International Journal of Digital Curation
The IJDC publishes research papers, general articles and brief reports on digital curation, research data management and related issues. It complements the International Conference on Digital Curation (IDCC) and includes selected proceedings as Conference Papers.
Copyright for papers and articles published in this journal is retained by the authors, with first publication rights granted to the University of Edinburgh. It is a condition of publication that authors license their paper or article under a Creative Commons Attribution 4.0 International (CC BY 4.0) licence.
A Brief Overview of Open Educational Resources Accessible in Indian Digital Learning Repositories
This paper provides an overview of the major open educational resource (OER) repositories available in India. The researchers focused on the digital learning repositories maintained by the INFLIBNET Centre, a premier institution under the University Grants Commission of India. The repositories examined include Shodhganga (a repository of Indian theses and dissertations), Shodhgangotri (a repository of research synopses), the INFLIBNET Institutional Repository, INFOPORT (a subject gateway for Indian e-resources), e-PG Pathshala (an e-content project), Swayam Prabha (free educational DTH channels), and Vidya-mitra (an integrated e-content portal). The study highlights the objectives, features, and content of these repositories, which provide free and open access to a wealth of educational materials across diverse disciplines. These repositories play a crucial role in preserving and disseminating India's intellectual output, benefiting both the academic community and the general public. The review of related studies indicates that factors such as perceived ease of use, usefulness, free access, and user attitudes positively influence the adoption of OERs. However, challenges remain, including limited time for resource discovery, lack of awareness about usage and copyright, technological limitations, inadequate organizational support, and quality assurance concerns. The paper underscores the need for further research and awareness-building efforts to maximize the potential of these open educational repositories in India.
Copyright (c) 2025 Abhijit Chakrabarti, Professor Bulu Maharana https://creativecommons.org/licenses/by/4.0
A Maturity Model for Urban Dataset Metadata
The rapid increase in published datasets has intensified challenges in sourcing and integrating relevant data for analysis. Persistent obstacles include poor metadata, ineffective presentation, and difficulties in locating and integrating datasets. This paper delves into the intricacies of dataset retrieval, emphasising the pivotal role of metadata in aligning datasets with user queries. Through an exploration of existing literature, it highlights prevailing issues, such as identifying valuable metadata and developing tools to maintain and annotate them effectively. The paper proposes a dataset metadata maturity model, inspired by software engineering frameworks, to guide dataset creators from basic to advanced documentation. The model encompasses seven pivotal dimensions, spanning content to quality information, each stratified across five maturity levels to guide the optimal documentation of datasets, ensuring ease of discovery, accurate relevance assessment, and comprehensive understanding of datasets. This paper also incorporates the maturity model into a data cataloguing tool called CKAN through a custom plugin, CKANext-udc. The plugin introduces custom fields based on different maturity levels, allows for user interface customisation, and integrates with a graph database, converting catalogue data into a knowledge graph based on the Maturity Model ontology.
Copyright (c) 2025 Mark S Fox, Professor, Bart Gajderowicz, Dr, Dishu Lyu, Mr https://creativecommons.org/licenses/by/4.0
Good Support Helps To Avoid Data Horror. Experiences With Organising Research Data Management Support
The complexity around data management decisions is a tangible burden to researchers and can be a significant barrier to engaging with Open Science principles. Researchers often face a multitude of choices regarding storage solutions, sharing technologies, security, and legal and ethical concerns, each with its own set of implications for their work. Simplifying these processes through the development of intuitive tools, clear guidelines, and nearby support is crucial for demystifying the preservation of research data and making it more approachable. Since 2018, Vrije Universiteit (VU) Amsterdam has been steadily developing a data management ecosystem consisting of professional support services, an adequate technical infrastructure, and an awareness-raising and training programme to increase the skillset of researchers on data and software management. This ecosystem is organised as a collaborative effort in which faculty and central services are strongly involved in a network organisation, the Network for Research Data Support (NeRDS). Operational since April 2021, NeRDS focuses on professionalising Research Data Management (RDM) and services through four main tasks: (1) providing user support on tooling and sustainable data and software management services; (2) maintaining the technical solutions on data and software management; (3) innovating and developing new services based on researcher demand; and (4) offering data and software management information and training within a community.
Copyright (c) 2025 Marcel Ras, Elisa Rodenburg https://creativecommons.org/licenses/by/4.0
In this paper, we discuss views on trust around custodianship and curation of non-photographic digital legacies. Physical photographs have long been the artefact of choice for storytelling and leaving a legacy for those that come after. Digital photographs, on the other hand, present curation challenges in terms of the size and complexity of the user-generated libraries. Further, their highly personal nature can lead to concerns over possible embarrassing content, which makes custodianship difficult. In a mixed method research project, five non-photographic digital asset types emerged as being significant in the daily lives of participants, namely music, books, programming, gaming and note-taking. We compared them to participants’ views of digital photographs as a legacy. We found that journaling, music and books read were on par with photographs for emotional attachment and providing a sense of self, in some circumstances. Indeed, some non-photo assets, such as music playlists and reading lists were cited as being less context specific and more able to communicate sense of self than photographs. Collections of non-photographic assets were typically much smaller than collections of photographs and were stored in more organised and centralised libraries, which makes curation easier. We also examined the intent to leave assets as legacy and the desire to curate them before leaving them. Our research also revealed lack of defined custodianship for digital legacy data being left behind. Participants were pre-occupied with ultimate target audiences that might be interested in the legacy, as opposed to who would be trusted to look after and administer the legacy. In future work, we plan to conduct a series of workshops on trust and custodianship to explore how a cohort of everyday users might navigate and show confidence in leaving a curated digital legacy.
