CORAL: A framework for rigorous self-validated data modeling and integrative, reproducible data analysis.

Area of Science:

• Data Science
• Information Management
• Computational Biology

Background:

• Organizations struggle with managing and analyzing heterogeneous datasets from multiple sources.
• Tracking interrelationships and provenance of diverse data is a significant challenge.
• The FAIR principles (Findable, Accessible, Interoperable, Reusable) address data stewardship and reuse.

Purpose of the Study:

• To present the Contextual Ontology-based Repository Analysis Library (CORAL) platform.
• To demonstrate CORAL's ability to facilitate adherence to all four FAIR principles.
• To address the challenge of making heterogeneous datasets interoperable and reusable within large organizations.

Main Methods:

• CORAL employs a data model requiring extensive documentation of data context.
• Tools maintain data context throughout the analysis pipeline.
• A web interface for data generators and a Jupyter notebook interface for analysts are provided, backed by a common API.

Main Results:

• CORAL enables on-the-fly creation of FAIR data types, reducing bespoke data modeling costs.
• The platform supports integrative cross-dataset analyses.
• Deeper insights are generated compared to traditional analysis tools.

Conclusions:

• CORAL significantly enhances adherence to FAIR data principles, particularly interoperability and reusability.
• The platform streamlines data management and analysis for complex organizational data.
• CORAL offers a powerful solution for generating novel insights from integrated datasets.