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.

We present a suite of operational services (TRL 7-9) that leverage Artificial Intelligence to augment, not replace, human expertise. We have developed a prototype national catalog for French research data that integrates hybrid search capabilities with a suite of AI-driven tools for metadata enhancement and quality assessment. The catalog combines traditional faceted search with a multilingual semantic search engine, using bi-encoder models for efficient retrieval and cross-encoders for precise reranking. To tackle metadata inconsistency, we utilize right-sized, open-source LLMs like Mistral Small to align entities to controlled vocabularies (e.g., ROR) and generate standardized classifications (e.g. scientific disciplines). This approach minimizes computational costs and environmental impact while ensuring transparency by always distinguishing between original and AI-generated metadata. Acknowledging metadata can be of low quality, we have also built a novel curation analysis tool using a few-shot LLM to assess the semantic substance of descriptions. Our roadmap focuses on evolving these tools into a proactive "FAIR by Design" ecosystem.

We designed an AI based tool to diagnose and help users write clear, accurate, and complete data descriptions. The tool's components include best practices data description guidelines, data descriptions reviewed by experts as few-shot prompts, and chain of thought reasoning to explain the diagnostic outputs. We engineered our prompts and Large Language Model choice so that a score of 8 reflects an acceptable data description. Users can double check the evaluations and the assisted descriptions to minimize scores inconsistent with expert reviewers and hallucinated outputs. The application is crafted to match the standards of our field and to be used with guided intention.

This paper outlines the collaborative development of an AI data curation tool to support data sharing in the journal publishing workflow. As scrutiny of research increases, concerns have grown regarding reproducibility, as well as fraud and bad actors in the research lifecycle. Transparent, reproducible, and well-curated data is foundational to restoring confidence. In this paper we describe the current data sharing policy landscape at academic publishers and outline key challenges which might limit further data policy implementation and enforcement on journals. We provide insights into a new approach to data sharing compliance checks, the DataSeer SnapShot tool, and how this tool was developed with the collaboration of the Open Science-, Implementation-, and Editorial Operations teams at academic publisher Taylor & Francis. The potential future iterations of the tool and the implications of its wider implementation are also discussed.

This study explores how digital repositories approach qualitative research data curation through an ethics of care lens, particularly when handling data containing identifiable participants. Through 44 semi-structured interviews with educational researchers and teacher-educators who produce and reuse video records of practice (VROP), the research examines perceptions of care in repository practices and the relationships between repositories and their designated communities.

Our findings indicate that (1) data producers and reusers in education view repositories as sites of care, (2) they view data curation as a form of care, and (3) they expect repositories to act in the best interest of the participants represented in research data, thereby enacting an ethics of care.

Interviewees emphasized that repositories must extend beyond technical compliance to embrace ethical commitments that preserve participant dignity throughout the data lifecycle. They sought repositories whose values aligned with their own ethics of care, particularly regarding protection of vulnerable populations. The study identifies care as both a relational process that develops over time and a framework that should inform repository policies from data selection through access decisions.

These findings extend current understanding of designated communities beyond consumers of data to include groups whose ethical frameworks should inform repository practices, with implications for qualitative data repositories containing data with identifiable participants.

In academic research, data sharing, particularly secondary data reuse, relies heavily on informal networking. ‘Grey’ transfers of data motivated by research purposes are common. In this paper, working through use cases presented by professionals in digital health, research governance and sensitive data management and publication, we explore the compliance challenges of informal data sharing, its detection, policy challenges such as penalties and associated risks such as accidental data breach and scientific impact. We highlight challenges of maintaining researcher awareness of best practice, given the fast-moving UK legal and regulatory landscape and the need to maintain compliance with standards required by key research partners in the EU. We then explore how good data privacy practices, privacy impact assessments, principles of privacy by design and existing frameworks might be used to support the process of engineering systems that provide the needed flexibility to researchers while minimising the risks.

Much has been written about artificial intelligence, with astonishingly rapid progress in computer sciences. In the social sciences, concerns have been raised that artificial intelligence may impact the actual production of scientific output. Most of the discussion has been about the writing of texts, and estimates suggests that the number of articles created and possibly submitted with the help of AI systems is non-trivial. Less interest has been devoted to the use of AI as part of the legitimate scientific production process. Yet use of AI methods in legitimate scientific work is also increasing. With the earlier “replication crisis” still in mind, the question for curators is whether and how to curate AI-supported research tools, input data, and outputs. This article will approach the topic from the perspective of a “data editor”, responsible for verifying reproducibility and supporting curation of research compendia for a prominent learned society in economics.

