Conference Agenda

This poster examines whether the quality of Data Management Plans (DMPs) in Croatia’s national repository has improved since the Croatian Science Foundation (HRZZ) introduced a DMP mandate in 2022. The study compares forty HRZZ DMP templates, the first twenty and the most recent twenty submitted between 2022 and 2025, using a three-point scale from “does not meet criteria” to “excellent.” The results provide the first insights from a Croatian perspective on how researchers have responded to the HRZZ requirement. Expected outcomes include improved quality of DMPs, greater researcher awareness, stronger compliance with HRZZ policies, and a step forward in national research data management practices.

The World Data System serves a membership of trusted data repositories and related organizations. The WDS mission is to enhance the capabilities, impact, and sustainability of our member data repositories and data services.

In 2025, the WDS launched a study to identify value-added benefits of data repositories to the international research ecosystem. The ultimate goal of this Data Repository Investment Value & Relevance (DRIVR) project has been to demonstrate the value of repositories in serving the needs of researchers, funders, policy-makers and other stakeholders. The seamlessness of well-managed repository services leaves the impression that the underlying effort is minimal or automated, although in reality ongoing maintenance and operations require significant resources. Insufficient funding leads to stagnation, leaving the repository holdings vulnerable; thus continual investment by funders is necessary to sustain and enhance repository services.

Using the Delphi study method, the first round involves brainstorming benefits and services that repositories provide, allowing participants to provide additional inputs to a pre-generated list based upon existing literature and expertise. Ultimately, 37 value-added benefits were identified. Subsequent rounds narrowed and ranked these benefits towards an agreed upon ordering. Here, we share results with demographics and narratives for high ranking benefits.

The Neuroscience pilot of the EU-funded SciLake project seeks to tackle the complexities of fragmented and disorganized open research data. This initiative has united partners with expertise in knowledge management with curators from EBRAINS (RRID: SCR_019260), an open research infrastructure focused on brain-related studies. The innovative Scientific Knowledge Graph (SKG) (https://bip.imsi.athenarc.gr/search/neuroscience-pilot) created through this pilot serves as a cohesive framework for data integration while enabling the use of value-added services provided by SciLake. The developed SKG combines curated datasets from EBRAINS with extensive neuroscience research products from the OpenAIRE neuroscience gateway (https://neuroscience.openaire.eu/). The SKG incorporates controlled terms from openMINDS (RRID: SCR_023173) as nodes and utilizes an entity recognition model to classify and map these terms in relevant texts, enriching the connections among research products. Additionally, the SciLake services deploy language models to extract mentions of research artifacts (RAs) and enhance the interlinking of research products. Fully integrated with impact-driven discovery and reproducibility tools and leveraging the analytical capabilities of SciLake, the SKG serves as a powerful resource for contextualizing curation efforts and exploring research trends, ultimately optimizing curation processes in the neuroscientific landscape.

We will compare the AI-generated assessments with those from data support professionals. Comparison of similarities and differences gives an insight into which questions and themes the attention was paid to. Also, we analyse how the reliability and usefulness of AI feedback vary across assessment criteria. This allows us to identify where and why AI aligns or misaligns with expert judgment, informing both tool refinement and context-aware use of AI in research support.

This poster examines how data management plans (DMPs) submitted to the Research Council of Finland by Tampere University researchers in 2021–2022 are realized in practice, particularly regarding data sharing. While researchers must follow FAIR principles and submit DMPs early in their projects, the actual outcomes of data sharing often remain unclear. By analyzing initial DMPs and conducting a follow-up survey, the study compares researchers’ intentions with actual practices, exploring reasons for any discrepancies. The findings highlight the need for improved DMP tools that support tracking and verification, and they inform the development of better follow-up processes and support services.

The evolution of research data management (RDM) practices, driven by policy changes, technological advances, and the growing emphasis on FAIR (Findable, Accessible, Interoperable, Reusable) data principles, has created significant skills gaps within the community of RDM professionals.

