Introduction
============

**For Users: Understanding RePORTaLiN**

Overview
--------

RePORTaLiN is a tool that helps you convert Excel spreadsheets into a cleaner, more organized 
format called JSONL. It's designed to be easy to use, fast, and reliable - perfect for handling 
medical research data without requiring technical expertise.

Purpose
-------

The RePORTaLiN pipeline addresses the common challenge of converting complex Excel-based 
research data into a structured, machine-readable format. It's specifically designed for:

- Medical research data management
- Clinical trial data processing
- Data standardization and validation
- Research data archiving

Key Benefits
------------

**Speed and Efficiency**
   The pipeline can process 43 Excel files in approximately 15-20 seconds, making it 
   suitable for large-scale data processing tasks.

**Intelligent Processing**
   - Automatic table detection within Excel sheets
   - Smart handling of empty rows and columns
   - Automatic data type inference and conversion
   - Duplicate column name resolution

**Robustness**
   - Comprehensive error handling and recovery
   - Detailed logging for debugging and auditing
   - Progress tracking for long-running operations
   - Graceful handling of edge cases

**Flexibility**
   - Works with any dataset folder automatically
   - Adjustable paths and settings
   - Easy to customize for your needs
   - Options to run specific parts of the process

Use Cases
---------

RePORTaLiN is ideal for:

1. **Data Migration**: Converting legacy Excel data to modern formats
2. **Data Integration**: Standardizing data from multiple sources
3. **Quality Assurance**: Validating and cleaning research data
4. **Archival**: Creating structured backups of Excel-based data
5. **Analysis Pipeline**: Preparing data for downstream analysis tools

System Requirements
-------------------

- **Python**: 3.13 or higher
- **Operating System**: macOS, Linux, or Windows
- **Memory**: Minimal (handles large Excel files efficiently)
- **Storage**: Depends on dataset size (outputs are typically smaller than inputs)

Design Philosophy
-----------------

RePORTaLiN follows these core principles:

**Simplicity**
   One command to run the entire pipeline: ``python main.py``

**Transparency**
   Comprehensive logging shows exactly what's happening at each step

**Reliability**
   Extensive error handling ensures the pipeline fails gracefully

**Maintainability**
   Clean, well-documented code makes it easy to understand and modify

Next Steps
----------

- :doc:`installation`: Set up RePORTaLiN on your system
- :doc:`quickstart`: Run your first data extraction
- :doc:`configuration`: Customize the pipeline for your needs