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