This paper offers a retrospective analysis of the 2016/17 Data Rescue movement, a grassroots initiative that mobilized librarians, technologists, and activists to preserve at-risk federal environmental data in response to the anticipated threats posed by the Trump administration. Drawing on 16 qualitative interviews conducted in early 2025, the study examines how participants now reflect on their motivations, methods, and the movement’s legacy. It explores the ethical and affective dimensions of emergency curation, the tensions between institutional and community-driven preservation, and the shifting trust in public data infrastructures. Participants expressed a strong sense of civic duty and emotional urgency, but also critical distance from the movement’s limitations, particularly its overreliance on downloading as a preservation strategy. The findings underscore that trust in infrastructure is relational and partial, shaped by both political context and social practice. Ultimately, the paper argues that digital preservation in politically volatile times must be grounded in care, accountability, and long-term infrastructural thinking, rather than reactive interventions alone.

The official and accredited training of data stewards is a relatively new phenomenon worldwide. With the ever-growing presence of the Open Science and Open Data movements within the confines of academia, the need for professionals skilled in state-of-the-art data management and curation is axiomatic.

In Hungary two initiatives started unfolding in almost parallel fashion. The very first graduates of the first higher education level data steward training program at ELTE University received their degree in the autumn of 2024 (after three semesters of attendance). Similarly, the first ’graduates’ (more of an informal, not an accredited program) of the HUN-REN data steward training program received their certificates in January 2025. The HUN-REN data stewards started working in their newly created and acquired positions on the 1st of October.

The evolution of the HUN-REN Data Steward Network reflects a commitment to improving and institutionalizing research data management in Hungary. Each milestone represents a step forward in creating a robust and unified approach to data stewardship, ensuring that research data is managed efficiently and effectively across the network.

As the network continues to grow and develop, it serves as a model for other institutions and countries looking to enhance their own data management practices. The collaborative efforts and progressive initiatives undertaken by HUN-REN demonstrate the importance of data stewardship in the modern research landscape.

Forming the Hungarian national data steward community is based now on the informal collaboration between professionals and beginners from important institutions, marking a step towards the data steward professionalization in Hungary in which both ELTE’s DS training program and HUN-REN informal Data Steward Training program, as well as the HUN-REN Data Steward Network aim to play an important and effective role.

Since 2016, the implementation of the FAIR Principles encouraged re-thinking how data are managed, particularly regarding Indigenous communities which, due to the processes of colonisation, had very little impact on their knowledge and data. Hence, in 2018 the CARE Principles were drafted with the aim to tackle past injustices and support Indigenous communities in governing their data. However, the CARE implementation has not been straightforward as researchers, information experts and Indigenous Peoples faced many challenges starting from the lack of IT infrastructure, skills, funding and metadata. Hence, successful implementation of the CARE Principles requires significant financial and human resources.

It has been six years since the CARE principles were published. The aim of this paper is to analyse the obstacles of implementing them, whilst arguing that the process is one of the biggest contemporary challenges in data curation and the best solutions will eventually be found. The second part of this paper will address these challenges in the UK by analysing data collected in autumn of 2025 from the DataCite UK consortium members and the webinar guest speakers.

This presentation outlines the latest update to the CoreTrustSeal certification requirements (v04.00), which will guide repository certification from 2026 to 2028. Reflecting extensive community consultation, the update introduces minimal but meaningful revisions to maintain stability while emphasizing active preservation as a core feature of trustworthy repositories. Attendees will learn about the update process, key changes, and how to stay involved as CoreTrustSeal evolves to meet the future of trustworthy data stewardship.

Transparent Trustworthy Repository Attributes Matrix (TTRAM) supports the identification of a common understanding of repositories capabilities and needs for the FIDELIS and EDEN projects and the European Open Science Cloud (EOSC). This talk examines the next steps for the Matrix in seeking to reach consensus on the characteristics of Trustworthy Digital Repositories (TDR) in terms of trust in context, types and categories, and their implications for transparency across digital objects and organisational entities (transparent objects/entities): tic-tac-toe.

