Programme

Abstract:
Celebrating ten years of the FAIR principles [1], we acknowledge a growing ecosystem of research (data) infrastructures, RDM tools, and IT services that support stewardship and promote fair data handling [2].

However, as data stewards at Universities of Applied Sciences (UAS), we face constrained infrastructure and RDM support due to funding gaps and delayed FAIR adoption [3, 4]. This limits our ability to implement FAIR practices and we are highly interested in exploring and (re)using solutions to complement our institutional portfolios.

Finding relevant tools and infrastructures is challenging. Germany and the National Research Data Infrastructure (NFDI) lack a registry for RDM resources comparable to re3data [5]. This hinders data stewards and researchers from institutions such as our UAS to access and reuse existing offers.

Our working group RDM-Tools4ALL within the FDM@HAW network of UAS data stewards has taken a first and pragmatic step towards mapping RDM resources. In this (Ignite) Talk, we present the RDM-Tools4All list in which we have documented approximately 80 RDM tools applying and adapting the metadata scheme of Lemaire et al. [6]. Aiming at a concrete, yet pragmatic, starting point to reduce discovery time for RDM tools and resources for our data stewarding practice, we concentrated on tools with simple and transparent access procedures, minimal local IT requirements or open licenses.

Keywords: RDM tools, University of Applied Sciences, data steward, research data infrastructure, FDM@HAW

[1] Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., et al. 2016. The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data 3, 1 (March 2016), Article 16018. doi.org/10.1038/sdata.2016.18.
[2] Wissenschaftsrat. 2025. Strukturevaluation der Nationalen Forschungsdateninfrastruktur (NFDI). https://doi.org/10.57674/wcdc-6d36. 
[3] Schimmer, T. M., Sarah, B., Claas, O., et al. 2025. The Role of Universities of Applied Sciences in the Development of a national Research Data Infrastructure. In Proceedings of the 2nd Conference on Research Data Infrastructure (CoRDI), Aachen, 26. – 28. August 2025. doi.org/10.5281/ZENODO.16735972.
[4] Boelter, S., Claas, O., Dähne, J., et al. 2025. The Role of Universities of Applied Sciences in the Development of a National Research Data Infrastructure. Situation and Potentials of Universities of Applied Sciences from the Perspective of the Network FDM@HAW. Zenodo, Presentation and Transkript. doi.org/10.5281/ZENODO.17055442.
[5] r3data.org. 2025. Registry of Research Data Repositories. www.re3data.org
[6] Lemaire, M., Helling, P., Jacob, B., et al. 2025. Ein konsolidiertes Metadatenschema für FDM-Services. Zenodo, Model. doi.org/10.5281/ZENODO.15025881.

Abstract:
PANGAEA - Data Publisher for Earth & Environmental Science is an established open-access data publisher for earth, environmental, and biodiversity sciences, with more than 30 years of experience in archiving, publishing, and disseminating georeferenced research data [1]. It is operated as a joint facility of the Alfred Wegener Institute and MARUM, providing a domain-specific infrastructure for curated, citable, and interoperable datasets. PANGAEA is a pioneer in FAIR and open data infrastructures, facilitating data-intensive research and constituting a core component of national and international science and technology frameworks. To address growing submission volumes while leveraging domain expertise within its community, PANGAEA follows a front office - back office model. PANGAEA offers its data archiving and publishing backbone (back-office) for usage by other institutes and workgroups (front-office) [1]. The back office provides the technical infrastructure and publishing services, including submission handling, review processes, and the overall publication workflow. It also develops and maintains comprehensive training materials and guidance for data stewards, data managers, data contributors, and users. Front offices at cooperating institutions act as local contact points for researchers and embed data stewardship within disciplinary contexts. Capacity building is a core element of this model: data stewards are trained through the PANGAEA network in standards, tools, and curation practices, and in turn disseminate this knowledge within their institutions through training and consultation. They are also responsible for the curation and publication of their institutes’ data submissions. The shared objective is the publication of high-quality datasets in accordance with the FAIR Principles. All submissions undergo a multi-stage review and curation process, including metadata standardization, quality control, and semantic enrichment. Data and metadata are published in a fully curated form, aligned with community standards and compliant with certification frameworks such as the  CoreTrustSeal, ensuring long-term accessibility and reusability.

Keywords: Open Access Data Publication, FAIR Principles, Long-term Accessibility, Data Stewardship

[1] Janine Felden, Lars Möller, Uwe Schindler, Robert Huber, Stefanie Schumacher, Roland Koppe, Michael Diepenbroek, and Frank Oliver Glöckner. 2023.. PANGAEA - Data Publisher for Earth & Environmental Science. Sci Data 10, 347, doi.org/10.1038/s41597-023-02269-x

Abstract:
Background: FAIR principles are widely adopted at the policy level, yet implementation in everyday research remains inconsistent. This gap is often framed as a lack of awareness or technical infrastructure. This talk argues instead that FAIR implementation fundamentally also depends on specialised competences and a governance architecture backing FAIR implementation - both still insufficiently institutionalised across many research institutions, including ETH Zurich. It shows how this challenge is being addressed at ETH.

Objectives: The talk illustrates how the establishment of a ‘FAIR Board’, a ‘FAIR Coalition’ [1] of research units, and ‘FAIR Competence Funding’ at ETH Zurich contributes to a maturing governance architecture for FAIR implementation. It highlights Research Data Management (RDM). Specialists as a critical layer of human resources for FAIR implementation, reflecting a shift from a narrow focus on “data stewards” to a broader understanding of RDM Specialists as an
umbrella term encompassing data stewards, research software engineers, and other domainspecific experts required to operationalise FAIR across data, code, and metadata.

Approach: Building on a recent position paper on RDM Specialists, [2] the contribution examines how institutional governance can support this vision. The ETH FAIR Coalition serves as an example: through its charter, funding instruments, and coordination via the FAIR Board, FAIR is framed as an institutional objective requiring dedicated expertise. The Board brings together heads of central service units and research units, linking strategy with practice.

Insights: Such governance frameworks can create structural incentives for embedding RDM expertise within research units and support hybrid models combining central services with locally embedded roles. The talk also shows how limited funding for RDM Specialists was leveraged to support researchers with RDM-tasks.

Relevance: The contribution proposes a competence-driven model of FAIR implementation, offering transferable insights into aligning governance, funding, and human resources to enable broader and more consistent adoption of FAIR practices.

Keywords: FAIR data, Governance, RDM Specialists, Institutionalisation

[1] ETH Zurich Open Science. 2026. ETH Zurich FAIR Coalition, ethz.ch/en/research/open-science/FAIR-Coalition.html (Accessed 29.04.2026)
[2] Caterina Barillari, Julian Dederke, John Griffin, Henry Lütcke, Iganz Strebel, and Matthias Töwe. 2026. Position Paper on the Professionalisation of Research Data Management (RDM) Specialists at ETH Zurich. ETH Zurich, Research Collection, Zurich. doi.org/10.3929/ethz-c-000795827
[3] OpenAI. 2026. ChatGPT (April 29 version) [Large language model]. chat.openai.com
 

Abstract: 
The usage of consistent documentation methods is an integral part of a proper research data management (RDM) inside a research institution. It allows for the implementation of quality standards and brings with it the often times biggest immediate benefit for researchers, which is to be able to understand the research data from colleagues inside the institution. However, it is not an easy task to define documentation strategies every participant can agree on. It
becomes an even more difficult task when the research institution has a broad spectrum of research areas as well as a very heterogenous group of users. In the presented case, a bottom-up attempt was made to find a set of experiment and resource categories as well as statuses for the electronic lab notebook (ELN) eLabFTW, that meets these requirements. The research facility in question is the Institut für Textiltechnik of RWTH Aachen University (ITA) with user backgrounds such as full-time researchers, student assistants, laboratory staff and machinery technicians from subdomains which range from single polymer filament production over weaving and knitting up to applications like blood vessel prostheses and aerospace structures. Challenges were choosing criteria to categorize experiments and resources by, finding the right granularity to offer both flexibility and accessibility and agreeing on unambiguous terms in both the English and German language to prevent misunderstandings.
The development process of the chosen set-up is presented in this Ignite Talk hoping to give an example for other institutes with very heterogeneous areas of research and user groups for a possible way to approach the challenge of providing an ELN set-up in their research work.

Keywords: electronic lab notebook, granularity, categorization, eLabFTW

Abstract:
Shared standards in research data management are the magic key to unlock the doors of data silos. RDM standards enable research data to be findable, accessible, interoperable and reusable in accordance with the FAIR principles. Standards create the conditions for this through uniform formats, metadata, processes and interfaces, which improve data quality and enable interoperability between research data and the services of the research infrastructure.
Many useful RDM standards have already been created, e.g. by the RDA, CODATA, DINI/nestor and GO FAIR. However, the application of these standards in practice is still lacking.

The aim of the German National Research Data Infrastructure (NFDI) is to establish a comprehensive and sustainable infrastructure for research data management that supports researchers of all disciplines in the efficient management and use of research data. To achieve this, NFDI has been given the task to “set standards in research data management” [1]. The statutes of the NFDI association provide for an institutional process to pass NFDI standards.

This process for NFDI standards ensures a wide community involvement by the 26 NFDI consortia and NFDIs 327 member organizations. It also includes obligations for the consortia and member organizations to apply the NFDI standards.
Data stewardship means taking care of data. Consequently, it is a core competence of data stewardship to apply standards. NFDI will support this by providing a canon of community-emerged standards, which re-uses existing standards as much as possible, and by fostering cross-disciplinary and cross-institutional commitment to such a set of shared standards. In addition, the NFDI standards will include measures to promote and evaluate their adoption.

We will present the process for NFDI standards, from development over adoption to application, and illustrated it by selected NFDI standard candidates, e.g. an NFDI standard for a generic metadata schema [2] and the RDM learning objectives matrix [3].

Keywords: standards, FAIR principles, interoperability, data stewardship

[1] Bund-Länder-Vereinbarung zu Aufbau und Förderung einer Nationalen Forschungsdateninfrastruktur (NFDI), 26.11.2018, www.gwk-bonn.de/fileadmin/Redaktion/Dokumente/Papers/NFDI.pdf
[2] Castro, L. J., Czerniak, A., Fliegl, H., Henzen, C., Koepler, O., Moore, J., Söding, E., Stervbo, U., Trippel, T., & Wiljes, C. (2026). Highlights of the 2nd Interdisciplinary NFDI Metadata Workshop. Helmholtz Metadata Collaboration | Conference 2026, Heidelberg. Zenodo. doi.org/10.5281/zenodo.19732521
[3] Petersen, B., Altemeier, F., Boße, S., Dalby, M., Düvel, N., Engelhardt, C., Fichtner, M., Hastik, C., Haugwitz, J.-M., Jacob, J., Koch, K., Kuntz, A., Manske, A., Mühlichen, A., Murcia Serra, J., Ortmeyer, J., Richter, M., Schranzhofer, H., Slowig, B., … Zollitsch, L. (2025). Lernzielmatrix zum Themenbereich Forschungsdatenmanagement (FDM)(Version 3). Zenodo. doi.org/10.5281/zenodo.15025246

Abstract:
Despite significant investment in research data management (RDM), much of today’s data stewardship remains fragmented, reactive, and difficult to scale. As institutions move beyond establishing services, the key challenge is no longer whether to provide support, but how to embed it sustainably within research practice. 

