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Presentation . 2026
License: CC BY
Data sources: Datacite
ZENODO
Presentation . 2026
License: CC BY
Data sources: Datacite
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Building Bridges Between Scientific Domains – TeSSHub as a Federated Training Catalogue Infrastructure

Authors: Knodel, Oliver; Andrabi, Munazah; Bacall, Finn; Reed, Phil; Voigt, Martin; Rioja, Kenneth; Juckeland, Guido; +1 Authors

Building Bridges Between Scientific Domains – TeSSHub as a Federated Training Catalogue Infrastructure

Abstract

The mTeSS-X project (“multi-Tenant e-Learning System for Sharing and eXchange”) aims to address one of the central challenges in modern research infrastructures: how to provide coordinated, yet domain-specific training resources across diverse scientific communities. Within the framework of ELIXIR and PaNOSC, the project develops a federated training catalog infrastructure called TeSSHub that connects communities from Photon and Neutron (PaN) Science and the Life Sciences (LS), enabling them to share, discover, and reuse training materials and event information across institutional and disciplinary boundaries. Scientific domains such as Life Science and Photon and Neutron share common challenges in training data stewardship, reproducible research, and the application of computational methods. However, their training ecosystems have traditionally evolved independently, often leading to fragmentation and duplication of effort. mTeSS-X directly addresses this by developing a modular, multi-tenant platform architecture that supports community autonomy while enabling interoperability and content exchange between training catalog instances. The software framework builds upon the ELIXIR Training eSupport System (TeSS) and introduces extensions that facilitate federated content discovery, metadata harmonization, and cross-domain search through standardized APIs and metadata schemas aligned with FAIR principles. From a technical perspective, mTeSS-X combines robust software engineering with semantic technologies to ensure that training resources are FAIR (Findable, Accessible, Interoperable, and Reusable) across infrastructures. It introduces an exchange mechanism that allows participating communities to selectively publish, synchronize, and enrich content, while preserving local governance and editorial control. This federated approach supports the vision of a shared European training ecosystem where institutions can seamlessly collaborate while maintaining independence. Beyond the technical implementation, mTeSS-X serves as a collaborative bridge between scientific cultures. By bringing together experts from the Life Sciences, where data-driven research and standardization are well established, and from the Photon & Neutron user facilities, which are rapidly evolving toward digital and open science practices, the project fosters mutual learning and capacity building. The collaboration has already led to a convergence of metadata models, improved visibility of training activities, and an enhanced understanding of how digital research infrastructures can support interdisciplinary skill development. In this talk, we will present the conceptual and technical foundations of mTeSS-X, illustrate its role as an enabler of cross-community collaboration, and discuss how it contributes to building a sustainable, federated training ecosystem for European research infrastructures. The presentation will highlight lessons learned from integrating heterogeneous systems, aligning community needs, and supporting the broader goals of Open Science, FAIR training materials, and research software sustainability.

Keywords

EOSC, training, TeSS, mTeSS-X, OSCARS, PaNOSC, ELIXIR

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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