This abstract outlines the development of services for the long-term preservation of sensitive research data. Initially, the institution’s infrastructure was not equipped to handle high-risk datasets, prompting a comprehensive effort to enhance both technical and procedural capabilities. The work focused on five key areas: reviewing legal and ethical requirements, strengthening a multidisciplinary expert network, enhancing internal competencies, refining user workflows, and upgrading technical platforms. Collaboration with legal, IT, and data protection professionals was essential, as was hands-on engagement with researchers to assess real datasets. Notably, the team introduced structured protocols such as Data Access Protocols and Data Protection Impact Assessments to ensure compliance and accountability. Despite challenges—particularly in defining responsibilities and balancing security with usability—the strong demand from researchers proved a powerful driver. This initiative aligns with themes of sensitive data curation, tool development, and community building, offering a practical model for institutions seeking to support secure, sustainable research data preservation.

In 2023, four Flemish universities (Ghent University, Hasselt University, University of Antwerp, and Vrije Universiteit Brussel) initiated the FAIRVault project to provide researchers with a secure solution for preserving and providing (controlled) access to research data. It especially targets cases where external repositories are less suitable, such as for sensitive or large datasets, ensuring proper data retention and security.

Drawing on Artefactual’s conceptual model for digital preservation systems, this talk focuses on the importance of Information Packages preserved by an Open Archival Information System (OAIS). In software design, there are core design principles for digital preservation systems that, taken together, represent the best chance to ensure data can persist over time and space. This talk will focus on one principle: the AIP must be the system’s primary concern as the source of truth.

This talk will explore how advancements in large language models can help review, write, and improve connections of machine-actionable data management plans. It will review ongoing initiatives at the California Digital Library to improve the benefits and easy burden of writing DMPs, while still keeping the human author as the authority. It will cover learnings and progress and tips for others exploring similar areas.

Researchers find preparing data management plans (DMPs) time consuming and see data stewards as an appealing solution for filling them in on their behalf.

At Eindhoven University of Technology (TU/e), data stewards support in filling DMPs is limited by the knowledge on the research topic.

Advances in technological solutions are perhaps the most feasible way to support researchers in filling their DMPs before the data stewards final approval.

At TU/e different research support groups, such as the Research Data Infrastructure (RDI) and the Data Stewards (DS), have initiated a collaboration with an external party to develop a GenAI tool that would directly and automatically extract the relevant information from research proposals and to pre-fill the DMPs.

The tool is able to extract structured metadata from research proposals, based on a user-defined schema specifying the desired metadata attributes, e.g., research goals, presence of human participants, data origin and size, etc. (including a description per metadata attribute).

This presentation examines how large language models (LLMs) like ChatGPT can assist researchers in generating documentation such as codebooks and README files, helping meet Open Science and FAIR data requirements. It explores how LLMs can streamline workflows, align outputs with repository standards, and reduce curation burden. Alongside these opportunities, the talk also addresses the ethical, legal, and environmental challenges posed by AI integration, especially in contexts involving sensitive social science data.

Integrating diverse data in Earth System Sciences (ESS) is hindered by semantic heterogeneity, as different disciplines use inconsistent terminologies. The BITS project addresses this challenge by providing a dedicated Terminology Service (TS) with over 40 vocabularies, maintained by the TIB for long-term use. A practical use case at the Senckenberg Institute combines AI tools, edge computing, and the TS to automatically annotate and process digitized natural science collections efficiently. This workflow improves data findability and interoperability, particularly for multilingual and handwritten records, while ensuring data security through near-data processing. The Lightning Talk highlights potential risks of irresponsible AI use, despite its clear benefits when integrated with other services.

The instability of the current United States research landscape has required the rapid response of data curators and librarians to emerging changes. As the shifts in the research landscape continue, shouldering the weight of the changes alone diminishes our ability to provide support to researchers, keep up to date on the situation, and respond to changes in an informed manner. Instead of placing the burden on a sole individual in an organization, a more holistic and sustainable model is developing a community focused on mutually supporting one another. This lightening talk will discuss how a data librarian built a community dedicated addressing the changes in federally funded research policies through knowledge sharing, dividing labor, and developing effective training. The goal of this presentation is to provide concrete takeaways from the successes and challenges of this use case, in order that attendees can develop their own resilient community in the face of continual fluctuations.