At the Technical University of Munich (TUM), the Research Data Hub at the Munich Data Science Institute (MDSI) is addressing this challenge by rethinking data stewardship not as a collection of individual services. The objective of this work is to move from isolated, project-based support towards a coordinated, institution-wide model that combines people, knowledge, and community.

Our approach consists of three aspects. First, a shared Data Steward model pools domain expertise across disciplines, enabling flexible yet targeted support for research projects ranging from individual labs to large initiatives such as SFBs and TRRs. Second, a Research Data Knowledge Hub captures and structures tacit expertise into reusable guidance, extending support beyond one-to-one interactions. Third, a growing Research Data Network fosters community engagement, connecting researchers, infrastructure providers, and national initiatives such as the NFDI.

We draw on practical implementation experience, internal evaluations of service uptake, and feedback from researchers and project partners. Early results indicate improved coordination of support, increased visibility of RDM services, and more consistent integration of FAIR data practices across projects. In addition, data stewards are increasingly acting as connectors between disciplinary needs and institutional or national infrastructures.

We conclude that scaling data stewardship requires not only additional resources, but also new organisational models that balance central coordination with domain specificity. This work is highly relevant to the DSgG community, as it addresses a shared transition: from establishing data stewardship services to growing them into sustainable, strategic components of the research data ecosystem.

Abstract:
In collaborative research projects, the implementation of data stewardship often requires coordination across institutional boundaries. While distributed data steward models enable domain-specific support and local integration, they also introduce challenges regarding communication, consistency, and shared service provision.

This talk presents the implementation and coordination of a distributed data stewardship model within NFDI4Health, a DFG-funded project, in which data stewards are employed at different institutions but jointly contribute to project-wide research data management (RDM) services. Each data steward supports local infrastructures and services, while crossinstitutional collaboration is realized through shared activities such as a joint helpdesk, coordinated training formats, and collective outreach at conferences and community events.

We describe the underlying organizational structure, including role definitions, communication routines, and coordination mechanisms that support collaboration across institutional and disciplinary boundaries. Particular attention is given to balancing local autonomy with the need for standardized services and a coherent project-wide approach.

Building on practical experience, the talk reflects on key challenges encountered during implementation, including aligning service quality, managing distributed workloads, and establishing efficient communication structures. For each of these challenges, we present concrete solutions and strategies developed within the project.

By focusing on implementation processes, workflows, and lessons learned, this contribution provides transferable insights for similar collaborative projects and infrastructures aiming to establish or improve distributed data stewardship models. The talk explicitly addresses the role of coordination and networking as key factors for sustainable and scalable data stewardship.

Keywords: Distributed Data Stewardship, Cross-Institutional Collaboration, Coordinated RDM services

Abstract:
Generating FAIR research data and enabling its reuse are the key goals of research data management. However, establishing machine-readable knowledge representation - the “I” in FAIR - as the foundation for interoperable data and metadata remains a major challenge for many research communities. In the AIMS project, we have developed an approach to create subject-specific RDF-compliant metadata profiles that enable precise and flexible documentation of research processes and data while relying on established terminologies for interoperability. Data stewards can build profiles for their specific use cases with a hierarchical and modular approach to ensure interoperability across different projects. Tools are available to generate web forms and search interfaces from these profiles, supporting the entry, exploration, and visualisation of RDF metadata and knowledge graphs. Researchers can use these tools to create structured metadata and contribute directly to a knowledge graph. 

To facilitate the modelling process and make it accessible to users with only limited knowledge of ontologies, we have developed a web service that provides a graphical user interface for creating metadata profiles [1]. The AIMS Metadata Profile Service is open source and allows integration into other tools, such as the RDM platform Coscine [2]. Its frontend allows users to search existing terminologies, add terms to a profile, and define restrictions such as expected data types or the cardinality of attributes. 

In the second funding phase of the project, AIMS 2 focuses on improving the user interface and integrating the profiles into electronic laboratory notebooks (ELNs) such as eLabFTW, helping researchers record semantic metadata without changing their existing workflows. In this talk, we present the AIMS modelling approach, the available tools, and current developments. We also show how data stewards can use them to create metadata profiles for their respective communities. 

Keywords: Metadata, RDF, Interoperability, Knowledge Graph

[1] NFDI4ING. Metadata Profile Service. Retrieved April 17, 2026 from profiles.nfdi4ing.de.
[2] Coscine. The research data management platform. Retrieved April 17, 2026 from about.coscine.de.

Abstract:
Professionals in research data management (RDM) face the task of imparting RDM competencies to researchers. However, integrating skills into daily disciplinary routines and transferring them to early-career researchers remains a key challenge. A particular group of academic staff is pivotal here: those supervising under- and postgraduate theses. Alongside Data Stewards, who are still rare in many disciplines, supervisors act as a key link between teaching and research. Through methodology seminars, final-year colloquia, or within the supervisory relationship, they act as facilitators, examiners, and mentors, making them key agents in promoting RDM competencies among early-career researchers.

However, this target group is often overburdened with responsibilities. Thus, “training” them to support the transformation towards a sustainable, RDM-structured science landscape is challenging. As RDM professionals, we need to support them in their essential role as multipliers. Therefore, educational concepts need to create visible and easily reusable benefits for their daily practice.

Currently, approaches like the "Train-the-Trainer Concept on Research Data Management" [1] or the "Learning Objectives Matrix on the Topic of Research Data Management" [2] focus on training RDM staff or young researchers. Additionally, integrating competences concerning research data management and data literacy [3] into curricula remains a key challenge for those responsible for degree and doctoral programmes. We present workshop formats developed specifically for this group of multipliers [4], providing them with ready-to-use methods and resources for low-effort integration. In this way, we sustainably strengthen RDM competency transfer and achieve structural improvements within research teams, creating capacity for creativity and scientific innovation.

We will share insights from our work at universities in Lower Saxony and the FDM-NDS initiative [5] in both a talk and an accompanying poster. Examples include the workshop "Research Data Management for Supervisors of Theses"[4], train-the-trainer and train-the-lecturer formats.

Keywords: rdm, data literacy, academic mentoring, supervisors, FDM-NDS, training materials

[1] Katarzyna Biernacka, Ron Dockhorn, Claudia Engelhardt, Kerstin Helbig, Juliane Jacob, Tereza Kalová, Adienne Karsten, Kristin Meier, Andreas Mühlichen, Janna Neumann, Britta Petersen, Benjamin Slowig, Ute Trautwein-Bruns, Jeanne Wilbrandt, and Cord Wiljes. 2023. Train-the-Trainer-Konzept zum Thema Forschungsdatenmanagement. Zenodo. doi.org/10.5281/zenodo.10122153
[2] Britta Petersen, Franziska Altemeier, Sophie Boße, Maya Dalby, Nina Düvel, Claudia Engelhardt, Mark Fichtner, Canan Hastik, Jan-Michael Haugwitz, Juliane Jacob, Katharina Koch, Alessandra Kuntz, Antje Manske, Andreas Mühlichen, Jorge Murcia Serra, Jochen Ortmeyer, Manuela Richter, Hermann Schranzhofer, Benjamin Slowig, Ute Trautwein-Bruns, Dorothee Urbaum, Anne Voigt, Stephanie Werner, Cord Wiljes, and Linda Zollitsch. 2025. Lernzielmatrix zum Themenbereich Forschungsdatenmanagement (FDM). Zenodo. doi.org/10.5281/zenodo.15025246
[3] Katharina Schüller. 2020. Future Skills: a Framework for Data Literacy - Competence Framework and Research Report. Zenodo. doi.org/10.5281/zenodo.3946067
[4] Sebastian B. C. Lehmann, Franziska Altemeier, and Düvel Nina. Nachhaltige Wissenschaft mit Forschungsdatenmanagement - Eine Einführung für Betreuende von QualifizierungsarbeitenHsH. doi.org/10.25625/EKEEFB
[5] Einstein your data. Retrieved April 29, 2026 from www.uni-vechta.de/bibliothek/for-schungsdatenmanagement/einstein-your-data

Abstract:
Running safe, ethical, and legal research projects requires a specialized set of competencies from researchers and from research data management professionals, such as data stewards. This is particularly true when working with sensitive data, such as pseudonymised health data or personal geolocated data. However, researchers and data stewards who are new to the field may lack important knowledge and competencies for safely running such projects. Given ever-changing technological developments, even experienced staff may profit from continued learning. Structured training for these groups exists, but the material is often quite extensive, spread over multiple locations, focuses mainly on legal aspects, or requires in-person or synchronous online training elements. This means that such training oftentimes has limited scalability and/or requires high time investments. ASSURED [1] addresses this issue by providing a flexible, modular online training system on the safe, ethical, and legal use of sensitive data for different target groups. The modules are short and self-paced, allowing for flexible integration of training into the audiences’ busy schedules. ASSURED covers “core” modules that are relevant to all involved parties (e.g., on data ethics, legal basics on data protection, and statistical disclosure control). We also aim to develop service-specific modules that can be tailored to the data stewards’ institutional context, data type-specific modules, as well as several specialised modules relevant for data professionals (e.g., on user management and output checking). In this talk, we will present the structure of ASSURED and how data
stewards can use and contribute to this platform (a) for their own training and the training of their team members and (b) to train researchers they are working with, thereby streamlining the certification process for data access. During and after the talk, we are inviting feedback from data stewards on ASSURED generally and on developing specialized modules specifically.

Keywords: training, sensitive data, research data management, trusted research
environments

[1] ASSURED. 2025. ASSURED: Safe Research by Safe People. assured-training.org

Abstract:
Ten years after Wilkinson et al. [1] introduced the FAIR principles, "data stewardship" has become a central concept in the research data management discourse, yet the term remains ambiguous, invoked across library and information science, FAIR infrastructure, biomedical informatics, environmental sciences, Indigenous data sovereignty, and corporate governance. Despite this growing prominence, no empirical mapping exists of where scholarly discussions of data stewardship actually take place. Practitioners and emerging data stewards lack a defensible guide to the core literature of their own profession.

