Research Data Management in EBV-MS
Managing research data efficiently is crucial for ensuring reproducibility, collaboration, and long-term impact. The EBV-MS project incorporates a large amount of pre-existing data, new experimental data, clinical trials, large-scale genomics and AI-driven analyses across varied data sources. And of course, the project requires collaboration across different institutions and countries. All of this can pose a challenge from a 鈥渄ata management鈥� point of view.
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By: Amy Curwin, Scientific Project Manager at the EGA
At the European Genome-Phenome Archive (EGA), we bring our expertise in Research Data Management (RDM) and handling of sensitive human data to the EBV-MS project by leading a dedicated work package focused solely on data management. We are ensuring that the project follows best practices in data governance, accessibility, and security, accompanying the partners through every stage of the data life cycle.听
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EGA鈥檚 role in EBV-MS
Drafting a detailed Data Management Plan (DMP) in collaboration with all project partners and ensuring its implementation throughout the project.听
- Implementing FAIR (Findable, Accessible, Interoperable, Reusable) data principles.
- Facilitating data sharing and long-term preservation.
- Empowering researchers with data management tools, resources and best practices.
RDM training, resources, and tools
To support the EBV-MS consortium, we also offer training sessions to help researchers navigate the complexities of data management. In these sessions we cover:
- Tools, platforms and resources available for secure data storage, harmonisation, analysis and sharing.
- Ethical and legal considerations for handling sensitive data.
- Data submission and archiving procedures for long-term preservation, sharing and reuse beyond the life-time of the project.听
Challenges of Data Management in EBV-MS
Diverse Data Sources: The project integrates genomic, clinical, imaging, and AI-driven computational datasets, requiring careful harmonization.
- Ethical compliance & Security: Sensitive patient and personal data must be handled in accordance with GDPR and ethical guidelines.
- Data Volume and Complexity: Storing and analyzing large-scale datasets, including AI-generated outputs, demands efficient infrastructure and scalable solutions.
- Interoperability & Standards: Ensuring data compatibility across different platforms and research groups is critical to applying the FAIR principals.听
At the EGA, we are proud to take on the challenge of managing the diverse and complex data landscape of EBV-MS. By providing expertise in data governance, security, and accessibility, we are ensuring the project data remains secure, FAIR-compliant, and ready for future reuse. We are excited to be part of this ground-breaking project and to contribute to its long-term success.听