Copyright (c) 2025 Duncan Reid, Daniel Woods, Maria Wolters https://creativecommons.org/licenses/by/4.0
Curatorial Agency in IR Migrations: A Case Study of the University of Toledo Digital Repository
This case study focuses on the role of curatorial agency in the migration of the University of Toledo Digital Repository (UTDR). Institutional repository (IR) migrations are necessary preservation actions intended to ensure long-term access to digital content. Disruptions resulting from iterative migrations may diminish user trust in IR services and present other risks. Curatorial agency refers to the responsibility and authority of curators mediating between digital media and audiences and can mitigate some unforeseen or unavoidable effects of data migrations. Curatorial agency is established through connections and negotiations within heterogeneous actor-networks, which result in transformational processes, such as those associated with data migrations. Therefore, this case study takes a sociotechnical approach needed for an analytical framework, which merges elements of the actor-network theory with those of the Digital Curation Centre’s (DCC) curation lifecycle model and a Levels of Representation in Digital Collections framework based on Lee’s model. It focuses on the vital role of curatorial agency in UTDR migrations. Using a detailed account of the repository migration and framework analysis, this case study offers significant insight into the role of curatorial agency in managing migrations and establishing new curation strategies, including virtual exhibitions. Key findings include increased transparency of transformational processes in the UTDR migrations and in the role of curatorial agency in the preservation framework.
Copyright (c) 2025 Arjun Sabharwal https://creativecommons.org/licenses/by/4.0
Base4NFDI: Fostering A Cross-Disciplinary Service Landscape For The German National Research Data Infrastructure
Base4NFDI is a joint initiative by the 26 consortia of the German National Research Data Infrastructure (NFDI), aiming to develop essential cross-disciplinary basic services that enable FAIR data practices. Through a proposal-driven, bottom-up process, Base4NFDI supports technical and organisational solutions, such as identity and access management, computing, software, and workflows that serve the NFDI community. Proposals emerge from NFDI sections, where domain and infrastructure experts collaborate across disciplines. The role of Base4NFDI is to provide and orchestrate a multi-stakeholder process to decide which services to fund and to ensure coherence through structured development phases (Initialisation, Integration, and Ramp-Up), supported by staff who facilitate coordination and quality assurance. So far, eight candidates are under development, such as IAM4NFDI, TS4NFDI, and Jupyter4NFDI. This brief report introduces the Base4NFDI approach, outlines the decision-making and support processes, introduces current service candidates, shares early experiences and challenges, and provides an outlook on sustainability and international interoperability, particularly with the European Open Science Cloud (EOSC).
Copyright (c) 2025 Sandra Zänkert, Antje Manske, Bernhard Miller, Juliane Fluck https://creativecommons.org/licenses/by/4.0
Application and Evaluation of the Hoffman et al. (2020) Data Rescue Framework using an historic Scottish Cloud and Rain Chemistry Dataset exemplar
Environmental data are vitally important and valuable research outputs, and there are vast quantities in laboratory storage and on servers where they are not easily or openly accessible. It is imperative for preservation and for potential reuse purposes that historical data of long-term value are efficiently curated and made publicly available. We evaluated the Hoffman et al. (2020) data rescue framework (DRF) for the initial assessment stage of a data rescue by applying it to an historic Scottish cloud and rain chemistry dataset. The DRF facilitated workload prioritisation, anticipating potential obstacles, and approximating resources required. We used a novel points-based adaptation of the DRF to identify suitability of datasets for rescue and compare the dataset status before and after rescue, particularly taking FAIR principles into account. The reusability of the dataset was greatly improved by the Hoffman et al. (2020) framework, and it is now published in an appropriate open access data centre with detailed metadata. It is recommended that the traceable DRF and scoring system be adopted in future to begin moving twentieth and early twenty-first century environmental data into the public domain.
Copyright (c) 2025 Shona J Ferguson, J.N. Cape, A. Crossley, F. Harvey, D. Fowler, D. Leaver, C.F. Braban https://creativecommons.org/licenses/by/4.0
Bridging the Gap Between Process and Procedural Provenance for Statistical Data
We show how two models of provenance can work together to answer basic questions about data provenance, such as “What computed variables were affected by values of variable X?”. Questions like this are central for understanding how data is managed and modified. W3C PROV is a widely used standard for describing the people, activities, and sources that create things like documents, a work of arts, and data sets. PROV associates processes with inputs and outputs, but it does not have a way to describe how data are changed within a process. PROV has no language for program components, like mathematical expressions or joining data tables. Structured Data Transformation Language (SDTL) was designed to provide machine-actionable representations of data transformation commands in statistical analysis software. SDTL describes the inner workings of programs that are black boxes in PROV. However, SDTL is detailed and verbose, and simple queries can be very complicated in SDTL. Structured Data Transformation History (SDTH) bridges the gap between PROV and SDTL. SDTH extends the PROV data model to answer questions about data preparation and management operations not available in PROV.
