DataCastle: A Pragmatic Approach for Research and Real-World Data Management

Area of Science:

• Biomedical Research Data Management
• Health Informatics
• Open-Source Software Development

Background:

• Effective research data management (RDM) is crucial for transparency and reproducibility in biomedical research.
• Fragmented infrastructures and heterogeneous data present significant challenges to RDM.
• Existing systems often struggle to bridge FAIR data acquisition with FAIR data utilization.

Purpose of the Study:

• To introduce DataCastle, a modular open-source platform designed to address RDM challenges in biomedical research.
• To facilitate FAIR data principles throughout the research lifecycle, from acquisition to analysis.
• To enhance data transparency, reproducibility, and collaboration through integrated data management solutions.

Main Methods:

• DataCastle integrates enrollment-time pseudonymization, metadata extraction, and background versioning.
• Structured data are captured using an Electronic Data Capture (EDC) system.
• Unstructured data are managed in a filesystem-based data lake, with metadata mapped to Health DCAT-AP for findability and EHDS alignment.

Main Results:

• The platform provides a unified environment for managing heterogeneous biomedical data.
• It connects managed data to analysis and visualization tools, enabling reproducible workflows.
• Metadata mapping supports findability and alignment with the European Health Data Space (EHDS).

Conclusions:

• DataCastle offers a modular, open-source solution to bridge FAIR data acquisition and use in biomedical research.
• The platform enhances data management by integrating key features like pseudonymization, metadata extraction, and versioning.
• By connecting data to analysis tools, DataCastle promotes reproducible and version-linked research workflows.