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NFDI-MatWerk – National Research Data Infrastructure for MSE

We support researchers in Materials Science and Engineering (MSE) with FAIR data solutions that enable new discovery.

We envision research data management (RDM) that supports every stage of a project, strengthens collaboration through easier data exchange, and integrates materials data from different techniques, including experiments and simulations.

We invite researchers with all levels of RDM knowledge to engage with an ecosystem of adaptable workflows, services, tools, and guidance that support daily laboratory and simulation work.

Laboratory Equipment

Materials Development & Characterization

Management of experimental data

Data Visualization

Materials Simulation & Data Science

Management of simulation data and knowledge

Microscopy

Materials Processing & Testing

Management of experimental data

Collaborative Research

Solutions

Management of research data

FAIR Data Workflow

Education

Learning & empowerment formats for the MSE community

NFDI-MatWerk & FAIRmat Spring School 2026 – Research Data Management in MSE

The Spring School on Research Data Management (RDM) and FAIR Data takes place 13–16 April at the University of Erlangen, with free participation, and is co-organized by NFDI-MatWerk and FAIRmat. The program provides students with hands-on training in FAIR data practices and core RDM methods, including data structuring, Electronic Laboratory Notebooks, Data Management Plans, and specific tools such as Coscine, alongside dedicated networking formats to support exchange and collaboration.

The Spring School on Research Data Management (RDM) and FAIR Data will take place from 13 to 16 of April at the University of Erlangen and the participation is free of charge. The event is co-organized by the NFDI consortia NFDI-MatWerk and FAIRmat.

The Spring School is a well-established format designed to provide students with essential knowledge and practical skills in modern research data management and FAIR data practices. Participants will gain domain-specific guidance designed to support sustainable data practices and compliance with funding requirements.

In the context of rapidly advancing digital research infrastructures, standardized and structured data management has become increasingly important. The consistent standardization of research data enables improved research efficiency while enhancing collaboration among researchers, better integration with well-designed IT systems, as well as long-term sustainability and reusability of research data.

A key focus of the event is the application of the FAIR Data Principles, ensuring that research data is Findable, Accessible, Interoperable, Reusable. These principles form a cornerstone of the European Open Science Cloud (EOSC) implementation and are increasingly required by funding bodies.

During the Spring School, participants will engage in a series of workshops, presentations covering topics such as Artificial Intelligence in MSE, Introduction to Research Data Management (RDM), Data Structuring, Electronic Laboratory Notebooks (ELN), Data Management Plans (DMPs), Coscine: Research Data Management Platform.

Special emphasis will be placed on Data Management Plans, which are now mandatory for many funding programs, including Horizon Europe, and are increasingly required by organizations such as the German Research Foundation (DFG).

In addition to technical sessions, the Spring School will provide structured networking spaces to encourage exchange and collaboration among participants.

The Spring School represents a significant contribution to advancing research data management skills in materials science and related disciplines. By promoting FAIR data practices and providing hands-on training in modern digital tools, the event supports the future of sustainable, collaborative, and efficient scientific research.

Register online: https://www.eusmat.net/research/other-events/nfdi-spring-school-2026/

Registration Deadline: 10 March 2026 

Participation is free of charge. Limited places!

NFDI-MatWerk
Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under the National Research Data Infrastructure – NFDI 38/1 – project number 460247524.

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