Copyright (c) 2025 Timothy McPhillips, Jack Gager, Thomas Thelen, George Charles Alter, Jeremy Iverson, Bertram Ludäscher, Dan Smith https://creativecommons.org/licenses/by/4.0
Global Open Research Commons: Enabling Curation for the Next 20 Years
This paper addresses the requirements for long-term preservation through a system lens. Rather than focussing on specific technical elements that are needed for curation, this paper considers all the system elements that need to be put in place, and intentionally maintained, to ensure curation for the long term. This paper begins by making the argument that curation requires attention to preservation over time. The need for preservation, in turn, requires both sustainable data content and sustained infrastructure. These infrastructures consist of many elements, both social and technical, all of which need attention. Then, this paper briefly introduces the concept of the open research commons as a way of conceptualising these elements, before examining in some detail the Global Open Research Commons (GORC) typology of essential elements. This work was developed through a Research Data Alliance Working Group, which started with a definition of a commons as ‘a global trusted ecosystem that provides seamless access to high-quality interoperable research outputs and services.’ The essential elements in the typology include Information and Communications Technology (ICT) infrastructure, services and tools, research objects, human capacity, rules of participation and access, governance, engagement, and sustainability. This general approach was then extended by the GORC International Model Working Group to ‘review and identify attributes or features currently implemented by a target set of GORC organisations.’ The GORC approach has already been used in designing the creation of new commons, characterising existing research infrastructures, and analysing interoperability between commons. Future work, to commence in 2025, will clarify how the International Model might be used and adopted, as well as improve how it is presented. Our researchers require ongoing access to reliable and sustainable data aggregations. These will need to be curated for reuse and interoperability over the long term to support the integrity of the scholarly record. The GORC groups are working towards an interoperable set of platforms that together build on advances in internet technologies and the consensus and strengths of the research community.
Copyright (c) 2025 Dr. Andrew Treloar, Dr. C. J. Woodford https://creativecommons.org/licenses/by/4.0
This paper examines milestones and unique service aspects of six research-intensive Higher Education Institutions’ approaches to research data management policy and service, almost one decade on from their respective beginnings, based on findings from a 2024 internal benchmarking study conducted by the University of Oxford, which consisted of interviews with library-based research data management service providers at five peer UK institutions. Both similarities and differences are examined, and milestones are mapped against external events and policies in the RDM field. Future directions, and areas of convergence and divergence especially, will be explored across six institutions: the Universities of Cambridge, Edinburgh, Manchester and Oxford, Imperial College London, and University College London (UCL).
Copyright (c) 2025 Ruth Mallalieu, Robin Rice https://creativecommons.org/licenses/by/4.0
Curation matters for data quality! Hardly any survey data user would disagree with this statement. But how much of a difference it makes is difficult to count. In this paper, we will illustrate on the example of data from two cross-national social survey programs, the European Value Study (EVS) and the International Social Survey Programme (ISSP), the most common errors that occur in uncurated international comparative data and draw attention to the problems that can arise from such errors in analyses’ results. To facilitate quality assessment and enable the assessment of data quality variation between countries within a survey, we developed a scheme that categorizes these errors, helps quantify them, and assigns them to possible curation measures. Based on this scheme, we developed an indicator that is called the Curation Impact Factor (CIF) that puts a concrete number on the data quality improvement due to curation effort and allows for comparability even across surveys. Therefore, the CIF could potentially be used to justify the use of resources for data curation in any survey data life cycle (e.g., in grant applications).
Copyright (c) 2025 Insa Bechert, Kerstin Beck, Ivet Solanes Ros https://creativecommons.org/licenses/by/4.0
Mad with the Writing: 100 Years of Collecting, 20 years of Digitising, 3% Completed
This is a story about the Australian War Memorial’s historically significant, preeminent archive, which represents 100 years of collecting, 20 years of digitising and to date is three per cent digitised! Whether the format of the original record is created in paper or digital, we are all still “mad for the writing”, and there is only one thing for certain: that for the next 20 years, we will still be digitising! We all want to read and access collections online. It is hoped that developments in technology will continue to make that desired access faster, easier, safer and more efficient. Artificial Intelligence will most certainly play a role in ensuring that we will not be trying to “drink from the fire hose” when accessing digital collections into the future. Transcription technologies will continue to deliver research data that is relevant today in a variety of contexts.
Copyright (c) 2025 Robyn van Dyk https://creativecommons.org/licenses/by/4.0
An Exploration of the Functionality and Usability of Open Research Platforms to Support Open Science
This paper examines the user experience and functionality of four open research platforms - Zenodo, Figshare, OSF, and Authorea - to assess their utility in disseminating research outputs that are varied in form as well as academic discipline, and in facilitating collaboration on larger projects by multi-institutional groups. The researchers analysed the platforms’ community features, record creation processes (including metadata fields), search functionality, and analytics capabilities.