At a time where research institutions globally are being faced with diminishing budgets, methods for prioritising data for preservation are essential. This talk will detail our application of the Hoffman et al. data rescue framework (DRF) in a recent project to prioritise datasets for rescue, plan workload, anticipate potential obstacles, and approximate resources required. I will detail our novel points-based adaptation of the DRF which facilitated the decision-making process of which dataset to save, accounting for our limited budget. I will also describe how we used this adaptation to quantitatively compare the dataset before and after rescue, taking the FAIR principles into account. The methodology I will describe is likely applicable to countless similar datasets currently held in inaccessible locations and gives a step-by-step structured process for data curation professionals to follow from prioritising data through to publication. It could greatly improve efficiency and prioritisation of data rescues if adopted by other institutions, particularly those affected by scarcity of budget and resource.

This talk will discuss recent work by the Digital Preservation Coalition to collaboratively create guidance and advice for the community on how to calculate the carbon footprint of digital preservation activities. Through the Carbon Footprint Task Force, which has been meeting every month during 2025, a group DPC Members has come together to share their own experiences and develop a toolkit for others within the community to use to help them work in this area. This short talk will describe this work and showcase the online resource which is due for publication in February 2026.

This presentation shows how Arquivo.pt is preserving websites related to scientific research projects and how web information from the past is becoming a source of data for research projects and the development of Artificial Intelligence applications. It is argued that the preserved contents of the web are also data and, as such, should be included in issues related to curation.

The National Social Security Fund (NSSF) Uganda, established under the NSSF Act, provides social security services to all employees in Uganda. In 2018, the Fund initiated a records digitization project to address challenges such as manual processes, long document flows, and rising operational costs. This led to the implementation of an Electronic Document and Records Management System (EDRMS), known as ADA (Advanced Digital Archival), aimed at digitizing historical records for easier access and retrieval.

Key activities included preparation, scanning, and indexing of records, resulting in a digital central repository that allows real-time access for employees, enhancing customer service and workflow tracking. The Fund achieved a 95% paperless operation, promoting a clean desk policy. However, challenges arose, including high software maintenance costs and the need for ongoing change management.

Looking ahead, the Fund aims to integrate the EDRMS with other systems, build internal capacity, and leverage data-driven decision-making. This presentation outlines the digitization journey, highlighting improvements in accessibility, operational efficiency, and customer service, while recommending strategies for other institutions to enhance their records management processes.

This paper presents a workflow for HTR dataset generation in Romanian using Transcribathon’s Correct HTR feature. Leveraging citizen-science transcriptions aligned with Transkribus outputs, our case study on Jurnalul lui Dumitru Nistor reduced CER from 15.26% to 0.13%. The approach enables efficient dataset curation and supports scalable model development in low-resource languages.

Construction of High Speed 2, the UK's largest linear infrastructure project, brought the need to undertake the most extensive archaeological programme the country had ever seen. Huge challenges to how its archaeological outcome would be recorded and preserved derived from the monumental geographic and temporal scale of the project. As specific government-mandated requirements were imposed on the overall scheme, this created a complex regulatory environment for the large number of parties involved. Discrepancies between companies with distinct methodologies and individual reporting standards posed a threat for the consistency of the records and therefore their preservation, access and reuse.

The Archaeology Data Service (ADS) was tasked with setting standards for data deposition, digital preservation, and access to all archaeological data created by HS2. With over 31 terabytes of data in an array of formats, the project shone a light on the limitations of existing frameworks when managing large-scale, heterogeneous datasets while presenting a significant opportunity for innovation in archiving practice, infrastructure, and rationale. The scale and complexity of HS2 introduced both technical and epistemological risks to how we provide long-term digital preservation to the data entrusted in our care while ensuring it remains findable, accessible, interoperable, and re-usable.

This paper analyses and reflects upon how the ADS has been transformed by the demands of HS2, not only in its technical capacity but in its understanding of the infrastructural, organisational, and ethical dimensions of large-scale digital curation. Challenges offered a proving ground in which future approaches to archaeological data management and archiving could be tested. This led to new tools, adaptable procedures, better workflows, and more nuanced perspectives on the value of curated data. Our capacity to ensure data integrity and accessibility in perpetuity has expanded, demonstrating the project’s long-term infrastructural benefit to the sector on a wider level.

