Chapter 3: Scientific Data & Workflows

Introduction: The Data Challenge in Science

Part I: Unique Challenges of Scientific Data

Challenge 1: Small Data

Challenge 2: Noisy and Heterogeneous Data

Challenge 3: Domain-Specific Vocabularies

Challenge 4: Temporal and Spatial Structure

Challenge 5: High Dimensionality

Part II: Data Sources in Science

Publications and Literature

Experimental Data

Simulation Data

Synthetic Data Generation (2024–2025 Advances)

Field Data and Observations

Part III: The FAIR Principles

Findable

Accessible

Interoperable

Reusable

Part IV: Data Preparation for AI

The AI-Ready Data Pipeline

Data-Centric AI: A Paradigm Shift

Quality Control Automation

Normalization Strategies

Data Splitting Best Practices

Data Augmentation for Science

Part V: Integrating AI into Research Workflows

The Hybrid Workflow

AI as Research Assistant

Autonomous and Self-Driving Labs (2024–2025)

Reproducibility in AI-Enhanced Research

Part VI: Automated Workflow Generation

From Description to Pipeline

Workflow Best Practices

Integration with Existing Tools

Summary

References

Additional Resources

Textbooks