Copyright (c) 2025 Whitney Thompson, Angela Murillo https://creativecommons.org/licenses/by/4.0
FAIR Data Implementation at Large-Scale Data Facilities in the USA
This paper examines current FAIR data implementation at large-scale data facilities in the USA. A 15-question survey was distributed to facility personnel to explore the current status of FAIR implementation, including progress, barriers, non-barriers, and data management practices. As FAIR implementation considerably increases trust and transparency, this research facilitates understanding the current status and needs for robust FAIR implementation.
Copyright (c) 2025 Angela Murillo, Don Brower, Jose Cordova, Sonja Crncevic https://creativecommons.org/licenses/by/4.0
FAIR Principles Implementation in ML/AI - Findings from Skills4EOSC Delphi Study
Implementing the FAIR (Findable, Accessible, Interoperable, and Reusable) principles for scientific data management in machine learning (ML) and artificial intelligence (AI) offers numerous benefits, including higher model reliability, more collaborative research, and greater reproducibility. Despite these advantages, there is a lack of clear, practical guidelines for improving the FAIRness of ML/AI outputs, especially the models. To address this gap, Skills4EOSC Task 6.3.3a of Work Package 6: Professional Networks for Lifelong Learning conducted a Delphi Study to gather expert consensus on implementing FAIR principles in ML/AI model development. A Delphi study, involving two rounds of surveys followed by an online meeting, was conducted. In the first round, ML/AI experts from Europe and beyond rated suggested FAIR practices and proposed additional ones. The second round involved feedback and re-evaluation of these practices. The final meeting included detailed discussions on the Top 10 practices for FAIR principles implementation in ML/AI. The resulting Top 10 practices aim to provide guidelines for researchers and data management professionals to implement FAIR principles in ML/AI.
Copyright (c) 2025 Elda Osmenaj, Curtis J M Sharma, Ugo Moschini, Lisana Berberi, Valentina Pasquale https://creativecommons.org/licenses/by/4.0
Professionalising Social Science Research Data Management with RDM Compas
It is not only since the publication of the FAIR principles, requiring the Findable, Accessible, Interoperable, and Reusable organisation of research outputs, that the demands placed on researchers when conducting good scientific practices have increased. Universities and other research institutions have responded to this and offer increasing support, especially regarding research data management (RDM). This is to support their researchers in the FAIR organisation and thus transparent sharing of all their research outputs. However, digital curation and RDM are complex and demand an interplay of various skills and knowledge. So far, there are no established training programmes. Therefore, projects aiming to support researchers in RDM often face one major challenge: Recruiting staff well trained in RDM. In our project, we examined data curation skill needs and various approaches to define these needs. Paired with the DCC Curation Lifecycle Model and survey results from Social Science Research Data Centres in Germany, we defined curation skill needs and learning trajectories and implemented these in an information and training platform for data curation in the social sciences, RDM Compas, with its content organized along the DCC Curation Lifecycle Model. A newly developed data curation module in a nationwide RDM certification course compliments the training path toward professional RDM.
Copyright (c) 2025 Anja Perry, Ekaterina Chicherina https://creativecommons.org/licenses/by/4.0
ASSURED: Assuring Safe Research by Safe People
The ASSURED project aims to address the need for standardised training for researchers and professionals working with sensitive data in Trusted Research Environments (TREs) in Germany. As data sharing for research continues to grow, safeguarding sensitive data is critical, particularly as Open Science and FAIR data principles promote wider access. However, ensuring secure data access while minimising risks requires robust safeguards, such as the Five Safes Model. An essential element of this model is the ‘Safe People’ component, emphasising the importance of well-trained individuals who understand data confidentiality and disclosure risks. Currently, training for researchers and TRE staff in Germany is inconsistent, with few formal systems in place. To remedy this, the ASSURED project has developed an e-learning programme offering flexible, modular training to ensure that researchers and TRE staff meet essential data security standards. The programme includes core modules applicable to all users, with additional role-specific content tailored to various TRE services. By integrating the training with an Authentication and Authorisation Infrastructure (AAI), the programme ensures streamlined tracking of completion and facilitates cross-service access. The ASSURED project aims to enhance data protection and support the European Open Science Cloud initiative, promoting responsible data use across borders.
Copyright (c) 2025 Dr. Deborah Wiltshire, Dr. Simon Parker, Dr. Vanessa González Ribao https://creativecommons.org/licenses/by/4.0
Evaluating the efficacy and impact of a pilot programme for FAIR data stewardship at a UK university
Increasingly, funders, publishers, and institutions expect researchers to comply with the FAIR principles to ensure that data is findable, accessible, interoperable, and reusable. In an institutional context, however, questions remain as to how organisations can move beyond a broad commitment to FAIR, coupled with support for researchers to comply nominally with related grant conditions, to a more embedded and sustainable approach with a meaningful and pervasive impact on the FAIRness of research outputs. A data stewardship model offers one way to achieve this, yet in contrast to universities in mainland Europe and especially in the Netherlands, the UK is substantially lacking in such infrastructure at an institutional level, hampering efforts to evidence its potential impact within UK institutions and thereby advocate for its adoption. This article examines efforts to address this challenge via a recent project at the University of Sheffield to establish a pilot support service around FAIR data stewardship. It also provides a case study of how the benefits and impact of such an intervention might be identified and articulated through an evidence-led evaluation.