This study identifies the core publication venues for research data stewardship through a reproducible bibliometric analysis, and quantifies the concentration or dispersion of the literature across journals. A structured Scopus query combining stewardship terminology with FAIR-, repository-, and lifecycle-related context terms was developed iteratively and validated against known landmark publications. The resulting corpus was cleaned, deduplicated, and analysed using Bradford's Law of Scattering to identify core, middle, and peripheral journal zones. The analysis yields an empirically derived ranked list of core data stewardship journals and a comparison against the venues most commonly cited in
practitioner-facing guidance.

A reproducible, evidence-based map of the data stewardship literature supports professionalisation, curriculum development, and onboarding of new data stewards, and the methodology is extensible to other definitional contexts. This work directly addresses DSgG26's themes of FAIR data excellence and the professionalisation of data stewardship by giving the community an empirical foundation for identifying its own scholarly canon.

Keywords: stewardship, bibliometrics

Abstract:
Effective research data management (RDM) depends on consistent, well-documented practices that are often perceived as routine or burdensome. This hands-on workshop introduces the concepts of gamification and serious games as practical strategies to increase data literacy, improve documentation quality, and foster sustained behavioral change among researchers. Gamification—the integration of game mechanics into non-game context—can improve the RDM by embedding playful and interactive elements into training and workflows, encouraging reflection and long-term adoption of best practices. Routine RDM tasks, such as documenting datasets, curating metadata, or ensuring data completeness, can be transformed into stimulating challenges that promote consistency, collaboration, and a culture of continuous improvement.

In this hands-on workshop, participants will:

• Learn about the differences between games and gamification and its application in RDM.
• Engage in practical exercises, including playing RDM-focused games.
• Explore how gamification elements—such as points, badges, and challenges—can be integrated into RDM workflows.
• Brainstorm and design gamified solutions tailored to the data life cycle or their own institutional contexts.

This workshop builds on the expertise and resources[1] developed by the Sub-WG Training/Further Education of the DINI/nestor WG Research Data featuring the serious game “Gami|cation – the learning-teaching adventure”[2] developed by 'Digital Learning and Gaming Cultures’ cluster at TU Dresden’s CODIP. This session is designed for RDM professionals, data stewards, and trainers seeking approaches to enhance data literacy and foster a culture of responsible data management. By the end of the workshop, participants will leave with ideas and resources to gamify RDM practices or tutorials.

Max Participants: 20
Language: English
Duration: 180min

Keywords: Research Data Management, Gamification, RDM, Behavioral Change

[1] Ron Dockhorn, Claudia Engelhardt, Britta Petersen, and Antje Manske. 2026. Games and Gamification in Research Data Management: Engaging methods to foster better data practices. RDA Deutschland Tagung 2026, Potsdam, Germany. Zenodo. doi.org/10.5281/zenodo.19386540
[2] Linda Christa Friedrich, Matthias Heinz, Josefin Müller, and Michelle Pippig. 2025. Gami|cation: Gamification Within (Higher) Education. In: Advanced Technologies and the University of the Future. Lecture Notes in Networks and Systems, E. Vendrell Vidal, U.R. Cukierman, M.E. Auer (eds.), vol 1140. Springer Nature Switzerland, Cham. 397–412. doi.org/10.1007/978-3-031-71530-3_25
 

Abstract:
Due to ongoing changes in the research landscape, high-quality and up-to-date education in research data management (RDM) is now more important than ever. This is especially true for RDM professionals, such as data stewards, data curators, and RDM trainers, who help achieve sustainable and FAIR (findability, accessibility, interoperability, and reusability) [1] RDM.

To support and strengthen substantial research data curation competencies among RDM professionals, KonsortSWD developed RDM Compas, an information and training platform specifically tailored to the needs of RDM professionals in the social, behavioral, educational, and economic sciences. RDM Compas’ Knowledge Base and Training Center can be used by RDM professionals to access targeted and capacity-building information and training materials. In addition to building in-depth curation competencies, it is also important to support RDM professionals in navigating the key steps of everyday data curation workflows. So-called cheat sheets can help manage and standardize these daily curation processes by
summarizing the most important information about each step of the process (e.g., see [2, 3]). They can serve as checklists for day-to-day work.

In this workshop, participants will create cheat sheets for professional data curation. These will provide a clear overview of the various tasks and resources required for the daily workflows of different types of RDM professionals. Working in small groups, participants will prepare cheat sheets based on a provided template and the information and training materials available on RDM Compas. These will be shared among the participants of the workshop and, with their consent and in recognition of their contribution, with the broader RDM community via Zenodo and RDM Compas.

We propose a maximum workshop size of 20 people and a workshop duration of 3 hours.

Ideally, participants should bring a laptop.

Keywords: RDM professionals, professionalisation of research data curation, data curation lifecycle, competency framework

[1] Mark D. Wilkinson, Michel Dumontier, IJsbrand Jan Aalbersberg, Gabrielle Appleton, Myles Axton, Arie Baak, Niklas Blomberg, Jan-Willem Boiten, Luiz Bonino Da Silva Santos, Philip E. Bourne, Jildau Bouwman, Anthony J. Brookes, Tim Clark, Mercè Crosas, Ingrid Dillo, Olivier Dumon, Scott Edmunds, Chris T. Evelo, Richard Finkers, Alejandra Gonzalez-Beltran, Alasdair J.G. Gray, Paul Groth, Carole Goble, Jeffrey S. Grethe, Jaap Heringa, Peter A.C ’T Hoen, Rob Hooft, Tobias Kuhn, Ruben Kok, Joost Kok, Scott J. Lusher, Maryann E. Martone, Albert Mons, Abel L. Packer, Bengt Persson, Philippe Rocca-Serra, Marco Roos, Rene Van Schaik, Susanna-Assunta Sansone, Erik Schultes, Thierry Sengstag, Ted Slater, George Strawn, Morris A. Swertz, Mark Thompson, Johan Van Der Lei, Erik Van Mulligen, Jan Velterop, Andra Waagmeester, Peter Wittenburg, Katherine Wolstencroft, Jun Zhao, and Barend Mons. 2016. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 1 (March 2016), 160018. doi.org/10.1038/sdata.2016.18
[2] Roman Gerlach, Jessica Rex, Kevin Lang, Nadine Neute, and Volker Schwartze. 2019. Fact Sheet: RDM Requirements - Overview of Funders’ Data Policies. doi.org/10.5281/ZENODO.3739190
[3] Antje Manske and Britta Petersen. 2025. 23 TrainingThings for Writing Learning Objectives. (March 2025). https://doi.org/10.5281/zenodo.15043810

Abstract:
Data stewardship is increasingly recognised as a key element of research support, yet its practical implementation remains shaped by unclear roles, heterogeneous expectations, and evolving institutional frameworks. In particular, the interaction between Data Stewards (DS), Principal Investigators (PIs), and institutional leadership often reveals mismatches in expectations, responsibilities, and decision-making authority.

Building on insights from the DataStew project and ongoing community exchange formats, this workshop creates a dedicated space for Data Stewards to reflect on their experiences within these constellations. The first part of the workshop focuses on sharing and analysing practical experiences: What challenges arise in collaboration with PIs and leadership? Where do misunderstandings, conflicts, or structural barriers occur?

The second part shifts towards a forward-looking perspective by eliciting expectations and wishes of Data Stewards towards PIs and institutional leadership. What kinds of support, communication, and strategic embedding are needed to enable effective data stewardship? Which organisational and cultural conditions would strengthen the role and sustainability of Data Stewards? By connecting individual experiences with broader questions of professionalisation
and institutionalisation, the workshop contributes to a better understanding of the interface between operational practice and strategic decision-making. The results will feed into the planned project DataStewDialogue with academic and institutional stakeholders and help inform recommendations for strengthening data stewardship in research organisations.

The workshop will start with a short introduction to the idea of DataStewDialogue. This will be followed by two rounds of active discussion involving the participants: first a “storytelling carousel” to collect the experiences of data stewards concerning the collaboration with superiors, PIs and other leaderships, second a “future back canvas” to explore the expectations of the participants for the future development of leadership.

Keywords: Data Stewardship Professionalisation, Institutional Collaboration, Stakeholder Engagement
 

Abstract:
The EU OSTrails (2024-2027) project aims to support researchers throughout the entire research process: from data management planning (plan) to data creation and use (tracking) to quality-checked (assess) sharing [1]. A central means to achieve this goal is the use of machine-actionable data management plans (maDMPs) [2]. These enable a smoother transition between different tools used at different phases of a research project and data can be tracked more easily throughout the entire research process. In the project RWTH Aachen university library acts as the national pilot for Germany. In this capacity, we apply the interoperability frameworks developed during the last two years to the specific situation in Germany. The essential part of our work is the exemplary integration of the DMP tool RDMO [3] with electronic lab notebooks such as Chemotion [4] or eLabFTW [5]. This does not only benefit the general interoperability of these systems, but indeed supports researchers in managing their data efficiently without being forced to start in each tool from scratch. Within a 90 minutes hands-on session, we want to try out the steps taken so far. We are
addressing up to 20 users of electronic lab notebooks that might or might not also use data management planning tools. We want them to create an RDMO-based DMP (you can also bring existing ones!), apply the mapping to electronic lab notebooks and give feedback in which regard the resulting structures are helpful (or not), what is missing, what might be misleading in order to improve the prototype. We will also discuss what kind of return – from electronic lab
notebooks to a later stage DMPs – might be helpful, e.g. feeding DOIs of published data back to keep track of results is a rather obvious thing. We are looking forward to your findings and to level the prototype to practical solutions for researchers and data stewards.

Keywords: data management plans, electronic lab notebooks

[1] ostrails.eu
[2] Tomasz Miksa, Paul Walk and Peter Neish. 2020. RDA DMP Common Standard for Machine-actionable Data Management Plans. Zenodo. doi.org/10.15497/rda00039
[3] rdmorganiser.github.io
[4] Pierre Tremouilhac, An Nguyen, Yu-Chieh Huang, Serhii Kotov, Dominic Sebastian Lütjohann, Florian Hübsch, Nicole Jung & Stefan Bräse. 2017. Chemotion ELN: an Open Source electronic lab notebook for chemists in academia. J Cheminform 9, 54. doi.org/10.1186/s13321-017-0240-0
[5] www.elabftw.net
 

Abstract:
The rapid expansion of microbial omics data - from raw sequence reads and assembled genomes to consensus sequences and associated process data offers immense potential for advancing microbiome research. At the same time, there is a growing need to include emerging and underrepresented ones, such as imaging and spatial omics datasets, which are essential for a more comprehensive research data ecosystem.
Realizing the full scientific value of these resources requires that data are not only generated at scale, but also deposited, described, and shared in accordance with FAIR principles [1] . However, the consistent implementation of data and metadata standards across microbiological domains remains uneven, with submissions to public repositories such as the European Nucleotide Archive showing considerable variability in annotation depth, controlled vocabulary usage, and adherence to community standards.

