About KRT Validator

Purpose and Rationale

The KRT Validator was developed to address the critical need for standardized and accurate Key Resources Tables in scientific publications. The ASAP (Aligning Science Across Parkinson's) initiative requires precise documentation of research resources to ensure reproducibility and transparency in Parkinson's disease research.

Manual creation and validation of Key Resources Tables is time-consuming and error-prone. This tool automates the validation process, identifies common errors, and provides intelligent corrections to ensure compliance with ASAP standards. The system also supports format conversion between different KRT standards used by various journals.

Validation Methodology

Multi-Stage Validation Process: The system employs a comprehensive validation pipeline that checks file structure, column requirements, data completeness, and content accuracy. Each KRT undergoes systematic validation against ASAP standards including required columns, resource type compliance, and identifier format verification.

Error Detection and Classification: The validator identifies three categories of issues: errors (critical problems requiring correction), warnings (recommendations for improvement), and informational notices. Each issue is tagged with specific cell locations for precise correction guidance.

AI-Enhanced Correction: The system utilizes Google's Gemini AI models to provide intelligent corrections for identified issues. The AI system analyzes context, suggests appropriate resource types, generates proper identifiers, and ensures compliance with scientific publishing standards.

Format Conversion Methodology

Bidirectional Conversion: The system supports conversion between Cell Press hierarchical format and ASAP tabular format. The conversion process preserves data integrity while adapting to different structural requirements.

Intelligent Mapping: Resource type mapping uses predefined dictionaries and fuzzy matching algorithms to handle variations in terminology. The system recognizes equivalent terms across formats and applies appropriate transformations.

Data Preservation: All metadata including source information, identifiers, and additional details are preserved during conversion. The system ensures no information loss while adapting to target format requirements.

ASAP Resource Types

The following resource types are recognized and validated according to ASAP standards:

Each resource type has specific validation rules for identifiers, sources, and additional information requirements. The system ensures compliance with these standards while providing guidance for proper resource documentation.

Technical Implementation

Architecture: Built on Django framework with pandas for data processing, the system provides a robust web interface for file upload, validation, and correction. The architecture supports both interactive web use and programmatic API access.

Data Processing: Advanced CSV and Excel parsing with encoding detection ensures compatibility with files from various sources. The system handles edge cases including malformed data, encoding issues, and structural variations.

Quality Assurance: Each validation and correction operation includes comprehensive logging, error tracking, and result verification. The system maintains detailed records of all operations for transparency and debugging.