Data auditing has become increasingly critical for large-scale biomedical repositories as they serve diverse research communities while maintaining scientific rigor and compliance with established standards. The Human BioMolecular Atlas Program (HuBMAP) aims to map the human body at single-cell resolution through curated spatial and molecular data. The Human Reference Atlas (HRA), a central output of HuBMAP, includes datasets such as Anatomical Structures, Cell Types and Biomarkers (ASCT+B) tables, 2D Functional Tissue Unit (FTU) illustrations, 3D reference organ models, and Organ Mapping Antibody Panels (OMAPs). This study reports the first comprehensive, third-party audit of the HRA, conducted from March to July 2024 to assess data quality, internal consistency, and adherence to Standard Operating Procedures (SOPs). The audit methodology combined systematic evaluation of metadata completeness and correctness with visual inspection protocols designed to assess user experience and functional utility across different digital object types. Using a combination of visual inspections, file metadata analyses, and spreadsheet comparisons across 34 ASCT+B tables, 22 2D FTU illustrations, 70 3D reference models, and 21 OMAP datasets, the audit demonstrated overwhelmingly positive results, with compliance rates of 94-100% across most evaluation criteria. Findings indicate that HuBMAP maintains robust curation standards, with structural issues present in fewer than 10% of ASCT+B tables. This audit provides a replicable model for future quality assurance activities in large-scale biomedical data infrastructures and highlights the importance of continuous audit processes for ensuring data integrity, transparency, and usability in contemporary digital curation contexts.

This presentation introduces the first comprehensive analysis of how Government of Canada open data is cited in academic literature, addressing a critical challenge digital curators face: how to demonstrate the impact and value of open data collections.

Using a fine-tuned BERT language model trained on over 3,000 manually verified citation examples, this study overcame the problem of inconsistent data citation standards. This study leveraged AI to identify 3,953 citing articles with 91% accuracy, significantly outperforming traditional keyword-matching methods at 73% accuracy.

The study reveals key usage patterns across disciplines, identifying environmental science, agriculture, and immigration studies as primary users of Canadian government data. The study's findings provide digital curators with evidence-based insights for strategic collection development and resource allocation decisions, while the open-source methodology offers the community immediately deployable tools for impact assessment.

In an era of budget cuts where archives must continually justify their value, this study demonstrates how AI can enhance traditional bibliometric approaches to provide more comprehensive and accurate measures of collection impact, directly addressing contemporary challenges in digital curation.

In 2024 the UK Reproducibility Network (UKRN) initiated a set of pilots involving institutional members and solution providers to establish good practice in institutional monitoring of Open Research through the creation of robust indicators. The Open Research Indicators Pilot was sector led, with institutions and solution providers working together to develop, test, and evaluate prototype machine learning solutions with valid, reliable, and ethical indicators for measuring Open Research. The University of Bristol was the lead for the ‘Openness of Data’ pilot and assessed providers’ data to ascertain the usefulness of machine learning for this purpose.

The pilot’s findings highlight the inherent challenges and limitations of monitoring and assessing published datasets for openness within a research landscape that prioritises articles as benchmark outputs; the combination of article primacy and existing publisher and repository systems means datasets can currently only be monitored in Data Availability Statements (DAS). Our analysis of machine learning tools confirmed an uncomfortable truth many in the RDM community suspected; we do not have enough openly available machine actionable metadata for digital tools to reliably and accurately extract DAS, and we are not doing enough at the human interface with researchers to ensure their DAS are easy to understand and describe how their data can be found by others, which impacts measuring openness.

The presentation "Standardization Vs. Preservation: Supporting Interoperability by Enhancing Thematic Metadata at Social Science Archives" addresses the challenges of data standardization and interoperability in social science research. Emphasizing the importance of effective metadata practices, the project ONTOLISST aims to study thematic ontologies with the purpose of improving data discoverability and sharing among diverse research infrastructures.

The project, funded by the European Commission's Horizon Europe program, investigates the varied approaches to thematic metadata creation across research repositories containing social science survey data. By analyzing metadata structures and curation practices, the research seeks to identify and explain commonalities and discrepancies in metadata schemes that hinder interoperability. The study highlights the need for rich metadata documentation while navigating the complexities arising from competing standards and the diversity of data describing practices.

Drawing on data documentation received from the repositories and extensive interviews with data management experts, the project presents two kinds of outcomes: research studies and technical innovation. The results of analysis feed into the development of a semi-automated thematic metadata-generating scheme based on a simplified thesaurus (LiSST). This tool aims to facilitate the integration and accessibility of social science data, fostering connectivity across disciplines and languages. Thus the anticipated outcome is a harmonized metadata structure that upholds the rich, nuanced meanings of original research while promoting discoverability and reuse.

