Chapter 3: Scientific Data & Workflows

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Introduction: The Data Challenge in Science

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Part I: Unique Challenges of Scientific Data

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Challenge 1: Small Data

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Challenge 2: Noisy and Heterogeneous Data

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Challenge 3: Domain-Specific Vocabularies

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Challenge 4: Temporal and Spatial Structure

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Challenge 5: High Dimensionality

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Part II: Data Sources in Science

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Publications and Literature

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Experimental Data

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Simulation Data

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Synthetic Data Generation (2024–2025 Advances)

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Field Data and Observations

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Part III: The FAIR Principles

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Findable

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Accessible

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Interoperable

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Reusable

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Part IV: Data Preparation for AI

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The AI-Ready Data Pipeline

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Data-Centric AI: A Paradigm Shift

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Quality Control Automation

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Normalization Strategies

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Data Splitting Best Practices

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Data Augmentation for Science

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Part V: Integrating AI into Research Workflows

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The Hybrid Workflow

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AI as Research Assistant

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Autonomous and Self-Driving Labs (2024–2025)

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Reproducibility in AI-Enhanced Research

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Part VI: Automated Workflow Generation

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From Description to Pipeline

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Workflow Best Practices

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Integration with Existing Tools

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Summary

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References

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Additional Resources

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Textbooks

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Up next

Chapter 4: Text, Code & Knowledge Generation for Scientists