The FAIR's FAIR Impact Award Scheme, coordinated by the Digital Curation Centre at the University of Edinburgh as part of the international FAIR Impact consortium project, represents an innovative response to these challenges, offering a structured mentorship programme that pairs experienced practitioners with emerging professionals to foster knowledge transfer, skill development, and community building within the RDM ecosystem.

We present an approach that places the transformation of Data Management Plans (DMPs) from static documents into dynamic, interoperable entities at the core of our integration work. We introduce DMPsee, a new, event-driven notification service that enables this transformation. Changes made in DMPonline, such as creation, updates, or deletion, become secure, timestamped events that can trigger actions in connected systems like RSpace. This allows DMPs to actively coordinate research workflows rather than serving only as compliance artifacts.

This approach represents a significant methodological innovation in research infrastructure design. Instead of relying on heavy, bespoke integrations, DMPsee uses lightweight webhooks and standardized events, improving resilience and scalability. Our work demonstrates a new model for delivering curation services at institutional and national levels, enabling platforms to plug into broader data ecosystems while also showing how digital infrastructures can be both technically robust and ecologically sustainable.

FAIRway is a collaborative initiative by INESC TEC, CIIMAR, and BIOPOLIS-InBIO to strengthen Research Data Management and Open Data practices under the Portuguese Programme for Open Science and Research Data (PNCADAI). The poster presents consolidated results, including a roadmap for Data Management Plans, mapping of researchers’ practices, training across disciplines, and pilot use of Electronic Laboratory Notebooks. Together, these actions demonstrate joint institutional efforts driving the transition towards FAIR data.

We are developing ethics recommendations to ensure that sensitive research data are handled ethically, responsibly, transparently and in line with FAIR and CARE data principles throughout the research data lifecycle and in a manner that promotes trust, reproducibility, and innovation.

The DDI Alliance is a global consortium stewarding a suite of open metadata standards designed for describing data across its full lifecycle—from study design and collection to preservation, sharing, and reuse. With deep roots in the social, behavioral, economic, and health sciences, DDI metadata standards are human-readable and machine-actionable, domain-sensitive, and already widely implemented in data archives, libraries, and national statistical systems.

This talk will showcase:

- How DDI standards directly support FAIR data principles (Findable, Accessible, Interoperable, Reusable);

- The value of structured metadata for interoperability, transparency, and data integrity, especially where institutional trust or longevity is uncertain;

- How researchers and institutions can use DDI to safeguard their data in restrictive or resource-constrained contexts;

- The organizational benefits of joining the DDI Alliance, including access to a global metadata community, implementation tools, and governance over future standards development.

The introduction of FAIR, CARE, and TRUST principles into research data management has significantly influenced how digital research objects are curated and preserved. Yet many digital repositories still lack clear, actionable frameworks to guide long-term preservation decisions—particularly in the face of limited resources and increasing data complexity. The EOSC EDEN project (2025–2027), funded by the EU Horizon programme, aims to address this gap by developing a comprehensive framework for FAIR-enabling digital preservation, tailored to the needs of diverse communities and disciplines.

The poster will present ongoing work and expected outcomes of the project, such as a framework for identifying data suitable for long-term preservation. This framework evaluates digital objects across three quality dimensions—contextual, technical, and metadata quality—while aligning with the expectations of designated communities and the capabilities of digital archives. EOSC EDEN will introduce a re-appraisal model that incorporates changing community expectations, archive capabilities, and usage metrics. The project will also deliver a suite of user-centric operational services.

Finally, EOSC EDEN will establish an expert curation network and host workshops and bootcamps to foster peer exchange and community building, strengthening the sustainability, inclusivity, and effectiveness of digital curation practices across Europe and beyond.