By focusing on the balance between standardization and preservation, ONTOLISST affirms that thoughtful approaches to thematic metadata can yield practical solutions to interoperability challenges, ultimately enhancing the usability and visibility of social science datasets in the global research landscape.

The EU Horizon Europe project EOSC EDEN was launched in 2025 to support trustworthy digital preservation in research data infrastructures. This paper introduces a first output of EOSC EDEN, the Core Preservation Processes (CPPs), a structured yet flexible set of process descriptions that articulates the essential steps involved in digital preservation in a hands-on manner. Developed by practitioners and designed as practical guidance, the 30 CPPs describe essential, system-agnostic actions that should be ensured by trusted digital archives. By offering a shared terminology and process model, the CPPs help bridge gaps between research data management and digital preservation, providing the communities a powerful tool to support training, policy, and system development. They shall address the challenge of making digital preservation knowledge more accessible, actionable and interoperable within the EOSC ecosystem and beyond.

This paper reports on a series of collaborative self-evaluations on support services for Research Data Management (RDM) at Irish Research Performing Organisations (RPOs). The evaluations are carried out as part of the iFrame project, which is tasked with drafting a national RDM framework for Ireland.

The aim of the evaluations is to:

1. Assess institutional maturity of the services provided and enable the identification of areas for improvement via tailored reports for individual institutions.

2. Gather inputs for a landscape report on the state of RDM service provision in Irish RPOs.

3. Identify best practices in institutions that can be integrated into the upcoming national RDM framework.

The paper commences with a contextual discussion of the Irish higher education environment and the research policy environment that advocates the development of RDM practices. This is followed by a brief review of the literature that examines RDM support and practice in RPOs, utilising maturity models. The main section of the paper outlines the research rationale, design, and methodology employed in the iFrame project, which is built around the principles of openness, collaboration, and process orientation. The paper concludes with an overview of results and anticipated impact, and provides an outlook on the next steps of the project and the development of RDM at Irish RPOs.

Trustworthy Digital Repository (TDR) certification processes are mechanisms through which digital repositories signal quality and commitment to best practices to external stakeholders. In a scholarly landscape with constrained funding and the threat of austerity measures, certification serves as a risk mitigation strategy that repositories use to articulate their value. This study examines repository staff attitudes about CoreTrustSeal (CTS) certification requirements to understand how professional expertise shapes perspectives on what makes repositories trustworthy for long-term digital preservation.

A survey was administered to all CTS certified repositories in fall 2020, with 88 responses from repository staff (53.98% response rate). Respondents ranked the three sections of the CTS Requirements by importance and answered follow-up questions about specific requirements. Professional roles were categorized as administration, digital preservation, IT, and other, and respondents indicated whether they had experience as CTS reviewers.

Findings demonstrate that respondents consistently ranked Organizational Infrastructure as the most important section of the CTS requirements, followed by Digital Object Management, and then Technology. Responses demonstrated a relationship between respondent expertise and attitudes about the CTS requirements. Additionally, those with experience as reviewers had more consistent views than those without review experience, indicating that exposure to multiple repository contexts through the review process also influences attitudes about certification.. These results suggest that expertise does indeed play a role in attitudes about CTS certification requirements.

Data citation is a crucial, but often neglected, component of data curation and data management. This contribution shows how data citation can be moved forward by different stakeholders. The stakeholders included here are data repositories, researchers, and academic journals. For all of them proper data citation practices offer benefits. However, often, data are not cited correctly, implying that the producer of the original data does not get appropriate credit for the work underlying the data production process. Improper data citation also implies that repositories cannot track their impact. In this contribution, we analyse several materials, including survey data and journal policies, and elaborate on the steps that are necessary to improve data citation practices, and the respective roles of repositories, researchers, and journals. We derive lessons learned and recommendations for best practice in data curation work, regarding which we want to engage with the IDCC community. In line with the theme of the IDCC 2026 and with current needs and discussions in data citation, our contribution will also reflect on how AI can help repositories, researchers, and journals in improving data citation practices. The focus of this contribution is on social science data in Switzerland; yet the lessons learned are also relevant for other disciplines and national contexts.