NFDI4Microbiota, as part of the National Research Data Infrastructure (NFDI) addresses these challenges by providing access to metadata standards, training, and analysis services tailored to the full spectrum of microbial research spanning
bacteriology, virology, mycology. [3]. A central focus is the development and promotion of standardized metadata frameworks that capture biological, experimental, and administrative context enabling robust discoverability and cross-study comparability [4] .

In this workshop we present use cases based on publicly available datasets including food microbiology studies (ENA: PRJNA947753) [5], large-scale microbiome sampling(ENA: PRJEB56918) [6]. These examples illustrate the diversity and
complexity of metadata requirements across subdisciplines and reflect three main data classes: raw research data, computationally derived process data, and supporting contextual files.

Participants will explore how current submissions align with FAIR principles and identify gaps in metadata completeness. We highlight practical recommendations for metadata registration at the sample, experimental, and analytical levels, including the use of community standards such as Genomic Standards Consortium checklists and the Environment Ontology, while also addressing areas lacking established ontologies. The workshop is designed for approximately 30 participants and requires only a laptop, ensuring accessibility.

Keywords: Data Management & Standards, Infrastructure & Repositories, Data types and workflows, FAIR principles, Community and Training

[1] Wilkinson, Mark D., Michel Dumontier, IJsbrand Jan Aalbersberg, Gabrielle Appleton, Myles Axton, Arie Baak, Niklas Blomberg et al. "The FAIR Guiding Principles for scientific data management and stewardship: Comment." Scientific data 3 (2016): 160018.
[2] Leinonen, Rasko, Ruth Akhtar, Ewan Birney, Lawrence Bower, Ana Cerdeno-Tárraga, Ying Cheng, Iain Cleland et al. "The European nucleotide archive." Nucleic acids research 39, no. suppl_1 (2010): D28-D31.
[3] Förstner, Konrad U., Anke Becker, Jochen Blom, Peer Bork, Thomas Clavel, Marius Dieckmann, Alexander Goesmann et al. "NFDI4Microbiota–national research data infrastructure for microbiota research." Research Ideas and Outcomes 9 (2023): e110501
[4] Yilmaz, Pelin, Renzo Kottmann, Dawn Field, Rob Knight, James R. Cole, Linda Amaral-Zettler, Jack A. Gilbert et al. "Minimum information about a marker gene sequence (MIMARKS) and minimum information about any (x) sequence (MIxS) specifications." Nature biotechnology 29, no. 5 (2011): 415-420.
[5] Zeng, Yan, Yufeng Li, Jin Yu, and Pei-Bin Zeng. "Metagenomic and Metatranscriptomic Approaches to Reveal the Dynamic of Microbial Composition and Active Genera During Doubanjiang Meju Fermentation."

Abstract:
Data stewards often face persistent challenges when describing research data with metadata. Metadata are frequently recorded in non-standardised formats and often lack semantic annotations, which limits their reusability, discoverability, and the overall FAIRness of the data. In addition, there is often uncertainty about which standards and vocabularies should be used within specific research communities.

The AIMS Metadata Profile Service [1], developed in the DFG-funded AIMS project (Applying Interoperable Metadata Standards), addresses these challenges by enabling the creation, sharing and reuse of interoperable, machine-readable metadata profiles based on RDF and SHACL. By linking metadata elements to well-defined ontological concepts, AIMS supports both humans and machines to better understand, integrate, and analyse complex datasets across domains.

With the AIMS Metadata Profile Service, data stewards can iteratively create domain-specific profiles by selecting relevant ontology terms and defining constraints such as cardinalities and data types. These profiles can be used to automatically generate web forms that guide researchers in capturing consistent, machine-readable metadata without requiring expertise in semantic technologies. The resulting metadata can be directly integrated into knowledge graphs, thereby improving interoperability and reuse.

This poster presents the AIMS Metadata Profile Service and illustrates the transition from heterogeneous and non-standardised metadata practices to a profile-based approach for creating interoperable, semantically enriched metadata.

Keywords: Metadata, RDF, Interoperability, Knowledge Graph

[1] NFDI4ING. Metadata Profile Service. Retrieved April 17, 2026 from profiles.nfdi4ing.de.

Abstract:
The Federal State Initiative for Research Data Management in Baden-Württemberg (bwFDM) offers a ten-month certificate course for Data Stewards and other data professionals, which launched in October 2025. A distinctive feature of this course is that participants will receive a Certificate of Advanced Studies (CAS) as well as microcredentials for specific learning units. The course encompasses all key areas of research data management (RDM). It also equips participants with relevant soft skills, including communication techniques, teaching skills, project management, and change management. The curriculum consists of six learning units: (1) Basic Concepts, (2) Data Organization, (3) Law and Ethics, (4) Technical Infrastructure, (5) Consulting, Training, and Project Management, and (6) Data Manipulation and Versioning. During the final phase of the course, participants complete a mandatory course-related project over six weeks, resulting in a graded written assignment to qualify for the CAS. Microcredentials are available for learning units (1) Basic Concepts, (3) Law and Ethics, and (4) Technical Infrastructure. The course is structured to be manageable alongside work: The workload is approximately eight hours per week, which includes three hours of interactive online training. A significant portion of the training consists of self-learning units. The course materials and lessons are curated and administered by RDM professionals to ensure up-to-date content and provide learners with first-hand insights from the field. The course language is German. The second cohort of CAS FDM will begin in October 2026. In this contribution, we report on our experiences in setting up and delivering the first course iteration, as well as the further development and evolution of the program with regard to e-learning materials, including an educational game designed to accompany the course from
October 2026 onward.

Keywords: data stewardship, research data management, training, certificate, microcredential, e-learning

Abstract:
The data-steward-network of the Max Weber Foundation (MWS) has established a research data publication workflow for researchers working at the foundation’s institutes [1] worldwide. Aiming at a smooth and connected infrastructure that eases the way to share and publish research data, the MWS takes advantage of its own RDMO (Research Data Management Organiser) instance [2] and uses existing interfaces. The entire workflow linking RDMO with other components of the institutional RDM is illustrated in detail on the poster.

The MWS questionnaire [3] was created in collaboration with the data-steward-network of the MWS and re-used within the NFDI consortium Text+. MWS data stewards, based at each of the eleven MWS institutes, encourage researchers to start their projects in RDMO. This step is mandatory for reporting and monitoring via the MWS project database [4]. While the interface and export function of this internal database was implemented and customised for the MWS,
the research data publication workflow integrates existing export functions that connect RDMO to other research data repositories.

At the dataset level, the metadata for a data publication can be exported to Zenodo using an existing RDMO plugin [5]. Research data published on Zenodo can in turn be harvested by the MWS’s publication platform perspectivia.net [6] and thus automatically indexed and made more widely findable in library catalogues, e.g. in BASE [7]. The required mapping procedure can be adjusted by an importer tool provided by VZG, head office of the Common Library Network [8].

The ability to export to Zenodo and perspectivia.net is not only a technical advantage but also motivates researchers to conceptualize their projects as data publications from the beginning. Researchers can use RDMO as a tool to prepare their datasets for publication, while saving time and effort by not entering metadata at each step.

Keywords: publication workflow, research data infrastructure, FAIR research data

[1] Max Weber Foundation. 2026. Humanities Institutes Abroad Worldwide. Retrieved April 15, 2026 from  https://www.maxweberstiftung.de/en/research.html#content-one
[2] Max Weber Foundation. 2026. Welcome to RDMO. Retrieved April 15, 2026 from rdmo.maxweberstiftung.de
[3] RDMO community. 2026. RDMO-Fragenkatalog der Max Weber Stiftung für Geisteswissenschaften. Retrieved April 15, 2026 from github.com/rdmorganiser/rdmo-catalog/tree/main/shared/MaxWeberStiftung
[4] Max Weber Foundation. 2026. Project Database. Retrieved April 15, 2026 from www.maxweberstiftung.de/en/research/projects/project-database.html
[5] Max Weber Foundation. 2026. The publication platform perspectivia.net. Retrieved April 15, 2026 from perspectivia.net/content/index.xml
[6] RDMO community. 2026. RDMO-Plugins-Zenodo. Retrieved April 15, 2026 from github.com/rdmorganiser/rdmo-plugins-zenodo
[7] Bielefeld University Library. 2026. Bielefeld Academic Search Engine (BASE). Retrieved April 16, 2026 from www.base-search.net
[8] GBV-Website. 2026. Head Office (VZG). Retrieved April 15, 2026 from en.gbv.de/informations/Verbundzentrale-en

Abstract:
NFDI4Microbiota – the German National Research Data Infrastructure for Microbiota Research – aims to empower the microbiology research community through improved access to data, analysis services, standardized metadata, and training. The NFDI4Microbiota Helpdesk is our central contact point designed to assist researchers across all domains of microbiology with questions related to microbial data and associated metadata.

We welcome questions from all individuals working with microbial data – whether students, early-career researchers, senior scientists, or data stewards. Support is provided irrespective of the organism, environment, or data type. Requests are addressed by a distributed network of NFDI4Microbiota experts, including researchers, software developers and data stewards, ensuring a wide range of expertise.

Support is delivered through a two-tiered system:

1.  A core team of data stewards and research data management (RDM) professionals
   handles requests related to RDM within the context of microbiology.
2. Domain-specific experts provide deeper insights into topics such as workflow
   standards, tool provenance, database setup and the use of NFDI4Microbiota
   services.

Researchers can reach the Helpdesk via a dedicated email address (helpdesk@nfdi4microbiota.de) or an online contact form (https://nfdi4microbiota.de/contact-form.html). All questions are documented, and recurring
topics are distilled into a curated FAQ and Knowledge Base – resources designed to serve both as a self-help hub and a living reference for the broader community. Common questions include “How can I participate in NFDI4Microbiota?” or “What metadata standards are used to document microbiology data?”.

By streamlining access to expert support and curated resources, the NFDI4Microbiota Helpdesk plays a crucial role in enhancing the reproducibility, transparency and efficiency of microbiota research in Germany. It acts not just as a support system, but as a bridge between scientific inquiry and sustainable research data infrastructure.

