Advanced HPC & Data Engineering
Overview
Scientific groups and Technological Units at the Department of Experimental Oncology at IEO rely on research computing and data (RCD) experts who work at the intersection of infrastructures, research, and data management. The Computational Research & Data Platforms team supports the technological infrastructure and digital ecosystem that enable biomedical and translational research activities at IEO.
Our team develops, manages, and maintains advanced research computing and data platforms, providing researchers with secure, scalable, and efficient environments for data analysis, scientific workflows, and collaborative projects. The group operates at the intersection of research, data management, bioinformatics, and digital infrastructure, supporting both computational and data-driven initiatives across the Institute.
Our Activities
Our activities include the management and support of research computing platforms, Cloud-based services, data infrastructures, and integrated digital systems for scientific research.
In particular, we:
- Support researchers in the use of computational and bioinformatics resources
- Manage research computing infrastructures and cloud environments
- Develop and maintain integrated software platforms and research applications
- Support data processing, storage, transfer, archiving, and reproducibility workflows
- Coordinate user access, permissions, and administrative operations for research platforms
- Collaborate with research groups and institutional partners on national and international initiatives (e.g. Alleanza contro il Cancro network - ACC -, Health Big Data - HBD -, in close collaboration with CNAF-INFN)
The group also contributes to the implementation of best practices for computational research, data governance, and FAIR data management principles.
Research Computing Infrastructure
We manage and support advanced Research Computing and Data (RCD) platforms, including high-performance computing (HPC) resources and data-intensive research environments.
These infrastructures are designed to support:
- Large-scale data analysis
- Genomics and bioinformatics workflows
- Artificial intelligence and imaging applications
- Scientific workflow orchestration
- Collaborative computational research
Our goal is to provide researchers with reliable, scalable, and reproducible computational environments that accelerate scientific discovery.
Data Platforms & Scientific Workflows
The group actively supports the management and integration of sequencing and clinical research data through collaborative platforms and automated workflows.
We also collaborate with institutional and national initiatives focused on clinical and biomedical data integration. Recent activities have focused on improving sequencing data handling, automated demultiplexing, conversion, and distribution workflows, with the objective of reducing operational burden and increasing research throughput.
Part of these developments has been presented in institutional scientific communications, including the poster:
Data Stewardship & FAIR Research
The group promotes modern data stewardship practices and supports researchers in the creation of structured and FAIR-compliant Data Management Plans (DMPs).
We contribute to:
- Improving data organization and traceability
- Supporting reproducibility and long-term data accessibility
- Enabling standardized research data management workflows
- Integrating research project lifecycle management with computational infrastructures
These activities help ensure sustainable and compliant research data practices across the institute.