The volume of data generated by research is growing at an exponential rate yet much of this data is unusable due to the lack of expertise, tools, and resources for effective data management. Data Stewards are the key to bridging the gap between data generation and reuse, as they have a fundamental role that ensures the quality, accuracy, accessibility and longevity of data across the entire data lifecycle. We place great value in data, but the current investment in infrastructure to promote engagement and support data excellence is lacking, and best practice like FAIR cannot be implemented without investing in data stewards. This is where CaSDaR comes in; a UKRI funded Network+ started in April 2025 with a goal to establish a diverse, inclusive, self-sustaining community of Data Stewards and to create a model for data steward support systems within research intensive institutions, thereby clarifying their role and integration within the research data lifecycle. This poster will provide a high-level overview of CaSDaRs activities, roadmap and deliverables, and explain how you can get involved!

Support strategies, such as training and resource provision, are essential in the process of improving the uptake of practices aligned with research excellence. The Physical Sciences Data Infrastructure (PSDI), an initiative developed in response to a disciplinary need, has developed resources and infrastructure to promote a cultural shift towards implementing sustainable data practices. PSDI has sought input from key stakeholders and worked with different partners to identify community needs for in-person and self-paced training, guidance resources, webinars, and workshops that provide the opportunity for peer discussion and learning. PSDI continues to work with the data curation community to identify future directions for our resource development and community engagement.

IIIF is a way to standardize the delivery of images and audio/visual files from servers to different environments on the Web where they can then be viewed and interacted with in many ways.

This poster introduces Anatomy of a Fatberg, a digital artwork that reimagines the fatberg, a congealed mass of human waste, as a provocative metaphor for contemporary Artificial Intelligence systems. Built from a blend of real time interaction and vast historical datasets sourced from the Statistical Office of the Republic of Serbia, the work critiques how data accumulation, under the guise of intelligence, increasingly mirrors processes of waste production, opacity, and self reinforcing extraction.

The Fatberg feeds on heterogeneous data from toxins in the Danube to telecommunications records, divorce rates, and economic metrics raising questions about what is curated, preserved, discarded, or exploited in digital systems. In this speculative metaphor, the AI Fatberg becomes the only beneficiary in a closed system of surveillance, over collection, and ecological degradation.

By situating the artwork in relation to the themes of AI, austerity, and authoritarianism, this contribution foregrounds the risks of opaque, extractive AI systems that consume public data without community control or transparency, the consequences of uncurated or miscurated data garbage data collected under austerity driven or authoritarian regimes, often without consent or clear value, and the role of artists as digital curators, intervening in data flows through subversion, metaphor, and embodied critique.

Organisational identity is essential for making research outputs easier to find, cite, and reuse. However, inconsistent metadata continues to be a problem across scholarly communication systems. Without reliable metadata about research organisations, outputs risk being misattributed, overlooked, or undervalued. OpenOrgs, a service in the OpenAIRE ecosystem, addresses this issue by disambiguating, structuring, and improving metadata about research organisations. While automation can scale detection, only a community of expert curators can ensure metadata truly reflects the complex and evolving research landscape.

At the heart of OpenOrgs is a growing community of curators - over one hundred experts from 60 countries - from national experts to the central Curation Board. To date, they have edited and approved more than 102.000 institutional records. Together, they maintain and improve the quality of organisational metadata. Curators work includes resolving conflicts, merging duplicate records, enriching metadata, and setting up parent–child hierarchies that reflect institutional structures. This guarantees that records are maintained accurately, remain up-to-date, and meaningful within the wider research system.

The PATTERN Horizon Europe project develops training activities for researchers to strengthen their transferable skills in Open and Responsible Research and Innovation. A focus on FAIR Research Data Management (RDM) fosters transparency, reusability, and impact in research. In the poster we showcase the reusable resources that have been created, highlight a collaboration between Croatia and The Netherlands, and recommend future directions.

We will actively seek feedback from poster viewers on our plans for a sustainable continuation of our collaborative work to facilitate the reuse and adaption of the PATTERN FAIR RDM training materials.