Keywords: helpdesk, support, FAQ, Knowledge Base, microbiology

Abstract:
Despite widespread adoption of the FAIR principles (Wilkinson et al., 2016), research data management (RDM) continues to face persistent challenges in practice, including limited data sharing across organisational boundaries, lack of control over data reuse, difficulties in integrating sensitive datasets, and the coordination of distributed infrastructures and responsibilities. Data trusts have emerged as a promising socio-technical approach to address these challenges by acting as trusted intermediaries that organise and govern data exchange. In the European context, such models can be understood as data intermediation services under the Data Governance Act, which introduces specific legal requirements regarding neutrality, trustworthiness, and governance. However, their practical implementation remains complex, particularly with regard to the underlying technical architectures and their implications for data stewardship.

This contribution introduces an architecture-centred typology of data trusts as a tool to better understand how different technical designs can address specific RDM challenges. The typology is based on a systematic analysis of data flow and processing logic and distinguishes four core types: organisational intermediation, technical intermediation, centralised data repositories, and transactional processing environments (“compute-to-data”), complemented by specialised types for consent management and secure data linkage. By relating these architectural types to typical RDM scenarios, the framework highlights how different models support data discovery, enable federated data sharing, facilitate standardisation and long-term curation, or allow secure analysis of sensitive data without transferring raw data.

The proposed typology demonstrates that architectural choices are not merely technical decisions but directly shape how core RDM requirements such as data sovereignty, interoperability, and secure reuse can be realised in practice. At the same time, regulatory requirements—such as those introduced by the Data Governance Act—are ultimately implemented through architectural design. By linking data trust architectures to concrete use cases and user needs, the contribution provides data stewards with a structured lens for selecting and communicating appropriate infrastructure solutions. In doing so, it supports the DSgG community in advancing data stewardship practices by connecting FAIR principles with
implementable organisational, technical, and regulatory designs.

The poster visualises the architectural types through schematic diagrams and links them to typical RDM scenarios and user groups.

Keywords: Data Governance, Data Trust Architectures, Data Stewardship.

Abstract:
The NFDI4BIOIMAGE consortium contributes to the growing landscape of research data management (RDM) in Germany by developing domain-specific yet transferable solutions for bioimaging data. Within this context, it also fosters and supports data stewardship practices across the community.
In line with the conference theme “from going to growing”, our poster highlights how NFDI4BIOIMAGE moves beyond initial service provision towards the implementation of sustainable, community-driven RDM practices. We introduce our data stewardship concept, which supports researchers across the full data lifecycle and aligns with FAIR principles while addressing the specific challenges of bioimaging data.
A key component of our approach is our Helpdesk, designed as a central, low-threshold entry point for researchers, imaging facility personnel and data stewards. It facilitates access to expertise, connects distributed knowledge within the consortium, and supports practical problem-solving in everyday RDM scenarios. In addition, our Helpdesk is closely connected with other domain and cross-domain helpdesks in continuous exchange, enabling coordinated support structures and knowledge provision across the broader RDM ecosystem.
Furthermore, we present the NFDI4BIOIMAGE service portfolio which provides tools and support for FAIR bioimage data handling and analysis, as well as guidance for their practical application.
By sharing our experiences, we aim to engage with data stewards from diverse disciplines, exchange best practices, and explore opportunities for cross-domain collaboration within and beyond the NFDI framework. This contribution invites discussion on how domain-specific initiatives can collectively strengthen and scale data stewardship in a growing community.

Keywords: Bioimaging, helpdesk, NFDI

Abstract:
RDM gains importance in the scientific community as a method both for quality assurance and solidarity among researchers. This brings with it changes in processes like grant-writing or publications which affect the majority of all scientific work. In some domains, especially the natural sciences, these changes appear to go relatively smoothly, since at least some aspects of RDM are being taught in the curricula, e.g. documentation protocols. In other domains, like in this case in manufacturing process engineering, the implementation of RDM seems to be harder. This is suspected to be caused by very heterogenous data along the research paths as well as the decision-making process in the development of machine parts etc., which is rather hard to document and quantify. These domain characteristics may be part of the reasons why the state of RDM in engineering sciences is, as of today, quite extendable and improvable.
Nevertheless, even in engineering research, the growing importance of RDM is understood and there are approaches to integrate RDM strategies into the research routine. In this poster, the approach at the Institut für Textiltechnik of RWTH Aachen University is presented. It consists of a number of different measures to evolve from a niche interest of some researches to an institute-wide culture. It addresses different RDM user groups, the domain specific difficulties and the institutional organization in a bottom-up approach. Successes and remaining challenges are being discussed. The approach is presented hoping to give an example or inspiration for other institutions that find it difficult to implement RDM measures in the institute culture.

Keywords: engineering, implementation, change process, research culture

Abstract:
Establishing sustainable research data management (RDM) and a FAIR data culture requires high-quality, domain-specific training materials that bridge the gap between abstract principles and practical workflows. To address the specific training needs of early-career researchers, such as master students, doctoral candidates, and postdocs, the chemistry consortium of the National Research Data Infrastructure (NFDI4Chem) has developed a comprehensive educational framework designed for chemistry and related disciplines. As NFDI4Chem focuses in the second funding phase on achieving a lasting cultural impact beyond the project’s duration, the reusability and adaptability of the materials provided were particularly important, leading to the publication of an extensive open educational resource (OER).

This poster contribution showcases our OER, 'FAIR Chemistry Data: A Research Data Management Workshop Concept'. [1] The OER derived from our chemistry-specific RDM workshop (and its related materials [2,3]) that has been successfully delivered over 40 times to more than 800 participants since 2022. The resource comprises a modular training package covering the topics basics of RDM, documentation, electronic laboratory notebooks, and data
preservation. Highlights of the publication include versatile slide decks, detailed speaker notes, well-described interactive exercises, and chemistry-specific examples. One key strength is the range of relevant tools, standards, services, and practices for data management in chemistry. To ensure maximum accessibility and flexibility for the community, all materials have been released under a CC0 licence.

Thus, the OER provides data stewards and educators with a ready-to-use yet highly customisable blueprint for delivering local RDM support. The materials provide structured conceptual input alongside interactive elements, allowing trainers to adapt the content to specific learning objectives, target groups, or institutional requirements. The OER lays the groundwork for the modularisation of our training framework for specific target groups, fostering community-driven knowledge transfer and supporting the professionalisation of data stewardship in the chemical sciences and beyond.

Keywords: NFDI4Chem, workshop, OER, open educational resource, training, research data management, RDM, research data, FAIR

[1] Annett Schröter, Ann-Christin Andres, Fabian Fink, and Benjamin Golub-Overbeck. 2026. FAIR Chemistry Data: A Research Data Management Workshop Concept. Zenodo (March 2026). doi.org/10.5281/zenodo.19084779
[2] Ann-Christin Andres, Benjamin Golub, Daniela Hausen, Sonja Herres-Pawlis, John Jolliffe, Johannes Liermann, Pascal Scherreiks, and Annett Schröter. 2023. FAIR Research Data Management: Basics for Chemists. Zenodo (August 2023).
doi.org/10.5281/zenodo.8238499
[3] Daniela Hausen, Annett Schröter, Ann-Christin Andres, and Benjamin Golub-Overbeck. 2024. 27 FAIR RDM exercises - A (Chemistry-specific) Catalogue. Zenodo (August 2024). https://doi.org/10.5281/zenodo.13353021

Abstract:
The Base4NFDI initiative within the German National Research Data Infrastructure (NFDI) develops cross-domain Basic Services to support sustainable and FAIR research data practices across disciplines. As a joint effort of all 26 NFDI consortia, it advances nine Basic Services into interoperable, long-term solutions designed to meet the needs of diverse scientific communities. 

Sustainable, long-term Research Data Management (RDM) services depend on continuous and meaningful user engagement from the research community. The Base4NFDI development framework supports this by involving users early through requirement analysis, target group identification, persona workshops, and user story development, alongside training and documentation. Incubator projects allow testing with early adopters to ensure services meet real needs and encourage broad uptake.
Data Stewards constitute a key target group within this framework. Functioning as end-users, co-developers, and institutional facilitators, they mediate between domain-specific requirements and technical implementation, thereby mitigating adoption barriers and embedding RDM practices into institutional workflows. Their active involvement serves as a necessary corrective to top-down service design, ensuring outputs remain aligned with operational realities.

Our poster builds on this development framework by presenting a selection of available Basic Services relevant to Data Stewardship practice, including Jupyter4NFDI for reproducible workflows, IAM4NFDI for federated identity and access management, TS4NFDI for harmonization of terminologies, DMP4NFDI for standardizing data and software management plans, and RDMT4NFDI for customizable modular training concepts. It details openly published components of the user engagement framework designed to accelerate service uptake and support institutional change processes.

By linking coordination, service development, and user engagement, Base4NFDI supports the broader RDM ecosystem together with Data Stewards and contributes to sustainable infrastructures. At the 2026 Data Stewardship conference in Cologne, we invite Data Stewards and RDM professionals to engage with our services and training resources, assess their relevance for their work and provide feedback on usability and gaps.

Keywords: DSgG 2026, Base4NFDI, Basic Services, Research Data Management, User Engagement, FAIR Data Principles
 

Abstract:
Researchers increasingly need accessible, self-paced training to systematically develop Research Data Management (RDM) competencies, yet tools that provide both structured progression and clear visibility into skill development remain rare. This contribution presents RDM Navigator, a web-based learning platform currently in conceptual design, that guides researchers through RDM competency development using a gamified interface structured around the research data lifecycle.

The platform’s central interface element is a progress wheel divided into six segments corresponding to lifecycle stages: Data Planning, Collection, Processing & Analysis, Preservation, Sharing & Publication, and Reuse. Within each stage, learners progress through three competency levels — Beginner, Intermediate, and Advanced — sequentially; cross-stage navigation remains freely accessible, supporting self-directed learning paths. A self-assessment quiz recommends an appropriate entry level per topic, while pass/fail quizzes with unlimited retakes gate progression within each stage.

Educational content is structured using the QUADRIGA Datenkompetenzframework [1], a community-developed model covering 15 RDM competency areas across knowledge, skills, and attitude dimensions. Each lesson additionally links to institutional RDM services and infrastructure, bridging conceptual competency development with local practice.

The gamification design draws on evidence that structured challenge levels, visual progress indicators, and achievement mechanics meaningfully enhance learner motivation and sustained engagement in e-learning contexts [2, 3]. Design decisions prioritize intrinsic motivation, avoiding the pitfalls of superficial reward systems identified in the literature.

This poster introduces the learning architecture and design rationale of RDM Navigator and invites discussion on how gamified, lifecycle-structured tools can complement institutional RDM support strategies and data stewardship practice.

