailed Description:

1. Study Type Mapping (Pyramid of Study Types):

• Objective: To provide a hierarchical classification of research studies, enabling a more nuanced understanding of methodological contexts.

• Implementation:

  • Utilize a pyramid-like structure to categorize studies, starting with broad categories and refining to specific types.

  • Examples of categories include:

    • Meta-Analysis & Systematic Reviews: Synthesizing existing research.

    • Experimental Studies: Manipulating variables to establish cause-and-effect.

      • Randomized Controlled Trials (RCTs)

      • Quasi-experimental studies

    • Observational Studies (Non-Experimental): Observing and analyzing existing phenomena without intervention.

      • Cohort studies

      • Case-control studies

      • Cross-sectional studies

    • Descriptive Studies: Describing phenomena.

      • Case reports

      • Case series

    • Theoretical Studies: developing and analyzing theoretical models.

      • Axiomatic Systems

      • Theoretical Models

    • Computational/Simulation Studies: Using computer models to simulate and analyze complex systems.

      • Agent-based simulations

      • Monte Carlo simulations

      • Computational modeling

    • Analytical Studies: Employing logic, mathematics, or critical analysis.

      • Mathematical modeling

      • Logical analysis

      • Art and humanities deliberations

      • Axiomatic Systems

    • Art Studies: Analyzing and interpreting artistic works.

    • Humanities Deliberations: Philosophical or historical analysis.

    • Empirical Sciences: Sciences based on observation or experiment.

2. Methodology Extraction and Classification:

• Objective: To accurately identify and categorize specific methodologies used within each study type.

• Implementation:

  • Develop NLP techniques to extract detailed methodology descriptions from scientific manuscripts.

  • Classify extracted methodologies based on their specific techniques and applications, aligned with the study type mapping.

  • Within each study type, further categorize methodologies. For example:

    • Experimental: Lab protocols, field experiments, etc.

    • Observational: Survey design, data collection protocols, etc.

    • Computational: Algorithm specifications, simulation parameters, etc.

    • Analytical: Statistical analysis methods, logical reasoning frameworks, etc.

  • Identify and extract key steps, parameters, and variables within each methodology.

3. Tooling Extraction and Classification:

• Objective: To identify and categorize the tools (physical or virtual) used in each methodology.

• Implementation:

  • Identify and extract information about tools used in each methodology.

  • Classify tools into relevant categories:

    • Software (e.g., statistical software, simulation platforms).

    • Hardware (e.g., lab equipment, sensors).

    • Instruments (e.g., microscopes, spectrometers).

    • Databases (e.g., genomic databases, literature repositories).

    • Computational resources (eg. cloud computing)

  • Extract relevant tool attributes:

    • Manufacturer or developer.

    • Version or model number.

    • Key features and capabilities.

    • Access and availability information.

4. Methodology-Tooling Mapping and Knowledge Graph:

• Objective: To create a structured representation of the relationships between methodologies and tools.

• Implementation:

  • Develop a knowledge graph or database to map methodologies to the tools used in each step.

  • Represent relationships:

    • Tools required for specific methodology steps.

    • Alternative tools for the same task.

    • Dependencies between tools.

  • Create a structure that allows for the creation of agentic plugins.

5. Visualization and Reporting:

• Objective: To provide intuitive visualizations and reports for exploring the methodological landscape.

• Implementation:

  • Develop interactive visualizations of the methodology-tooling knowledge graph.

  • Generate reports summarizing extracted information, including methodology classifications, tool descriptions, and relationship mappings.

  • Create visualizations that show the pyramid of study types, and the method and tool mappings within each type.

6. Input, Output, and Potential Technologies:

• Input: Corpus of scientific manuscripts (PDF or text).

• Output:

  • Knowledge graph or database of methodologies and tools.

  • Interactive visualizations.

  • Detailed reports.

  • Agentic plugin creation structures.

• Potential Technologies:

  • Advanced NLP (e.g., transformer models, named entity recognition).

  • Knowledge graph databases (e.g., Neo4j, RDF databases).

  • Data visualization libraries (e.g., D3.js, Plotly).

  • Ontology mapping tools (e.g., Protégé).

7. Evaluation Metrics:

• Accuracy: Precision and recall of methodology and tooling extraction.

• Completeness: Coverage of methodologies and tools within the knowledge graph.

• Coherence: Consistency and logical structure of the mapping.

• Effectiveness: Usability and clarity of visualizations and reports.

• Reproducibility: The degree to which the agentic plugin creation structure allows for reproducibility.

Desired Outcomes:

• A comprehensive and structured knowledge graph of scientific methodologies and tools, organized by study type.

• An agentic plugin creation structure that enables autonomous methodological implementation.

• Improved reproducibility and accessibility of scientific methodologies.

• Enhanced understanding of the methodological landscape across scientific fields.