FIDELIS is committed to provide training and support for the entire European repository landscape ( Including Horizon Europe associated countries), allowing all to improve their relevant skills and potentially onboard onto the FIDELIS Network. In these efforts, we recognise there are parts of the community where underrepresentation in previous training and support activities, lower certification rates, and limited access to resources and development can be observed. Taking into account these factors, the FIDELIS training and support strategy includes specific attention to balanced representation and specific actions we will take to ensure this. FIDELIS aims to 1) actively facilitate increased representation in our training and support programmes of countries that have previously been less well represented, so their repositories can be strengthened, 2) actively facilitate repository networking throughout Europe and allow onboarding onto the FIDELIS Network by all countries, and 3) improve understanding of the unique challenges, needs, and questions that are prevalent in these areas and may be different from previously identified topics in the repository landscape. We aim to facilitate community input on our strategy plans and to set up meaningful connections to better involve our intended audience through an initial online meeting, which people will be able to sign up for at the conference.

Longitudinal datasets that serve an entire domain present a number of specific challenges to data citation and DOI assignment, particularly when they contain sensitive data. How do we approach DOI assignment? How much of the responsibility of accurate citation should be taken on by a data curation unit? How much by researchers?

As technology advances open up new ways of discovering and re-using data, researchers need to be equipped with the skills and knowledge to understand how to adapt these new methodologies to their own scientific discipline, and meet journal and funder data policy requirements. While compliance can help layout these requirements, education is a key tool in showcasing the benefits, opportunities, and importance of good data management throughout the research project lifecycle. Here, we explore the lessons learnt in building a research data management (RDM) training community within our organization, and discuss how interactive engagement with researchers from different career stages can lead to improvements in creating open and FAIR data in the environmental sciences.

While many Japanese universities have adopted research data policies, providing enough supporting research data management (RDM) remains challenging, particularly for smaller institutions with limited staff and resources. The Research Data Management and Utilization Working Group (WG) was formed in September 2024 to improve RDM practices at universities in Japan's Kyushu-Okinawa region.

The WG serves as a platform for collaboration and knowledge sharing among its members. It is supported by the Research Data Management Start-up Support Project, sponsored by the National Institute of Informatics. Kyushu University is the regional coordinator of this project.

The WG also operate under the Kyushu-Okinawa Open University (KOOU), a consortium of 11 national universities, aims to enhance regional research capacity through researcher and student development, shared research infrastructure, RDM, and development of research support personnel in Kyushu and Okinawa area. RDM is regarded as an important element in enhancing the research reputation of universities.

KOOU and the WG plan to hold a joint workshop in December 2025 and aim to expand their outreach to non-member universities. The WG will contribute to promote collaboration and best practices in RDM across the region.

At universities, several communities co-exist and often turn into silos, specialised communities working independently and isolated within the organisation.

The Swedish University of Agricultural Sciences (SLU) generates data in diverse areas, including e.g. wildlife, soil use, water, food, forestry, agriculture and aquaculture, through research and environmental monitoring and assessment activities. The latter are often initiated and funded by external official authorities, with specific requirements that may affect data management.

The scientific activities take place within silos. The same goes for the data curation community, where various organisational units have tended to form silos as well.

- - The scientific silos are in many aspects unique, needing specialised and customised solutions. Their data sovereignty is a vital part of academic freedom and thus must be protected.

- - The support silos hold the general know-how regarding best practice in RDM. The data curation community need general and standardised solutions to work within our budget while representing researchers as well as the institution and the public.

To enable support in the face of the challenges and to create and maintain trust from the scientific silos, we need to work towards cultural change. Collaboration between silos, flexibility, communication and transparency are essential in this development.

In 2025 AI has become a world-wide topic with new AI-solutions to existing problems being presented on a regular basis. On top of that the implementation of the world’s first law governing AI, the AI Act, has started. The cherry on top? You still have the GDPR, national law and other regulations to take into consideration.

For the Swedish University of Agricultural Sciences (SLU) questions regarding maps, drones and agricultural data are important. Sweden also has the principle of transparency of public work that goes further that open science/data. The principle is applicable for authorities and since SLU is an authority this leads to questions regarding how data can be requested, used in AI and later potentially be used in a way that is harmful.