Keywords: Research Data Management, gamification, e-learning, competency framework, data literacy, research data lifecycle

[1] QUADRIGA. 2025. QUADRIGA Datenkompetenzframework v3.3. Zenodo. doi.org/10.5281/zenodo.15058057
[2] Awaz Naaman Saleem, Narmin Mohammed Noori, and Fezile Ozdamli. 2022. Gamification Applications in E-learning: A Literature Review. Technology, Knowledge and Learning 27, 1 (2022), 139–159. doi.org/10.1007/s10758-020-09487-x
[3] Wafaa Elsawah. 2025. Exploring the Effectiveness of Gamification in Adult Education: A Learner-Centric Qualitative Case Study in a Dubai Training Context. International Journal of Educational Research Open 9 (2025), 100465. doi.org/10.1016/j.ijedro.2025.100465
 

Abstract:
The Leibniz Institute for Catalysis (LIKAT) conducts diverse research spanning homogeneous, heterogeneous, and electrocatalysis. Effective Research Data Management (RDM) is critical for internal collaborations and leverage modern data science tools. Currently, two Electronic Laboratory Notebook (ELN) systems are being used for the areas of chemical synthesis and heterogeneous catalysis and spectroscopy. While this is currently the solution that works, it does not allow for seamless cooperation between the individual departments.

We therefore aim to establish a comprehensive RDM system across all departments, including analytics, integrating an ELN and a file management system with granular rights management. We are building upon the existing prototype, CaReD (CatalysisResearchData manamagementsystem) [1], an ontology-driven Laboratory Information Management System (LIMS) that stores data with rich semantics and is compatible with knowledge graphs to facilitate the
application of Artificial Intelligence (AI) for experiment optimization and automatic analysis. The implementation faces significant challenges due to the variety of research paradigms and workflows since the actual “result” can vary, ranging from newly synthesized molecules or materials to in-situ characterization spectra and selectivity metrics of catalytic tests. Developing a unified RDM infrastructure at LIKAT requires bridging distinct scientific workflows and the possibility of rapid adaptation to new scientific needs.

This work provides a practical case study of implementing FAIR data principles in a multidisciplinary research environment eventually enabling AI readiness accelerating scientific discovery.

Keywords: ELN, institutional RDM, LIMS, catalysis

[1] CaReD at NFDI4Cat: nfdi4cat.org/nfdi4cat/en/Services/CaReD.html

Abstract:
Since the publication of the FAIR principles [1] in 2016, research data infrastructure has evolved significantly. FAIR has provided an important framework, but FAIR is not synonymous with quality. Reproducibility, factual accuracy, and scientific data quality are deliberately excluded from the FAIR principles [2, 3]. What truly constitutes good data and software quality has not yet been defined by consensus or made systematically measurable. Researchers in the Helmholtz Association explicitly call for data and software publications to be recognised in performance evaluations [4]. This wish is difficult to fulfil without clear quality criteria. The situation is further complicated by the fact that established databases such as Web of Science, Scopus, and even OpenAlex still do not systematically index these publications [5]. Several international initiatives call for rethinking research evaluation, yet without measurable criteria,
these ambitions remain difficult to operationalise [6].

The Helmholtz Association addresses this challenge with two quality indicators: one for research data (FAIR-C) and one for research software (FAIR-ST) publications [7]. By combining the FAIR principles with a five-level maturity model, they translate abstract quality criteria into a practical evaluation framework, visualised as a radar plot that shows at a glance how a publication performs in terms of openness, metadata quality, or reusability. The indicators are currently in the testing phase; their mandatory implementation as institutional KPIs will begin in 2029.

Initial tests at HZDR reveal two structural findings. First, a repository bias: established repositories such as RODARE [8] automatically achieve higher scores. The indicators currently capture infrastructure maturity rather than intrinsic data quality, with meaningful variance emerging primarily where human judgement is required. Second, an inventory problem: many institutions lack a systematic record of their data and software publications, making
systematic quality assessment difficult to initiate.

The poster presents the indicators' design and invites the data stewardship community to discuss responsibilities for data collection, approaches to missing inventories, and how investments in data and software quality can ultimately be rewarded.

Keywords: FAIR principles, research data quality, research assessment, repository
bias, data stewardship

[1] Wilkinson, M. D. et al. 2016. The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data 3 (160018). doi.org/10.1038/sdata.2016.18
[2] Mons et al. 2020. The FAIR Principles: First Generation Implementation Choices and Challenges. Data Intelligence 2(1-2). doi.org/10.1162/dint_r_00024
[3] Miller et al. 2025. A FAIR Perspective on Data Quality Frameworks. Data 10(9), 136. doi.org/10.3390/data10090136
[4] Vleugel, M. et al. 2026. Divergence between Perceived and Desired Criteria for Assessing Researchers. Helmholtz Open Science Office. doi.org/10.5281/zenodo.18944700
[5] Torres-Salinas, D. and Arroyo-Machado, W. 2026. The 'Big Three' of Scientific Information: A Comparative Bibliometric Review of Web of Science, Scopus, and OpenAlex. InfluScience Editions. doi.org/10.5281/zenodo.18411229
[6] DORA: San Francisco Declaration on Research Assessment, 2012. sfdora.org. CoARA: Coalition for Advancing Research Assessment, 2022. coara.eu. Barcelona Declaration on Open Research Information, 2024. barcelona-declaration.org
[7] Genderjahn S. et al. 2026. A Framework for Assessing Research Data and Software Publications – The Helmholtz Quality Indicators. Helmholtz Open Science Office. doi.org/10.48440/os.helmholtz.085
[8] Helmholtz-Zentrum Dresden – Rossendorf. 2018. RODARE – Rossendorf Data Repository. re3data.org. doi.org/10.17616/R3BR40

Abstract:
As research data volumes and analytical complexity grow, ad hoc file handling and manual copy–paste workflows increasingly break data lineage and undermine scientific reproducibility. This leads to the loss of critical provenance information and impeding the reusability of research outputs. DataLad[1][2][3] addresses these challenges by providing a transparent, version-controlled, and fully reproducible framework for managing data and workflows with
complete audit trails of file locations and reproducibility of analysis steps.

DataLad is a free, open-source, distributed data management system built on git and git-annex that captures detailed provenance for every file creation, modification, and transformation. Its cross-platform availability (Linux, macOS, Windows) and Python-based command-line interface make it accessible for both programmers and non-programmers. Key features include dataset and code version control, lightweight pointers to large files (git-annex), automatic
capture of provenance metadata for commands and pipelines, and straightforward integration with common computational environments and workflow engines. Furthermore, it supports publishing and sharing to GitHub, GitLab, and Cloud storage simplifying open, collaborative science and promoting the FAIR data principles.

By presenting this poster, I aim to foster meaningful discussions, share insights on the usage, and inspire further exploration of FAIR data handling in daily research work.

Keywords: Research Data Management, DataLad, RDM, Data Provenance

[1] Yaroslav O. Halchenko et al. 2021. DataLad: distributed system for joint management of code, data, and their relationship. Journal of Open Source Software 6, 63 (July 2021), 3262, doi.org/10.21105/joss.03262
[2] Adina Svenja Wagner. 2023. DataLad: An Introduction to Research Data Management. M. Hanke & L. Waite (eds.). Independently published. Groß Twülpstedt. ISBN: 979-8857037973
[3] DataLad Developers. 2015. DataLad. Accessed April 26, 2026 from www.datalad.org

Abstract:
The FAIRagro Helpdesk, operated by the Data Steward Service Center (DSSC), has become the central point of contact for research data management (RDM) in agrosystem sciences. As part of the FAIRagro consortium within the German National Research Data Infrastructure (NFDI), the Helpdesk acts as a vital human interface between agrosystem science researchers, FAIRagro use cases, and FAIRagro service developers.

Over the past three years, a distributed team of six domain-specific data stewards has supported researchers across Germany and beyond with a wide range of RDM questions. Their expertise spans soil and field data, ‘omics’ and phenotyping data, DMPs, RDMO, robotics and sensor data, large-scale geospatial datasets, and legal and ethical aspects such as licensing and data protection [1]. Operating under the principle “from the community for the community”, the Helpdesk delivers tailored, discipline-specific guidance throughout the research data life cycle - evolving from an initial concept to a high-impact community resource.

To effectively engage its target audience, the DSSC implemented proactive outreach strategies, including participation in RDM trainings, collaborations and networking with partner institutions. Complementary resources are provided such as the FAIRagro Knowledge Base (community-oriented reference covering RDM fundamentals, agrosystem-specific data practices derived from the helpdesk tickets) also offering a low-threshold access to Open Educational Resources (OER) [2] and repository recommendations [3]. Further, the DSSC is actively involved in the development of OER for agrosystem science researchers [4].

Quantitative analyses of anonymized helpdesk ticket statistics with over 225 processed tickets to date, demonstrate a steady increase in community engagement and highlight correlations between outreach activities and service uptake. This poster reflects on key achievements, challenges, and lessons learned, illustrating how community-driven data stewardship fosters sustainable RDM practices and supports the implementation of FAIR principles in agrosystem research.

Keywords: Helpdesk, Stewardship, RDM, Training, FAIRagro

[1] Marcus Schmidt, Florian Beyer, Elena Rey Mazón, Wahib Sahwan, Lea Sophie Singson, Lucia Vedder, Sophie Boße and Nikolai Svoboda. 2023. DATA FACT SHEETS of the Data Steward Service Center (DSSC) from the NFDI consortium FAIRagro. PUBLISSO. doi.org/10.4126/FRL01-006461782
[2] Sophie Boße, Lea Sophie Singson, Wahib Sahwan, Elena Rey Mazón and Lucia Vedder. 2024. How to Learn and Teach Research Data Management in Agrosystem Research - a FAIRagro Collection (1.0). Zenodo. doi.org/10.5281/zenodo.11148701
[3] Elena Rey Mazón, Lucia Vedder, Florian Beyer and Marcus Schmidt. 2024. Recommended repositories by the FAIRagro Helpdesk for agrosystem research (1.0). Zenodo. doi.org/10.5281/zenodo.11144471
[4] Sophie Boße, Elena Rey Mazón, Wahib Sahwan, Lea Sophie Singson and Lucia Vedder. 2026. Research Data Management for Agrosystem Sciences - Modular Training Material for Reuse (1.0). PUBLISSO. doi.org/10.4126/FRL01-006527953

Abstract:
Professionals in research data management (RDM) face the task of imparting RDM competencies to researchers. However, integrating skills into daily disciplinary routines and transferring them to early-career researchers remains a key challenge. A particular group of academic staff is pivotal here: those supervising under- and postgraduate theses. Alongside Data Stewards, who are still rare in many disciplines, supervisors act as a key link between teaching and research. Through methodology seminars, final-year colloquia, or within the supervisory relationship, they act as facilitators, examiners, and mentors, making them key agents in promoting RDM competencies among early-career researchers.

