Goals and Concept

Research benefits greatly when data and artifacts are openly accessible: results become more reproducible, studies can be compared, data can be reused, and new research questions can build on existing work. Models play a key role here — not only in computer science but in many other scientific disciplines. They serve as central representations of knowledge, processes and structures, help to explain, understand and analyze, and often form the basis for experiments and simulations. At the same time, models themselves can be an important tool for research data management by structuring data, enriching it semantically and improving reusability and quality. As the importance of data and models grows, so does the pressure to support scientific findings with accessible and well-documented data and artifacts. Publications and funding proposals increasingly require that empirical data, experiment context and accompanying artifacts such as models, descriptions, software or tools be made openly available. To support this, infrastructures are emerging that provide research data according to the FAIR principles (Findable, Accessible, Interoperable, Reusable) — also within the framework of the National Research Data Infrastructure (NFDI). The NFDIxCS consortium is working on systematic approaches to identify requirements for infrastructures for research data management not only for computer science in general but specifically for the field of modelling. Building on previous workshops in this series, the planned workshop invites the community to contribute their experiences, reflect together and advance the discussion on research data management in modelling. The workshop centers on three overarching questions:

• How is research data defined and utilized in modelling and who benefits from its use?
• How can such data be sustainably collected, shared and reused to promote quality, reproducibility and collaboration?
• How can models themselves serve as a tool to structure research data, semantically enrich it and thereby improve quality and reusability?

Call for Papers

We invite submissions for the workshop; both novel contributions and under-review or previously published works may be presented to share insights and foster discussion. In addition to scientific perspectives, concrete use cases and practice reports should be discussed, e.g., challenges in sharing or reusing modelling data, tools or artifacts. Possible topics for submissions (not exhaustive):

• Types and roles of empirical data and artifacts in modelling research
• Standards and structures for metadata and documentation
• Examples of successful or problematic reuse (use cases)
• Legal, ethical and data protection aspects
• Existing repositories and infrastructures and their use in modelling
• Community building, sustainability and integration into NFDI activities

Sessions and the final program for the workshop will be determined after reviewing submissions. Planned elements include thematic session grouping, possibly a keynote talk, interactive small-group work on selected issues and a moderated panel discussion on a focused topic.

Contributions will be published as a joint proceedings volume in the Digital Library of the German Informatics Society (GI e.V.).

Submission formats

Submissions may be in English or German and should follow the LNI formatting guidelines. The following rules apply to the different submission types:

• Novel contributions: 4–8 pages
• Resubmission of published contributions: 2–4 pages (summary)

Please use the EasyChair platform to submit your paper: https://easychair.org/conferences?conf=rdimod26

Registration and Participation

The workshop will take place as part of the conference "Modellierung 2026" of the GI cross-sectional committee Modellierung from 09–12 March 2026 in Bayreuth.

Information about conference registration can be found on the conference website: www.modellierung-2026.uni-bayreuth.de.

PROJECT NFDIxCS

The main goal of the NFDIxCS consortium is to identify, define and deploy services that allow storing complex domain-specific data objects from the various sub-domains of Computer Science and to realize the FAIR principles across the board. This includes producing reusable data objects specific to the different types of CS data, which not only contain the data and related metadata but also the corresponding software, context and execution information in a standardized way. These data objects can be of any size, structure and quality. More information is available on the project website.