Another topic is how AI can be used to improve SLU:s research. This leads us to questions on how to conduct research with the help of AI in a responsible way so that the standard for data protection and security is upheld.

This contribution will bring a legal aspect to the question on AI that focuses on how sustainable solutions can be brought forth.

Croatian Research Information System (CroRIS) is the central Croatian digital infrastructure which aims to collect, curate, and provide structure and access to metadata on research activities. CroRIS superadministrators (at Ruđer Bošković Institute and University of Zagreb University Computing Centre - Srce) manage the entire system, work on the system's construction and further development, have the ability to edit all the entries, add new and manage existing authority files and are directly responsible for editing and verifying the accuracy of the orphaned entries. Considering a large amount of metadata is linked to some Croatian research and higher education institution, access to data management was granted to institutional administrators. Institutional module editors are able to administer those CroRIS entries which pertain to their institution. This division of work enables prompt data verification and accelerates the process. CroRIS thus created a community of data curators who collaborate together to serve a common goal and as such serves as an excellent example of a successful data curation partnership.

Transformation of biomedical research through meticulous development and expansion of resources for curation, extraction and translation for clinical interpretation is a fundamental component of clinical genetic practices. In clinical contexts, curation of molecular and clinical entities from available sources provides real-time data collection, interpretation and reporting. There is an increasing need to expand the current practices to include population descriptor entities. However, populations can be described in various ways including social, biological and geographical constructions. Furthermore, these constructs can differ across both place and time, resulting in multiple dynamic population entity types. Here, we aim to develop an event annotated corpora for the task of extracting population descriptors. In our approach, we include identification of population target types and guidelines for population entity annotation. We extend the approach to biomedical information extraction to include all types of population descriptors. In development of this corpus, we manually annotated biomedical literature using a combination of structured and unstructured representation for entity extraction across multiple trained annotators. Our event extraction approach was applied to a variety of population descriptor extraction targets, including clinical, demographic, geographic and social. With a focus on Low- and Middle-Income Countries (LMICs), we selected 100 biomedical research papers from biomedical journals published in LMIC countries marking references to population descriptor entities and domain-relevant processes. Trained annotators evaluated relevant entity types for annotation and developed a set of detailed guidelines for annotation in text. Secondly, experts created structured event annotation in both abstracts and published manuscripts. The resulting corpora is intended to serve as a reference for FAIR training and evaluation methods for population entity mention detection in biomedical science from LMICs.

Software Heritage’s mission is to collect, preserve, and share all software that is publicly available in source code form. It’s the largest archive of software source code ever assembled. The archive holds 22 billion unique source files from over 340 million projects. Software Heritage is a non-profit multi-stakeholder initiative launched by Inria in partnership with UNESCO, hosted by the Inria Foundation, and with a growing number of partners.

The poster highlights three strategic pillars: technical durability (through open standards and a network of mirrors, through intrinsic identifiers), organizational sustainability, and governance.

At TU Wien, the Center for Research Data Management (CRDM) has developed and successfully operates institutional tools and services such as DAMAP (the Data Management Plan tool) and the TU Wien Research Data Repository, based on InvenioRDM. Despite these established infrastructures, there remains a clear need to strengthen data stewardship capacity across the university. With over 26,000 students and 6,000 employees in eight faculties, TU Wien’s research landscape is broad and diverse, making domain-specific data stewardship a key component for sustainable digital curation.

Beginning in October 2025, CRDM will launch a pilot phase introducing data stewards across selected faculties and institutes. The Faculty of Architecture and Planning and the Faculty of Mathematics and Geoinformation (specifically Geo Department) will participate in this initial phase. Six data stewards, each bringing domain-specific expertise, will undergo a structured training programme that combines theoretical foundations with hands-on practice.