However, this target group is often overburdened with responsibilities. Thus, “training” them to support the transformation towards a sustainable, RDM-structured science landscape is challenging. As RDM professionals, we need to support them in their essential role as multipliers. Therefore, educational concepts need to create visible and easily reusable benefits for their daily practice.

Currently, approaches like the "Train-the-Trainer Concept on Research Data Management" [1] or the "Learning Objectives Matrix on the Topic of Research Data Management" [2] focus on training RDM staff or young researchers. Additionally, integrating competences concerning research data management and data literacy [3] into curricula remains a key challenge for those responsible for degree and doctoral programmes. We present workshop formats developed specifically for this group of multipliers [4], providing them with ready-to-use methods and resources for low-effort integration. In this way, we sustainably strengthen RDM competency transfer and achieve structural improvements within research teams, creating capacity for creativity and scientific innovation.

We will share insights from our work at universities in Lower Saxony and the FDM-NDS initiative [5] in both a talk and an accompanying poster. Examples include the workshop "Research Data Management for Supervisors of Theses"[4], train-the-trainer and train-the-lecturer formats.

Keywords: rdm, data literacy, academic mentoring, supervisors, FDM-NDS, training materials

[1] Katarzyna Biernacka, Ron Dockhorn, Claudia Engelhardt, Kerstin Helbig, Juliane Jacob, Tereza Kalová, Adienne Karsten, Kristin Meier, Andreas Mühlichen, Janna Neumann, Britta Petersen, Benjamin Slowig, Ute Trautwein-Bruns, Jeanne Wilbrandt, and Cord Wiljes. 2023. Train-the-Trainer-Konzept zum Thema Forschungsdatenmanagement. Zenodo. doi.org/10.5281/zenodo.10122153
[2] Britta Petersen, Franziska Altemeier, Sophie Boße, Maya Dalby, Nina Düvel, Claudia Engelhardt, Mark Fichtner, Canan Hastik, Jan-Michael Haugwitz, Juliane Jacob, Katharina Koch, Alessandra Kuntz, Antje Manske, Andreas Mühlichen, Jorge Murcia Serra, Jochen Ortmeyer, Manuela Richter, Hermann Schranzhofer, Benjamin Slowig, Ute Trautwein-Bruns, Dorothee Urbaum, Anne Voigt, Stephanie Werner, Cord Wiljes, and Linda Zollitsch. 2025. Lernzielmatrix zum Themenbereich Forschungsdatenmanagement (FDM). Zenodo. doi.org/10.5281/zenodo.15025246
[3] Katharina Schüller. 2020. Future Skills: a Framework for Data Literacy - Competence Framework and Research Report. Zenodo. doi.org/10.5281/zenodo.3946067
[4] Sebastian B. C. Lehmann, Franziska Altemeier, and Düvel Nina. Nachhaltige Wissenschaft mit Forschungsdatenmanagement - Eine Einführung für Betreuende von QualifizierungsarbeitenHsH. doi.org/10.25625/EKEEFB
[5] Einstein your data. Retrieved April 29, 2026 from www.uni-vechta.de/bibliothek/forschungsdatenmanagement/einstein-your-data

Abstract:
Research Data Management (RDM) is gaining increasing importance [1, 2], yet Data Stewards and research consortia still face a central challenge: information on existing RDM solutions is often fragmented, difficult to identify, and not easily reusable for field-specific needs [1]. As a result, parallel solutions are developed repeatedly, consuming resources and often proving difficult to sustain beyond the lifetime of individual consortia.

To address this, FAIRspace Cologne is being developed as a local platform for Data Stewards, researchers, and research consortia in Cologne and beyond. RDM experts are invited to share their knowledge on this platform, bringing together existing RDM tools, practical guidance, training materials, tutorials, and community news in one place. As a collaborative hub for Data Stewards it will support the reuse and adaptation of proven solutions; for researchers, it will
provide guidance structured around concrete research tasks and the data lifecycle, so that support can be found when it is needed.

The platform is intended to make existing support more visible, accessible, and actionable, while linking local services with broader German RDM activities and reusable community resources. In this way, it aims to strengthen awareness of RDM as part of everyday research practice and lower the threshold for engagement.

In addition, FAIRdata Cologne represents one of the RDM solutions that can be jointly used by research consortia in Cologne and adapted to their individual needs. FAIRdata Cologne is a Dataverse-based [3] metadata catalogue. It can support large collaborative research groups, such as collaborative research centres (CRCs), in creating project-specific spaces, adapting metadata schemas to local needs, and defining data-sharing processes within and beyond the
consortium.

Together, FAIRspace and FAIRdata Cologne illustrate how local infrastructure that is still under development can translate distributed RDM knowledge and tools into a practical, institutionally embedded support offer for Cologne and beyond.

Keywords: Data Stewardship, Knowledge Sharing, FAIR Data, Dataverse, RDM Support Services, Research Consortia

[1] Andrew M. Cox, Mary Anne Kennan, Liz Lyon, and Stephen Pinfield. 2017. Developments in research data management in academic libraries: Towards an understanding of research data service maturity. Journal of the Association for Information Science and Technology 68, 9 (2017), 2182–2200. doi.org/10.1002/asi.23781
[2] Richard Cheng Yong Ho, Suei Nee Wong, Patsy Chia, Chris Tang, and Magdeline Tao Tao Ng. 2026. Research data management services in academic libraries to support the research data life cycle: A systematic review. An Annual Review of Information Science and Technology (ARIST) paper. Journal of the Association for Information Science and Technology 77, 1 (2026), 272–300. doi.org/10.1002/asi.70008
[3] Gary King. 2007. An Introduction to the Dataverse Network as an Infrastructure for Data Sharing. Sociological Methods & Research 36, 2 (November 2007), 173–199. doi.org/10.1177/0049124107306660

Abstract:
The German Center for Mental Health (DZPG-Deutsches Zentrum für psychische Gesundheit) is a consortium of 30 research and clinical care institutions. Collaboration and networking at DZPG are of paramount importance, particularly regarding the creation of data infrastructures building the foundation for excellent research at the DZPG. Data Stewards being responsible for the curation of research data and their standardized ingest to these infrastructures are essential for sustainable use of data within the DZPG and beyond. To enable Data Stewards to operate these data infrastructures and advise researchers how to use them, a research data management training that is tailored to the specific needs of the
DZPG is key. To convey the fundamentals of research data management (RDM) and contribute to standardization of data management processes at the DZPG, an RDM certification course tailored to the specific needs of the DZPG is developed.

A course comprising five modules addressing central RDM topics, like data documentation, data management planning or archiving and preservation, will be provided. Moreover, to address specific needs of data stewards at DZPG, DZPG-specific parts will convey relevant knowledge on standard operating procedures in the different thematic areas and how to implement them. To this end, the present course concept allows for a comprehensive and needs-oriented competence acquisition that directly translates theoretical knowledge into relevant best practices. Hereby a blended learning approach has been developed, teaching RDM basics and applying the gained knowledge to develop tailored data curation workflows for data infrastructures in the DZPG and to benefit from crowd intelligence in collaborative and interactive formats like subject specific workshops or How to-Sessions.

To share and further expand the knowledge available at DZPG internally, there is a need for a continuing education program tailored to specific needs of the DZPG, which is offered as part of this certificate course.

Keywords: Data Stewardship, Research data management, Certificate course, blended learning

Abstract:
Capturing and sustaining engagement in FAIR data training and outreach across diverse audiences remains an ongoing challenge. In this contribution, we share our efforts to introduce playful elements into training and outreach activities. Euro-BioImaging is a distributed European research infrastructure providing open access to advanced biological and biomedical imaging technologies [1], where FAIR data training is essential to support reproducibility and data reuse. Our FAIR Data Services team develops and delivers training to support imaging facilities and researchers in implementing FAIR practices.

This contribution explores gamification as a complementary approach to conventional training formats, using interactive elements to make core concepts more approachable and memorable. As one example, the FAIR Data Crossword [2] is designed as a playful learning tool to familiarize participants with key terminology and concepts through guided clues and collaborative problem-solving. It can also serve to reinforce and assess learning in a low-stakes environment. We will further reflect on additional gamified elements being explored within training activities, showing how such approaches can support different learning styles and settings, both online and on-site to spark discussion and peer interaction during workshops.

Keywords: FAIR, data stewardship, gamification, crossword, bioimaging

[1] Retrieved April 30, 2026 from www.eurobioimaging.eu
[2] Isabel Kemmer and Euro-BioImaging. 2025. Crossword puzzle: FAIR BioImage Data. doi.org/10.5281/zenodo.17798356

Abstract:
The pilot project “Rent a Data Steward” [1], launched in 2023 by the Thuringian Competence Network for Research Data Management, was designed to support research groups at Thuringian universities and universities of applied sciences in developing practical solutions to their research data management (RDM) challenges. Rather than providing standardized guidance, the project adopted a collaborative and consultative approach: data stewards acted as sparring partners who helped researchers identify needs, reflect on workflows, and explore best practices tailored to their specific contexts.

As the project approaches its conclusion at the end of 2026, this contribution reflects on its development, achievements, and limitations to evaluate whether this approach can be considered a successful and sustainable support model. Based on our practical experiences across Thuringian higher education institutions, we analyze the conditions that facilitated or hindered implementation. Particular attention is paid to the importance of engagement and commitment of the involved parties, the importance of available technical and organizational support structures, and the benefits of group-centered training formats.

The model proved especially valuable where research groups were able to articulate concrete needs and where institutional structures enabled follow-up and implementation. At the same time, limited on-campus presence, varying levels of RDM knowledge, and resource constraints restricted the impact of the service.

Based on these experiences and with alternative RDM support models implemented in the German RDM community in mind, we outline possible future scenarios for cross-institutional data stewardship. Such a model may offer a viable way to provide domain-agnostic data stewardship for smaller-sized institutions that cannot establish such roles locally, while also complementing project-based or discipline-specific data stewards through coordination and flexible support.

Keywords: Research Data Management, Data Stewardship, Research Support Services, Cross-Institutional Support

[1] Stefan Kirsch, Steve Göring, Nadine Neute, Kevin Lang, Jessica Rex, and Roman Gerlach. 2024. Data Stewards in Thüringen – Konzeptpapier. Zenodo. doi.org/10.5281/zenodo.12563328
 

Abstract:
This poster discusses how data stewards in humanities can focus on building skills and navigate RDM infrastructure to achieve compliance with RDM principles.