Copyright (c) 2025 Zuzanna Zagrodzka, Jenni Adams, Richard Campbell, Helen Foster https://creativecommons.org/licenses/by/4.0
Informed Consent Contexts in a Multidisciplinary Research Data Repository
Secondary use of research data requires an understanding of the contexts in which it was collected. While depositors are often encouraged to describe methodological and structural contexts in the form of metadata and documentation, ethical contexts have received much less attention. As open data mandates and an ethos of FAIR (findable, accessible, interoperable, reuseable) data proliferate across disciplines, participant consent for unknown future secondary uses of data is increasingly sought, even for minimal risk research. Terms of broad consent generally establish limitations on data reuse, but those limitations may not be clear when data are accessed via an open repository. The absence of these contexts increases the risk that secondary uses of data will be inconsistent with the expectations of original research participants and may place unnecessary burden on research ethics boards. This study examines the dataset records in a large, multidisciplinary data repository to determine the extent to which and how informed consent information is communicated to secondary users, and the degree to which conditions of access and use of data adhere to terms of informed consent. We identified all records published in Borealis: The Canadian Dataverse Repository between January 2022 and September 2024 containing individual-level human data. From those records, we analysed the frequency with which consent information was included and methods used to do so. We further compared terms of consent with the licensing, textual, and technological conditions placed on access and use of the data. Results indicate that informed consent contexts are infrequently provided alongside data and that access and use conditions align with terms of consent for a slim majority of the sample datasets. Based on these findings, we provide recommendations for the development of repository policy and guidelines that harmonise terms of consent and data use, the standardisation of language establishing access and use conditions, the adoption of metadata schema describing ethical contexts, and additional collaboration among data stewards and research ethics boards.
Copyright (c) 2025 Brian Jackson, Nurulamirah Azmi https://creativecommons.org/licenses/by/4.0
Starting with the Digital Doesn’t Make it Easier
As organizations continue to overwhelmingly abandon all forms of paper-based record keeping, libraries are still adapting to increased offers of born digital archival donations. Simple misunderstandings or disconnects between the units facilitating donations and maintaining born-digital collections creates pain-points for donor relations and can result in a lack of transparency over how their records may be processed. To facilitate better donor transparency and cross-area collaboration over born digital records, Special Collections and archives need comprehensive policies and shifts in training and collaboration paradigms. This paper analyses the intersections of born digital archiving, collection development polices, donor relations, human-supported AI tools, and digital records education within American academic libraries to propose a functional toolkit for born digital acquisitions. Unrealistic expectations of collection processing, retention, growth, and publication onto openly accessible platforms can quickly overwhelm a libraries’ digital collections’ team due to size, need for digital forensics work, copyright limitations, or other capacity-related issues. Intertwined within this discussion is an additional discourse over the need to carefully curate our digital spaces not only for practical cost reasons, but due to the environmental costs of massive data storage solutions. Through an analysis of the elements stated above, the paper will reflect on the need to integrate born digital materials into archival acquisition procedures and provide practical solutions to meet this need.
Copyright (c) 2025 Amanda Boczar http://creativecommons.org/licenses/by/4.0
Data Curation: Introducing a Competency Framework for the Social Sciences
Research data management includes more than the question how researchers handle their data. In the sense of the FAIR principles, it is also about the sustainable safeguarding and organized reusability of research data. For social science, data-intensive research, research data centers and their data curating staff are therefore becoming increasingly important: data curators usually take on curation-specific tasks such as data preparation, securing research data in suitable archival environments, ensuring data accessibility, and the related control of the conditions of data re-use by third parties. Hence, they are specialized in the entire data curation process and, in particular, take on tasks of archiving and providing research data for reuse. Although the standards of comprehensive research data management are becoming more and more specific, this trend has not yet arrived in the corresponding training and further education measures. As a result, there is a gap between the growing demands on data curators and the development of competencies in the field of research data management with a focus on data curation. The competency framework presented in this article is intended to help close this gap: based on a Data Curation Lifecycle Model, a competency framework has been developed to support the development of targeted training and continuing education programs in the field of data curation, the formulation of learning objectives, and the evaluation of the corresponding trainings. The article points out the necessity to advance the development of competencies for this field, illustrates the schematic substructure of the data curation lifecycle, describes the development as well as the central core elements of the presented competency framework and discusses its perspectives. Overall, this competence framework is aimed in particular at (future) data curators, or as a schematic basis for the training of the relevant personnel. The focus is primarily on the data-intensive discipline of social sciences, although large parts can certainly be adapted for other disciplines and the corresponding data curation. The competency framework and this companion article are thereby intended to assist in advancing the sustainable professionalization of the previously understudied competency field of data curation.