Here we compare the performance and cost of four language models (GPT 4, Llama 3, Gemma 2 and Mixtral 8x7b) in the lightweight task of population group curation. Our findings provide insight into potential sustainable curation practices in the presence of limited resources.

Research data management needs better, integrated approaches. Data Management Plans (DMPs) are key, but fragmented systems limit their FAIR principles potential. This paper introduces FJORD, a framework evolving DMPs to integrate and orchestrate research ecosystems at an institutional level. FJORD enhances DMPs into supplementary tools, leveraging a project proposal platform to manage, monitor, and track intellectual assets via knowledge graphs, aiding researchers across university systems. The framework exports machine-actionable DMPs (maDMPs) compatible with RDA and existing DMP tools. By providing unified metadata management and system interoperability, FJORD improves FAIRness tracking and assessment, streamlines administration, and accelerates scientific discovery, while maintaining data sovereignty. This paper details FJORD's tenets, architecture, and impact on institutional data strategies and governance.

This technical paper explores the development and strategic role of Geographic Entity Object (GEO) codes in Bangladesh, focusing on their integration into census and survey operations. GEO-Codes are hierarchical geographic identifiers that represent administrative units from Division down to Village and Enumeration Areas (EAs), enabling accurate data collection, integration, service delivery, and smart governance. Each and every dataset can be tracked using this scientific GEO-code.

The Bangladesh Bureau of Statistics (BBS) initiated the GEO-Code system in 1978 to prepare for the 1981 Population Census. Collaborating with the Land Record and Survey Department, BBS digitized and systematized the Mouza lists and maps into a scientific coding system. Over the years, this system has evolved into a sophisticated, multi-tiered structure used across all national censuses and socio-economic surveys.

Initially, in 1961, Bangladesh had 4 Divisions and 19 Districts. By 2022, this expanded to 8 Divisions and 64 Districts. Population growth also surged, from 50.84 million in 1961 to 169.83 million in 2022. The number of villages increased from about 68,038 in 1974 to 90,049 in 2022. GEO-Codes have been uniquely assigned to each administrative unit: Division, District, Upazila, Union/Ward, Mouza, and Village.

Historically, geo-coding systems trace back to British colonial cadastral surveys and pre-1971 East Pakistan’s Thana-level census framework. Post-independence, Bangladesh transitioned to a structured multi-level coding system that has now been digitized. The first fully digital implementation occurred in the 2022 Population and Housing Census, covering over 58,846 Mouzas and 600 Upazilas.

By reviewing both historical practices and contemporary innovations, the paper emphasizes that a robust GEO-Coding system is a cornerstone for inclusive development, precise data governance and national planning. With further coordination, Bangladesh can use GEO-Codes for urban planning, AI-driven policymaking, real-time census & survey analytics and maintain robust control over all the government agencies where it is needed most.

The administrative load on researchers is continuously growing. They are confronted with an increased demand to comply with administrative bureaucracy such as filling in Data management Plans (DMPs), Ethics forms, and much more. Most of these activities involve researchers filling in multiple forms, and sometimes these forms also require filling in the same information repeatedly. In the Netherlands, many universities are facing similar issues where the Research Data Management (RDM) landscape is getting diverse as more and more data management solutions are becoming available. Researchers are also confronted with several rules’ regulations and a wider formalization of RDM practices. This comes with new requirements around data management, including the requirement by funders to write a Data Management Plan. This paper presents the research data management platform developed at Vrije Universiteit Amsterdam (VU), designed to reduce the administrative burden for researchers through automated task flows. This provides guidance based on the research lifecycle and connects with support and topic-related support staff.

With the Research Data Management Administration Platform (RDMA), VU Amsterdam created a solution that integrates curation, tools, and services within the research project process. In this paper, we describe the co-creation design process, the challenges involved in scaling across faculties, and the implementation of the platform. We reflect on the importance of tools like the RDMA platform and its impact on the reduction of administrative bureaucracy, whilst offering a model and support for institutions seeking to embed such systems.

The digital preservation of e-Theses presents several challenges. This is partly because the status of doctoral theses within the scholarly communication ecosystem is unclear. The shift from physical submission of theses to digital submission is also relatively new, spurred on by the Covid-19 pandemic. This paper is focused on policies, practices and workflows as they relate to e-Thesis submission and digital preservation. The research underpinning the paper is based on interviews, surveys and focus groups with doctoral colleges, institutional repositories and doctoral researchers within UK universities.