Ten years after Wilkinson et al. (2016) defined the FAIR principles, RDM in linguistics is shaped by FAIR challenges, but also by data that cannot be made anonymous (speech recordings, video of face-to-face interaction), data that may fall under CARE principles (Carroll et al. 2020), and many sub-traditions that rely on many different methods. This poster presents the work of a research support project within the CRC 1252 "Prominence in Language" at the University
of Cologne and reflects on what FAIR compliance concretely looks like in this context.

Organizational factors — clarity of roles, needs, and RDM acceptance — play an important role [1–3]. We respond to such challenges by focusing on capacity building rather than checking whether researchers adhere to data policy. We focus on increasing transferable skills and on relying on low-threshold infrastructure. We are supported by the Data Center for the Humanities (DCH), the C3RDM competence center, and NFDI Text+, of which DCH is a member.

We have four focus areas. 1) Hosting tools for e.g. general-purpose file-sharing and specific annotation and corpus analyses. 2) Supporting data processing for 20 projects generating data ranging from EEG, eye tracking, and articulography to corpus annotations and multimodal interaction recordings. 3) Statistical consulting and teaching open and publicly accessible workshops (covering R, Python, Bayesian modelling, reproducibility). We also organize an open MediaPipe Special Interest Group, which helps exchange experiences with multimodal analysis and connects researchers. 4) Maintaining an archiving pipeline that supports data publication via the Language Archive Cologne and Zenodo. A high participation rate is achieved through a simple design (using Excel sheets) and specific metadata contact persons. Maintenance of this infrastructure is shared with the DCH.

Keywords: RDM in Humanities, Collaborative Research Projects, Organizational Factors

[1] Ortrun Brand and Jens Dierkes. 2020. Failures and major issues. Bausteine Forschungsdatenmanagement 2 (November 2020), 89–96. doi.org/10.17192/bfdm.2020.2.8102
[2] Claudia Engelhardt and Harald Kusch. 2021. 5.3 Kollaboratives Arbeiten mit Daten. In Praxishandbuch Forschungsdatenmanagement, Markus Putnings, Heike Neuroth and Janna Neumann (eds.). De Gruyter Saur, 451–476. Retrieved April 30, 2026 from www.degruyterbrill.com/document/doi/10.1515/9783110657807-025/html
[3] Silke Schwandt. 2019. Forschungsdatenmanagement in Sonderforschungsbereichen: Best Practices. Bausteine Forschungsdatenmanagement 2 (September 2019), 22–30. doi.org/10.17192/bfdm.2019.2.8084

Abstract:
Scaling data stewardship requires training approaches that are both sustainable and closely aligned with real research infrastructures. Within NFDI4Health, we are developing a coordinated training framework that integrates research data management (RDM) training directly with the consortium’s services and expertise.

Our approach combines three RDM training packages: a knowledge base for self-learning inspired by RDMkit [1], a synchronous, regular online training programme, and a summer school, structured through a shared roadmap/workflow from needs identification to evaluation. A key approach is the use of NFDI4Health FAIR enabling services within training: topics such as Data management planning, study hub, metadata standards, federated analysis, or anonymisation are taught through concrete tools and workflows developed in the consortium [2].

Each service is represented by dedicated data stewards who contribute domain-specific expertise to training content and delivery. This distributed model enables authentic, practice-oriented learning and supports the reuse of training materials across contexts. Moreover, through iterative processes and close collaboration, data stewards benefit from mutual learning and knowledge exchange.

However, this approach also raises organisational challenges. While data stewards act as trainers, developers, and coordinators, their contributions depend on allocated person-months and competing responsibilities across services. Ensuring sustained engagement and balanced workload distribution remains a key constraint for scaling training efforts.

In this poster we present design principles, benefits, and challenges of this approach, and discuss how integrating infrastructure, expertise, and training can support the professionalisation and growth of data stewardship. We also report experiences from collaborative activities within the Biomedical Interest Group and RDMTraining4NFDI.

Keywords: Training, knowledge base, RDM, biomedical

[1] Pinar Alper, Flora D’Anna, Bert Droesbeke, et al. 2025. RDMkit: A research data management toolkit for life sciences. Patterns, 6(9), 101345 doi.org/10.1016/j.patter.2025.101345
[2] NFDI4Health Service. Retrieved April 30, 2026 from: www.nfdi4health.de/en/service.html

Abstract:
Collaborating on metadata, in addition to code and data is important for research institutes and consortia. In this poster, we will present the metadata system which we are developing for tracking research information. It consists of a server-side metadata store with an API (Dump Things Service [1]) and a web UI (shacl-vue [2]). Both the API and the forms displayed in the UI are generated from flexibly defined data models (our current data model, research information [3], is heavily inspired by the Provenance Ontology [4]) and controlled via configuration files. 
Metadata records are organized in collections, which, together with access tokens (or external systems), can be used to control access scopes. This allows hosting a “metadata pooling service” which can be used to collaboratively source information about data collections and other research activities, which can later be queried and reused. We tend to deploy this this metadata system alongside our preferred solution for working with code and data: Forgejo [5] (a “self-hosted lightweight software forge”) with added git-annex support for large file storage (forgejo-aneksajo, [6,7]). Together, this self-hosted stack can go a long way in supporting a single- or a multi-site research endeavor.

Keywords: metadata, self-hosted

[1] orinoco. dump-things-server. Psychoinformatics Hub: Powered by Forgejo-aneksajo. Retrieved April 30, 2026 from hub.psychoinformatics.de/orinoco/dump-things-server
[2] orinoco. shacl-vue. Psychoinformatics Hub: Powered by Forgejo-aneksajo. Retrieved April 30, 2026 from hub.psychoinformatics.de/orinoco/shacl-vue
[3] Research information - DataLad Concepts. Retrieved April 30, 2026 from concepts.datalad.org/s/demo-research-information/unreleased/
[4] Khalid Belhajjame, James Cheney, David Corsar, Daniel Garijo, Stian Soiland-Reyes, Stephan Zednik, and Jun Zhao. 2013. PROV-O: The PROV Ontology. Retrieved from www.w3.org/TR/prov-o/
[5] Forgejo – Beyond coding. We forge. Retrieved April 30, 2026 from forgejo.org
[6] forgejo-aneksajo. forgejo-aneksajo. Codeberg.org. Retrieved April 30, 2026 from codeberg.org/forgejo-aneksajo/forgejo-aneksajo
[7] Michael Hanke. 2025. Collaborative infrastructure for a lab: Forgejo. To be distributed…Retrieved April 30, 2026 from blog.datalad.org/posts/lab-infra-forgejo/

Abstract:

Data Management Plans (DMPs) are considered an essential component of research data infrastructure, especially in facilitating cooperation among stakeholders involved in Research Data Management (RDM). As a starting point for RDM, DMPs are commonly addressed when providing generic RDM knowledge through workshops, webinars, training materials, or
knowledge platforms. DMP tools such as the Research Data Manager Organiser (RDMO) support the planning, implementation, and administration of RDM.
Data stewards play an increasingly important role in supporting researchers with DMP creation, yet often face challenges in demonstrating their added value and in motivating communities to engage with them proactively. To address these challenges, the DMP4NFDI basic service for the National Research Data Infrastructure (NFDI) developed a modular Train-the-Trainer Concept on Data Management Plans and RDMO [4]. The concept is based on the modular structure and didactic principles tested in the Train-the-Trainer Concept on Research Data Management [5], incorporating new learning objectives that target the use of
RDMO and offer a more in-depth understanding of DMPs, especially for those adapting templates or supporting others.
The poster demonstrates how the modular training formats, didactic approach, and strategic measures of the DMP4NFDI concept address current challenges in DMP and RDMO support. It further presents first implementation outcomes and reflects on the concept's potential to strengthen data stewardship and foster sustainable knowledge transfer across RDM communities.

Keywords: DMP4NFDI, Train-the-Trainer, Data Management Plan, machine-actionable DMPs, RDMO

References:

[1] Katja Diederichs, Celia Krause, Marina Lemaire, Marco Reidelbach, and Jürgen Windeck. 2024. A Vision for Data Management Plans in the NFDI. doi.org/10.5281/ZENODO.10570653
[2] Tomasz Miksa, Peter Neish, Andreas Rauber, and Paul Walk. 2018. 304.1 Defining requirements for machine-actionable data management plans. OSF. doi.org/10.17605/OSF.IO/CGP86
[3] Martin Spenger and Gerald Jagusch. 2025. 10 years of RDMO – The Research Data Management Organiser as Software and Community. September 09, 2025. Zenodo. doi.org/10.5281/ZENODO.17084290
[4] Marisabel Gonzalez Ocanto, Katja Diederichs, Sabine Schönau, David Wallace, and Jürgen Windeck. 2025. DMP4NFDI Train-the-Trainer Concept on Data Management Plans and RDMO. doi.org/10.5281/ZENODO.15771036
[5] Katarzyna Biernacka, Petra Buchholz, Sarah Ann Danker, Dominika Dolzycka, Claudia Engelhardt, Kerstin Helbig, Juliane Jacob, Janna Neumann, Carolin Odebrecht, Britta Petersen, Benjamin Slowig, Ute Trautwein-Bruns, Cord Wiljes, and Ulrike Wuttke. 2021. Train-the-Trainer-Konzept zum Thema Forschungsdatenmanagement. doi.org/10.5281/ZENODO.5773203

When: Tuesday, 29 Sept. 2026 from 14:00 to 16:00

Venue: Seminar building No. 106

Description:

This workshop tackles a familiar issue in research data management: Data Management Plans have the potential to make research more efficient, transparent, and reusable—yet they are still too often treated as a box-tickingexercise. Starting from the question “What’s in it for me?”, we explore how DMPs can become more useful, usable, and appealing from a researcher’s perspective.

Participants will share their experiences—what works when convincing researchers, and where it fails—and identify common barriers such as low perceived value, usability challenges of DMP tools, or competition withAI tools. A short hands-on session with DMP4NFDI and RDMO provides practical insight into current solutions.

In interactive group work, we will develop concrete ideas to improve DMP adoption, from better templates and wording to more effective outreach and service design. Participants leave with practical arguments, peer-testedapproaches, and actionable ideas to make DMPs more attractive in their own context.

The workshop is organised by DMP4NFDI and Base4NFDI and targets RDM professionals and data stewards. It will be held as a satellite event of the Data Stewardship goes Germany (DSgG) Community Meeting 2026 at the Universityof Cologne.

Please register here:  https://eveeno.com/dmps-researchers-actually-want

Max Participants: 20

Language: English

Duration: 120min