Data Lakes & FAIR Principles: Making Scientific Data Accessible and Actionable

The FAIR principles—Findable, Accessible, Interoperable, and Reusable—are a widely adopted framework to optimize scientific data management and break down data silos14.

Recent advances leverage Data Lakes to implement FAIR principles by incorporating rich metadata, unique identifiers, machine-readable formats, and robust access controls134.

Modern life sciences platforms assign persistent identifiers (such as DOIs or UUIDs) and use machine-actionable metadata for improved data discovery4.

Interoperability is achieved by describing data with standardized vocabularies and ontologies, enabling diverse datasets to be seamlessly combined and analyzed4.

Reusable data requires clear licensing, provenance documentation, and context-rich annotation so that datasets can be replicated and utilized across disciplines4.

Latest webinars and industry resources highlight strategies for implementing FAIR principles in large-scale Data Lakes to make research data more accessible and actionable5.

Sources:

1. https://aws.amazon.com/blogs/industries/implement-fair-scientific-data-principles-when-building-hcls-data-lakes/

3. https://www.databricks.com/blog/2021/09/07/implementing-more-effective-fair-scientific-data-management-with-a-lakehouse.html

4. https://www.tiledb.com/blog/fair-data-principles-explained

5. https://www.biospace.com/register-data-lakes-fair-principles-making-scientific-data-accessible-and-actionable

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