Copyright (c) 2025 Kathrin Behrens, Tatiana Kvetnaya http://creativecommons.org/licenses/by/4.0
ESRC Research Data Policy in a Changing Landscape
The Economic and Social Research Council (ESRC) is the UK’s largest funder of economic, social, behavioural, and human data science. ESRC research data policy is ‘intended to support ESRC grant holders who collect, produce and re-use data by defining researchers’ roles and responsibilities, as well as the roles and responsibilities of the ESRC and its data service providers.’ This paper reports on an independent review of this policy, commissioned and funded by the ESRC, and carried out by the authors, to make recommendations for an updated policy in the light of changes to both the data and legal/policy landscape. Following an initial scoping review, the study comprised an online survey of stakeholder views that was followed up by a series of focus groups, and an analysis of a sample of data management plans (DMPs). In this brief report, we concentrate on those aspects of our review, both in process and in substance, which are of most relevance to the data curation and data management community, and outline the next steps in the policy review process.
Copyright (c) 2025 Paul Allanson, Angela Daly, Awais Elahi, Alistair Geddes, Maeve Malone, Niamh Nic Daeid, Lucille Tetley-Brown http://creativecommons.org/licenses/by/4.0
Data Makers and Users' Views on Useful Paradata
Understanding and making data (re)usable requires adequate documentation of the data but also information on how it has been created, curated, manipulated and used, termed in data documentation literature as paradata. This paper reports results of a survey study (N=91) of data creating and (re)using archaeologists' views of what data creation, curation, manipulation and use related information (termed here as paradata) they consider important when they are working with data. Data makers' and users' perceptions align to a considerable degree. It is important to have an explanation of the original general context of data creation and knowing the purpose, procedures and methods of data making, analysis and documentation. The findings underline that there is a need to continue developing and testing ideas how to capture and document paradata, and to find ways how to help data makers adopt proven practices to facilitate paradata making. Simultaneously, it is crucial that the paradata aimed at facilitating data use is relevant for data users rather than, for instance, technical or administrative details considered useful primarily by data makers.
Copyright (c) 2025 Isto Huvila, Lisa Andersson, Olle Sköld, Ying-Hsang Liu http://creativecommons.org/licenses/by/4.0
Development of an Integrated Lifecycle of RDM Tools: Looking Back and Forward at KU Leuven
After the creation of an institutional research data management (RDM) policy some years ago, KU Leuven decided it was crucial to create an ecosystem of RDM tools for the entire RDM life cycle to enable KU Leuven researchers to manage their data well and in a FAIR way. In 2018, KU Leuven started on the road to an institutional data repository, which resulted in the launch of the Dataverse-based Research Data Repository (RDR) in January 2022. In line with this, there was a strong focus on tools to manage data in a structured and metadata-rich way during the research project, when data was still being worked on. Based on this, an Integrated Rule-Oriented Data System (iRODS) instance that would enable easy and integrated metadata management from the start of a research project via a self-developed portal was launched in March 2023 under the name “ManGO,” which is a shortened version of MANagement van Gegevens voor Onderzoek. Because one of the four FAIR principles is interoperability, and to fully enable FAIR data management, it was important to facilitate this interoperability of data by ensuring that KU Leuven RDM tools are interoperable, with a first focus being the connection between ManGO and RDR to select and copy data ready for publication. As with ManGO, RDR, the integration work, and other RDM developments made or adapted at KU Leuven, all relevant work will continue to be shared in open source where possible to ensure that investments that the university makes in RDM tool development can be used by and are accessible to other institutions.
Copyright (c) 2025 Dieuwertje Bloemen, Ingrid Barcena Roig https://creativecommons.org/licenses/by/4.0
In this paper, we report the results of a study examining 78 Research and Data Lifecycle (RDLC) models located in a review of the literature. Through synthesis-analysis and the nominal group technique, we investigated the RDLC models from the point of view of their disciplinary focus, use cases, model creators, as well as the specific stages and shapes. Our study revealed that the majority of the disciplinary focus for the models was generic, science, or multi-disciplinary. Models originating in the social sciences and humanities are less common. The use cases varied in a wide spectrum, with a total of 34 different scenarios. The creators and authors of the RDLC models came from more than 20 countries with the majority of the models created as a result of collaboration within or across different organizations. Our stage and shape analysis also outlined key characteristics of the RDLC models by showing the commonalities and variations of named stages and varying structures of the models. As one of the first empirical investigations examining the deep substance of the RDLC models, our study provides significant insights into the context and setting where the models were developed, as well as the details with regard to the stages and shapes, and thereby identified gaps that may impact the use and value of the models. As such, our study establishes a foundation for further studies on the practical utilization of the RDLC models in research data management practice and education.