Our poster will present the modular training framework we are developing, including implementation timelines and the rationale behind our design choices. The modules will cover:

1. Fundamentals of Research Data Management (RDM)

2. RDM Infrastructures and Services

3. FAIR (Meta)Data Principles

4. Legal and Ethical Frameworks

5. Research Data Quality

6. Introduction to AI/ML

7. Supplementary (optional, eg, command line systems, programming basics, etc.)

Our approach stands out by bringing together researchers’ disciplinary perspectives and the university’s existing data management infrastructure, ensuring that stewards can translate RDM principles into real research workflows.

By sharing our framework at IDCC26, we aim to gather community feedback on our training approach, discuss scalability to other TU Wien faculties, and contribute to the broader dialogue on building sustainable, domain-specific data stewardship models. We plan to present outcomes and lessons learned from the pilot at IDCC27.

This poster showcases the FAIR journey of the INFRA-ART Spectral Library, an open-access repository supporting heritage science and related fields. It highlights how interoperability standards and trustworthy governance transformed a standalone database into an EOSC-aligned service. The poster highlights both technical workflows and user impact, showing how FAIR and TRUST principles enable greater confidence, visibility, and reuse of interdisciplinary datasets.

This document presents the development proposal for ArtInsight AI, an innovative system designed to assist art curators in the creation of professional critiques. Addressing the persistent challenges of time constraints, inconsistent quality, and laborious research in curatorial practice, this project leverages generative AI to streamline the writing process. The following proposal details the project's core motivation, technical methodology, system architecture, and its anticipated impact on enhancing the efficiency and academic rigor of the curatorial field.

This lighting talk showcases how curators working with new technologies can adapt to uncertain conditions, such as limited resources or the complexity of the digital systems themselves. The talk is a reflection on my experience as a curatorial intern for [ARTIFICIAL] TERRITORIES, an interactive transmedia exhibition held in 2025 at Das LOT in Austria. Organised by the collective ECHOLOT, the exhibition brought together 23 artists and 12 multimedia artworks that engaged with AI as both a creative tool and a critical subject.

This talk highlights how transmedia methods helped public engagement and respond creatively to real-world limitations, and also how participatory design, storytelling, and digital technology can allow curators to be adaptable and agile when collaborating with novel technologies. The project demonstrates that even within uncertain systems, curating can be an act of connection and imagination.

The digitization and preservation of heritage materials involve a complex curation process. Today, artificial intelligence tools are used to translate languages, read manuscripts, and generate metadata for heritage and fragile materials for discovery and accessibility. The Balme Library is the largest and most prestigious library in Ghana, housing valuable historical collections and playing a key role in preserving Ghana's and Africa's historical materials. The Digitization Project was launched in 2010 which aims to curate heritage materials for long -term preservation by reducing stress on the fragile originals. The Balme Library, Ghana's largest and most prestigious library, houses valuable historical collections. These collections include Folio, Furley, Africana Rare Materials, and old newspapers on microfilm that are of great significance to researchers and historians. The collection features documentary materials related to Ghana’s history, manuscripts, maps, pamphlets, theses, old books, and old newspapers and theses on microfilms. Curating of these collections helps connect the past, present, and future which is a large-scale project requires sustainable funding, adequate digital and artificial intelligence tools, curation skills, effective policy, and a collaborative effort to preserve our heritage.

This poster presents a comprehensive overview of the Grad Student's Data Survival Guide, detailing individual lessons and demonstrating how our curriculum aligns with established data curation frameworks, particularly the Data Curation Network's CURATE(D) model. We will provide interactive access to the Guide via tablet, enabling attendees to explore the content firsthand and offer feedback on both specific materials and overall pedagogical structure. We welcome input from curation experts on potential gaps in our coverage and opportunities for improvement.

In this poster, we will focus on sharing the growth and formalization of the Data Rescue Project. We will report on volunteer engagement, data captured, and resources we have created to support data rescue efforts. Additionally, we will reflect on experiences carrying out rapid-response coordination and the building of an involved and sustainable community infrastructure. While this project is based in the United States context, we hope our project pathway will be easily adaptable for other rapid response data curation projects.

