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

Federating CERN’s REANA pipelines

The REANA science case focuses on enabling near-data computation—sending computational workflows to where large scientific datasets are stored, rather than transferring massive volumes of data to the researcher.

The use case demonstrates this concept through particle physics—a field that generates enormous data volumes—but it is applicable to many other domains, including astronomy and life sciences. The project aims to show how researchers can execute their analyses directly at the data source, using REANA—CERN’s Reproducible research data analysis platform—to manage containerized workflows across federated computing resources.

EOSC Federation scientific use case – Federating CERN’s REANA

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

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

CAPABILITIES & ACCESS

Interactions with other EOSC Nodes

How to access the Node

Rules of Participation

SURF services are available to SURF members, with cross-border provisioning models in place for specific use cases. Access policies for others are under development.

Access points

Via home page (eosc.nl)

Node capabilities

Federated services

AAI SURF Research Access Management
Workflow integration (SURF High Performance Data processing)

Data repositories

SURF FAIR data repository

Compute and storage

SURF Data archive
SURF object store

VRE

SURF Research Cloud
VRE
Jupyter
RStudio
Galaxy
container-based workflows

File sync-and-share

SURF ResearchDrive (OpenCloudMesh API)

Other

SURF Filesender for large file transfer
NL Research Portal as a resource catalogue

COORDINATION

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

FAIR data

Scientific use cases

Federating CERN’s REANA pipelines

The REANA science case focuses on enabling near-data computation—sending computational workflows to where large scientific datasets are stored, rather than transferring massive volumes of data to the researcher.

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.

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.

Other use cases

Status

Candidate National EOSC Node

Deliverables

Technical status

Completed

Planned

Interactions with other EOSC Nodes

How to access the Node

Rules of Participation

Access points

Node capabilities

Federated services

Data repositories

Compute and storage

VRE

File sync-and-share

Other

Coordinating organisation

Node organisation

Coordinator

Operation Manager

Technical Product Manager

Security Officer

Communication Officer

Node Architect

Contributing organisations

100+ universities, universities of applied sciences, UMCs, research institutions, Thematic Digital Competence Centers (NES, LSH, SSH), OS-NL, eScience Center, DANS and Health-RI.