Data Validation

Problem Statement

Unvalidated or inconsistent data can mislead ML models and design decisions in materials research.

Our Solution

Our Validation Engine:

• Detects outdated or incorrect values
• Distinguishes between experimental and theoretical datasets
• Applies outlier detection and statistical checks
• Flags suspicious entries for review

Key Features

• 📊 Anomaly Detection – LOF, skew correction, and outlier filtering
• 🧪 Classification – Distinguish experimental vs theoretical data
• 🔄 Scheduled Validation – Keep data in sync with source updates
• 📝 Reviewer Interface – Human-in-the-loop corrections

Workflow Overview

1. Load structured dataset
2. Run rule-based and statistical checks
3. Detect anomalies or outdated info
4. Flag for correction or auto-fix