Making Common Fund data more findable: catalyzing a data ecosystem

Area of Science:

• Biomedical Informatics
• Data Science
• Health Research

Background:

• Researchers face challenges accessing diverse biomedical datasets.
• Existing data silos hinder comprehensive analysis across NIH Common Fund programs.

Purpose of the Study:

• To describe the Common Fund Data Ecosystem's (CFDE) data federation system.
• To introduce the Crosscut Metadata Model (C2M2) for unifying biomedical data descriptions.
• To demonstrate a centralized approach for discovering federated datasets.

Main Methods:

• Developed a flexible data federation system enabling data discovery without data movement.
• Created a centralized catalog integrating dataset descriptions from Data Coordination Centers (DCCs).
• Implemented the Crosscut Metadata Model (C2M2) to standardize diverse metadata.

Main Results:

• Successfully ingested and indexed data descriptions from 11 DCCs.
• The CFDE system provides a unified portal for searching disparate biomedical datasets.
• The C2M2 model accommodates a wide variety of data types and metadata terms.

Conclusions:

• The CFDE data federation system enhances biomedical data discoverability.
• The C2M2 metadata model offers a flexible solution for integrating diverse data resources.
• This approach facilitates research by simplifying access to NIH Common Fund data.