EOSC Node | SURF - EOSC Association

The SURF EOSC Node federates the SURF Research Cloud, FAIR data repository, and EFSS services, bridging Dutch national infrastructures with European-level services.

SURF is the ICT cooperative for Dutch research and education institutions, providing compute, storage, data management, AAI, security, publishing, and innovative services such as quantum and AI fabrics. As an innovator, SURF will bring together key national stakeholders to contribute to the development of a Dutch EOSC Node. These activities will bring together resources and capacity with the aim of making them available to the EOSC Federation and will assess how resources from other EOSC Nodes can be made available to the research community in the Netherlands.

EOSC Node | SURF homepage

Key objective: To support research and education in the Netherlands through computing, data and networking services, consultancy, training and contracting. Through the EOSC Federation, SURF will explore offering these services cross-border to the wider EOSC community.

Science areas: Multidisciplinary – compute, storage, and FAIR data services across natural and engineering sciences, social sciences and humanities, and life and health sciences.

SCIENTIFIC IMPACT

FAIR data

The SURF EOSC Node offers a comprehensive collection of Dutch open research information, covering publications, research data sets, research software items, from Dutch institutional repositories and research information systems (CRIS), linked to research grants (NWO) and research performing organisations (UNL, VH, KNAW, NFU).

Scientific use cases

Imaging data workflows on Galaxy

This science case on imaging data workflows on Galaxy demonstrates how a federated, open-access computational platform can transform the way imaging data are processed, shared, and reused across diverse scientific domains.

The use case leverages the Galaxy platform to integrate data from a wide range of imaging-based research fields—such as life sciences (microscopy), astrophysics (telescope data), climate science (satellite data), and marine science (underwater imagery)—into a unified analysis environment. The goal is to demonstrate that Galaxy can be integrated into the EOSC Federation’s common infrastructure to serve many disciplines simultaneously, allowing researchers to share workflows, reuse methods, and access powerful computational tools without needing specialised technical expertise.

EOSC Federation scientific use case – Galaxy integration

Federated analysis of pathogen genomes

The federated analysis of pathogen genomes science case outlines a federated, cross‑border capability for timely analysis of pathogen genomes that brings computation to the data instead of copying sensitive datasets across institutions.

The objective is to shorten time‑to‑insight for outbreak detection, source attribution, and antimicrobial‑resistance (AMR) surveillance while preserving data sovereignty and meeting European legal and ethical requirements. Experience from COVID‑19 showed that sequencing at scale can transform public‑health decision‑making. Operationally, the effort starts with two neighbouring nodes of the EOSC Federation—the Slovakian national node providing workflows, datasets, computational infrastructure and domain expertise, and the Polish national node (via Poland’s National Science Centre (NCN) and a scientific repository service) supplying key technical support and their own datasets. Their geographical proximity make the two EOSC Nodes an ideal pair for a cross-border pilot. The approach demonstrates how to establish a federation of trusted sites, run harmonized workflows locally, and share only the minimum results needed for action.

EOSC Federation scientific use case – Pathogen genomes

Multi-centric validation of AI models for prostate-cancer screening

Advances in digital pathology are transforming cancer diagnosis by enabling high-resolution imaging of tissue samples and the application of artificial intelligence (AI) for clinical decision support.

Prostate cancer, one of the most prevalent malignancies among men worldwide, represents a critical case for early and accurate diagnosis, as survival rates are strongly tied to timely detection and treatment. The objective of the multi-centric validation of AI models for prostate-cancer screening (MCVAL) use case, coordinated by the BBMRI-ERIC EOSC Node, is to create a secure environment in which AI models for prostate cancer screening can be validated using data from different hospitals. Rather than building new diagnostic systems from scratch, the project focuses on testing an existing model trained on whole-slide images and assessing how well it performs when applied to data processed elsewhere.

EOSC Federation scientific use case – MCVAL

Prostate cancer imaging data for AI training. Courtesy of BBMRI-ERIC

Other use cases

Tools to the Data – EESSI software stack across EOSC nodes for uniform workflows and federated analyses.
Collecting Data – Federating EFSS services (ResearchDrive, OpenCloudMesh API)

STATUS & TIMELINE

Status

Candidate National EOSC Node

EOSC Node | SURF Project Charter

CEO of SURF Ron Augustus signing the EOSC Federation MoU at the EOSC Symposium, 04 November 2025. ^{©️PRYZM • Ievgeniia Pavlenko}

Deliverables

Project Charter
Node Architecture Design Document
Node Security Plan
Initial Node Platform
Demonstrated Use Cases
IT Governance, Data Protection, Operational Quality Plans (to be defined during build-up phase)
Mandated as Dutch EOSC National Node