This poster reports a mixed-method evaluation of the impact of TU Delft’s three-week, blended RDM101 course. Using feedback from every run, pre/post surveys from four runs, and 13 semi-structured interviews (11 alumni, 2 data stewards), we assess short- and long-term impact on data curation skills. Short-term outcomes are substantial: awareness of institutional RDM support rose from 45% to 98%; reliance on secure institutional storage increased markedly with 90% of learners expressing they changed their storage strategies after the course and 81% established new backup routines; 98% felt better equipped to start or improve a DMP, frequently citing the Data Flow Map (DFM); FAIR understanding shifted from naming the acronym to enacting practices (documentation, metadata, README files). Intentions to publish data/code increased from 76% to 95%, with choices moving toward trusted repositories. Course alumni reported sustained organisation, documentation, and reproducibility practices, reduced anxiety about compliance, and continued engagement with data stewards. Drawing on these findings, we outline educational design principles for resilient RDM training to inform the 2026 course update: (1) blend modular and discipline-specific learning pathways with personalised feedback; (2) prioritize actionable skills over definitions; (3) sustain evaluation and timely updates. 

This poster presents the CESSDA Trust Support programme, outlining the support routes, outcomes, challenges, and recent adaptations. Although the CESSDA Trust Support is rooted in the social sciences, the programme can be applied in other domains. We welcome feedback and ideas from a wider audience to refine the support programme, and to prepare for new challenges in digital curation and trustworthy repository practices.

Digital curation is being reshaped by a mix of accelerating AI uptake in research, public budget austerity, and a hardening political landscape. These forces intersect in ways that threatens the promises of open, equitable, sustainable and democratic research, paradoxically framing research data as both capital to protect and a public good to share This poster examines how FedOSC, Belgium’s Federal Open Science Cloud initiative aims to navigate these dynamics and strengthen resilient, democratic and transparent digital curation infrastructures.

Austerity creates resource constraints that may reduce the capacity of the Federal Scientific Institutions (FSIs) to conduct high-quality data management, sustain research data or services, guarantee access or maintain long-term preservation. At the same time, AI offers efficiency through automated metadata management or linked data, yet inducing risks related to transparency, bias, externalization, and long-term maintenance. Patterns of digital authoritarianism, such as global north biases, or corporate monopolies, pose challenges to data sovereignty, archival integrity and equitable knowledge access.

FedOSC proposes a strategic response to these points of pressure by enhancing or developing shared infrastructures and tools, aligning governance, policies and guidelines across FSIs. It promotes FAIR data practices, supports interoperable repository development, and provides training that emphasizes transparency, inclusivity, and accessibility. By coordinating efforts at a federal level and aligning with the European Science Cloud (EOSC), FedOSC reduces redundancy costs and strengthens resilience against technical, economic and political vulnerabilities. It commits federal research to the EOSC’s ambition of a truly functional open knowledge market in Europe and empowers FSIs to access EOSC services.

Through equitable prioritization strategies, responsible AI integration, robust governance and shared infrastructure, FedOSC ensures that Open Sciences values remain intact even under pressures, guaranteeing access to scientific knowledge, reframing the apparent tension between Open Science and knowledge security as an opportunity for mutually reinforcing dissemination and preservation strategies.

In response to the growing demand for professional research data support, Latvia has taken strategic steps to embed data stewardship within its academic institutions. Formation and development of the Latvian Data Steward Network, national initiative to strengthen Open Science and research data management, establishment of a national repository along with an associated website – core components of the project “Support for Practical Implementation of Open Science and Solutions for Research Data Sharing and Participation in the EU Open Science Cloud.”

Research Data Management (RDM) is essential throughout the entire research data lifecycle, encompassing data collection, processing, analysis, long-term storage, and sharing. A Data Management Plan (DMP) serves as a critical instrument to systematically organize RDM processes and ensure compliance with best practices. Increasingly, funding agencies require the submission of a DMP as part of project proposals to promote open science and enhance research reproducibility. However, variations in DMP requirements across funders are challenging and limit efficient and consistent reuse of DMP information across stakeholders, like data stewards, project office, funding agencies etc.

