The Polish EOSC Node aims to provide a FAIR digital research environment for researchers by seamlessly integrating national digital resources and services—ranging from computing to data storage—into EOSC.
Guided by the FAIR and CARE principles, it promotes inclusivity, ensures secure federated AAI, and implements the interoperability framework. At the same time, the Polish EOSC Node places strong emphasis on developing and strengthening EOSC-related competences.
Key objective: To actively support the development of the EOSC Federation by integrating national resources, enhancing their accessibility, interoperability, and reusability, and presenting use cases that demonstrate EOSC’s value and impact.
Science areas: Multidisciplinary, including trans- and inter-disciplinary sciences.
FAIR data
The Polish EOSC Node federates trusted Polish repositories and databases, aligns with FAIR and CARE principles, and supports metadata harvesting and semantic interoperability.
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.
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.
Biological sequestration of carbon in the ocean
The scientific use case addresses the ocean’s critical role in mitigating climate change through biological carbon sequestration, a process by which marine microorganisms capture atmospheric carbon dioxide and store it in the deep ocean for centuries.
Despite its global importance for climate modelling, this mechanism remains poorly represented in current models, which still rely on simplified representations of marine ecosystems. The use case seeks to fill this knowledge gap by integrating genomic, environmental, and modelling data within a unified, open, and interoperable framework provided by the EOSC Federation.
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.
Other use cases
- Federated data discovery for FAIR Research
- End-to-end federated workflows for data-intensive research
- Integration of environmental thematic nodes
- Collaborative platforms for environmental research