Copyright (c) 2025 Jie Jiang, Danielle Maurici-Pollock, Rong Tang http://creativecommons.org/licenses/by/4.0
Reproducible preservation of databases through executable specifications
We propose a new preservation method for relational data and a corresponding tool. The method involves writing a specification that can later be executed by the tool without user interaction, transforming the input files and databases into an encapsulated package suitable for archiving. Thus, the transformation steps become reproducible, which facilitates automation by reusing the specifications and allows for an iterative process, where for each iteration the specification is extended or adjusted and then executed to check that the result is closer to fulfilling future access requirements.
Copyright (c) 2025 Ivar Rummelhoff, Thor Kristoffersen, Bjarte M. Østvold http://creativecommons.org/licenses/by/4.0
The Copyrights Chain in the Digital Curation Process
The internet era, open access movement, image sharing platforms, and mainly the rise of artificial intelligence (AI) emphasise the importance of providing moderated access and licences for the reuse of collections by memory institutes as a key step in the digital curation process. Trusting individuals to control their data and its (re)use was an important concern since the early establishment of the digital collections in 1998 at the University of Haifa Library. However, there were no specialised tools for keeping the owner’s copy of rights, which led to the use of the note field in the metadata record. This brief report will describe the experiences in a recent ongoing project to enrich the University of Haifa’s digital collections with clear copyright status, with preference to open Creative Commons licences to benefit the user and the owner’s experiences. The steps performed will be explored as part of the workflow designed for the ‘Which Copyrights’ project 2024–2025. The scope of the project includes approximately 80,000 bibliographic records and more than 200,000 digital representations, covering local cultural heritage data. Within 7 months, over 52,000 records containing approximately 100,000 digital objects have been processed and are available with the copyright’s representation notice online. In total, 25% of those records have been updated with CC BY 4.0. The success encouraged the design of the next step, to use a public platform to make the dataset available for the public. The Digital collection of the Younes & Soraya Nazarian Library, University of Haifa offers an open access portal, primarily used by the academic community. Therefore, visibility and usage will be expanded to a broader public, including via non-academic platforms, such as Wikipedia or Wikimedia. By sharing these plans, ideas, and ongoing actions, the library experience could be useful to other institutions that work with digital heritage collections, for the benefit of researchers and the broader public.
Copyright (c) 2025 Keren Barner https://creativecommons.org/licenses/by/4.0
Volumetric Video: Preservation and Curation Challenges of an Emerging Medium
Volumetric video is an emerging media format that uses multiple cameras to record live-action subjects and produce three-dimensional, time-based digital media. The resulting digital objects encode visual and spatial information, colour, textures, and sound in a format that allows for users to view the subject from any angle and use the assets in video games, virtual reality, augmented reality, or films. The technology has been pioneered by Hollywood production companies but is now being experimented with by digital humanities scholars. As it becomes more popular, information institutions, particularly academic libraries and others that support researchers, will likely need to support this new format throughout its lifecycle, which may draw on research data management, digital preservation, and repository services. This article introduces volumetric video capture, discusses some of its current applications outside of the commercial film industry, and outlines the curation and preservation challenges that this new media format presents. The paper compares two different production workflows that result in different output qualities: professional and prosumer studio-based workflows. The analysis explores the digital curation challenges that volumetric video raises within these workflows, with considerations for selection and appraisal criteria, file format sustainability, metadata requirements, legal/ethical considerations, and directions forward for future research in digital curation.
Copyright (c) 2025 Zack Lischer-Katz, Bryan Carter, Rashida Braggs http://creativecommons.org/licenses/by/4.0
Using Metadata to Promote Transparency in Health Research
Data sharing is a key strategy for fostering transparency, reproducibility, and trust in scientific research. Data sharing is endorsed and even required by many funders, such as the National Institutes of Health (NIH) in the United States. However, many NIH-funded projects face obstacles to data sharing, either to protect research participants’ privacy, safeguard proprietary data, or remain compliant with data use agreements. Yet event researchers who cannot openly share data still benefit from openness and transparency into one another’s work, and to making their own research more transparent where possible. The Social, Behavioral, and Economic COVID Coordinating Center at ICPSR (SBE CCC) has launched a new archive aimed at addressing these challenges within the domain of social, behavioral, and economic (SBE) research into the COVID-19 pandemic. In September 2023, SBE CCC launched the COVID measures archive with the dual goals of a) offering researchers the ability to compare measures across SBE studies of COVID while b) protecting contributors’ needs for privacy and confidentiality in health research. The COVID measures archive primarily holds variable-level metadata, which provides visibility into the individual variables and measures employed in studies without necessitating the sharing of confidential or restricted data. This brief report describes the features of the COVID measures archive and illustrates how it can be used to foster transparency and consistency across SBE COVID studies.