To address these issues, we reviewed DMP templates from funding agencies and compiled a generic DMP template for the IPK Gatersleben, in context with German National Research Data Infrastructure consortium FAIRagro. Our approach involved a comprehensive analysis and categorization of existing funder questionnaires to identify semantic overlaps and unique elements. The harmonized set of questions was then integrated into a template using RDMO (Research Data Management Organizer), an open source software platform designed to facilitate DMP creation.

Within RDMO, a mapping between questions and answers using attributes enables the generation of customized DMP views that satisfy individual funder requirements. This setup allows researchers to efficiently reuse their responses across different funding applications, streamlines maintenance of the questionnaire, and supports extensibility to incorporate new or updated funder requirements.

The poster will present our methodology for analyzing and harmonizing funder requirements, highlight areas of overlap and divergence, and share lessons learned during the template development process. By providing a flexible, maintainable, and extensible solution, we aim to reduce the administrative burden on researchers and support the goals of open science and research reproducibility.

Uedu (https://uedu.tw) is a generative AI–powered front-end platform designed to assist students in learning and reduce cognitive load, while giving educators new insights into students’ knowledge growth. Built as a trusted multimodal learning data lake, it integrates digital learning traces, wearable physiological data, and classroom environmental measurements. Generative AI supports semi-automated metadata creation, summarization, and planned cross-modal semantic linking to reduce manual curation burdens in resource-limited contexts.

A data sovereignty framework enables students, educators, and researchers to define access rights, licensing terms, and reuse conditions, supported by machine-readable policies. Aligned with FAIR principles, this approach promotes findability, accessibility, interoperability, and reusability of diverse educational datasets. Deployed in multiple university courses, Uedu has generated curated datasets for teaching practice research and cross-institutional collaboration, demonstrating its potential to operationalize data sovereignty in real educational settings while sustaining open scholarship and community trust.

This poster presents the work package 2 of the French funded project, FAIRwDDI, whose purpose is to create awareness and resources on FAIR (Findable, Accessible, Interoperable, Reusable) research data reuse and preservation.

The FAIR principles largely rely on metadata and metadata standards like the Data Documentation Initiative (DDI) for their implementation.

Specialists from CDSP France and CLOSER UK got together for two sprints and created training materials on the Data Documentation Initiative Standard.

We will showcase the bilingual training material created in the FAIRwDDI, that will be openly and freely made available for the data preservation community.

Slovenian research policy increasingly supports open science as the national legislation requires data sharing of mainly publicly funded research in line with FAIR principles. However, institutions still face challenges in tracking and supporting these practices. At the University of Primorska (UP), OA reporting depends on researcher self-assessments, which often lacks details regarding research data.

This study investigates data sharing practices in scientific articles published by UP researchers based on a sample of 120 papers. While most articles were openly accessible and 80% reported generating data, more than half did not include a data availability statement and only 10% of papers referenced datasets deposited in trusted repositories. The level of FAIRness of these datasets was evaluated by using three different FAIR assessment tools (F-UJI, FAIR Evaluator and FAIRshake), and results were compared to expert evaluation. Additionally, authors of papers in the sample that were not sharing data were contacted by two data stewards and offered support in preparing data for deposition.

Findings from this study will inform institutional policy development aimed at enhancing compliance with open science mandates and supporting automated monitoring of OA and FAIR practices.

The Research Data Alliance is pleased to announce that nominations for the 2026 edition of ‘The Sarah Jones Award for exceptional contribution to fostering collaboration in Open Science‘ are now open. This bi-annual award honours the memory of Sarah Jones, and her significant contributions to the Open Science community globally.

RDA is inviting the submission of nominations for individuals who have demonstrated an exceptional contribution to fostering collaboration in Open Science. This impact can be on an organisational, community or individual level. This award is open to candidates from across the globe and is not restricted to members of the RDA community. The deadline for receipt of nominations is 14th June 2026. This poster will highlight the award and the deadlines for submission of nominations and announcement of the winner.