handlers/experiment.rs - Research Experiment Management

Requirements and Dataflow

  • Manages research experiment lifecycle including creation, participant enrollment, and results analysis
  • Provides experiment discovery and joining functionality for research participants
  • Handles experimental data collection and statistical analysis
  • Supports data export for research publication and analysis
  • Implements proper research ethics and participant consent management

High-level Purpose and Responsibilities

  • Experiment Management: Research study creation, configuration, and lifecycle management
  • Participant Enrollment: Volunteer recruitment and consent management
  • Data Collection: Experimental data gathering with proper research protocols
  • Results Analysis: Statistical analysis and research findings generation
  • Data Export: Research data extraction with privacy protection and ethics compliance
  • Ethics Compliance: Research participant protection and consent tracking

Key Abstractions and Interfaces

  • Experiment creation and configuration with research parameter definition
  • Participant joining system with consent management and eligibility checking
  • Results collection and statistical analysis with research methodology compliance
  • Data export functionality with ethics approval and participant consent verification

Data Transformations and Flow

  1. Experiment Setup: Research design → experiment configuration → participant criteria → ethics approval
  2. Enrollment Process: Participant interest → eligibility verification → consent collection → enrollment confirmation
  3. Data Collection: Participant activities → experimental data capture → protocol compliance → data validation
  4. Results Analysis: Collected data → statistical processing → research findings → publication preparation
  5. Export Processing: Ethics verification → data anonymization → format conversion → secure delivery

Dependencies and Interactions

  • User System: Research participant management and consent tracking
  • Learning System: Integration with learning activities for data collection
  • Statistics Engine: Research-grade statistical analysis and significance testing
  • Ethics Framework: Research participant protection and consent management
  • Data Privacy: Participant data protection and anonymization systems
  • Audit System: Research activity logging and ethics compliance tracking

Architectural Patterns

  • Research Ethics: Comprehensive participant protection and consent management
  • Data Collection: Systematic experimental data gathering with protocol compliance
  • Statistical Analysis: Research-grade analysis with proper methodology and significance testing
  • Privacy Protection: Participant data anonymization and secure handling
  • Audit Trail: Complete research activity logging for ethics compliance
  • Export Control: Secure data export with ethics approval and participant consent verification