Copyright (c) 2025 Megan Chenoweth, John Kubale http://creativecommons.org/licenses/by/4.0
Managing Retractions and their Afterlife: A Tripartite Framework for Research Datasets
Retractions serve as a critical, albeit last-resort, post-publication correction mechanism in scholarly publishing, playing an important role in upholding the integrity of the scientific record. By formally retracting flawed or misleading research, the scientific community mitigates the harm caused by errors or misconduct that may have escaped detection during peer review. While retractions of research articles have been extensively discussed across scientific disciplines and are well-integrated into most publishers' workflows, the retraction of research datasets remains underexplored and rarely implemented. This paper seeks to address this gap by reviewing recent developments in this area, analyzing a sample of publicly available retracted dataset records considering existing recommendations and guidelines, and putting forward a few points for discussion—particularly for cases where datasets have been published and correction is no longer feasible, or when all efforts to amend the dataset have been exhausted. These considerations are framed into three main categories: (1) preventive actions and timely response, (2) purposeful damage control, and (3) community engagement and shared standards. Although still preliminary, this framework aims to help entertain future debates and inform actionable strategies for addressing the unique challenges of managing retracted datasets where scientific rigor has been compromised. By contributing to the discussion on dataset retractions, this work seeks to better equip data curators, repository managers, and other stakeholders with tools to enhance accountability and transparency throughout the data preservation process, while also helping to mitigate the error cascade effect in science.
Copyright (c) 2025 Renata Curty http://creativecommons.org/licenses/by/4.0
Supporting the Research Data Management Journey of a Postgraduate Student at the University of St Andrews
Most research funders have requirements for data management plans and open data to foster good research data management practices. In order to embed these practices in the postgraduate research (PGR) student journey we have introduced the requirement for a data management plan as part of the first-year progress review and the encouragement to make data underpinning theses publicly available. To support students through these processes we provide a suite of training workshops and are available for one-to-one consultations. User feedback and frequently asked questions are used to review and improve our support offering. This brief report discusses the planning and implementation processes for data management plan requirement and encouragement of underpinning data. It dives deeper into the workflows, especially for the data deposit, and describes training and support available to students. Statistics on training uptake, data management plan submissions and annual trends for data deposit are also presented. The report concludes with lessons learnt and the team’s plans for the near future.
Copyright (c) 2025 Federica Fina, Haley Eckel, Panagiota Spanou, Jackie Proven http://creativecommons.org/licenses/by/4.0
The Standardized Data Management Plan for Educational Research
Although there is an increasing number of tools and support opportunities, research data management is still challenging. Conventional templates of data management plans (DMP) guide users, but hardly support them in implementing and realizing data management. Instead, users of conventional templates require more tailored guidance to better understand how to manage their data according to the needs of their research discipline, and its methods and practises, e.g., regarding data sharing. To provide more tailored, discipline-specific guidance, Science Europe (2018) suggests developing and using so-called Domain Data Protocols, i.e., a model DMP for a given discipline or community. The project Domain Data Protocols for Empirical Educational Research was one of the first to turn this concept into a practically useable DMP template tailored to educational research by developing the Standardised Data Management Plan for Educational Research (Stamp). The Stamp is designed to assist researchers in managing their data, appropriately, and to ensure shareable data according to the FAIR Data Principles. Due to its flexible structure, its checklist and auxiliary materials, the Stamp tackles most of the challenges of conventional DMP templates. Providing tailored, discipline-specific guidance and enabling to manage various types of data, the Stamp is an innovative approach to further professionalize data management.
Copyright (c) 2025 Sebastian Netscher, Harald Kaluza, Reiner Mauer, Kati Mozygemba, Karsten Stephan http://creativecommons.org/licenses/by/4.0
From the Research Cycle to the People Cycle: Humanizing Digital Curation
In this paper, we argue that digital curators need to move beyond explaining technical jargon and workflows to collaborate with researchers to meet them where they are at when educating them on digital curation skills. At Mississippi State University, we teach students, staff, and faculty about digital curation by connecting it to the skills they already have and cut out complicated jargon to get to what really matters: knowing how to manage data so it can be understood and used. We focus our pedagogy on strengthening the curation skills they already have and bringing in newer concepts by framing them within areas they are already familiar with. We hope that by adding a people lifecycle to the research lifecycle, we can empower our researchers to be proactive in digital curation and feel confident to continue developing these skills.
Copyright (c) 2025 Lauren Geiger, Kathryn New, Carolina M. Siniscalchi https://creativecommons.org/licenses/by/4.0
TROV - A Model and Vocabulary for Describing Transparent Research Objects
The Transparent Research Object Vocabulary (TROV) is a key element of the Transparency Certified (TRACE) approach to ensuring research trustworthiness. In contrast with methods that entail repeating computations in part or in full to verify that the descriptions of methods included in a publication are sufficient to reproduce reported results, the TRACE approach depends on a controlled computing environment termed a Transparent Research System (TRS) to guarantee that accurate, sufficiently complete, and otherwise trustworthy records are captured when results are obtained in the first place. Records identifying (1) the digital artifacts and computations that yielded a research result, (2) the TRS that witnessed the artifacts and supervised the computations, and (3) the specific conditions enforced by the TRS that warrant trust in these records, together constitute a Transparent Research Object (TRO). Digital signatures provided by the TRS and by a trusted third-party timestamp authority (TSA) guarantee the integrity and authenticity of the TRO. The controlled vocabulary TROV provides means to declare and query the properties of a TRO, to enumerate the dimensions of trustworthiness the TRS asserts for a TRO, and to verify that each such assertion is warranted by the documented capabilities of the TRS. Our approach for describing, publishing, and working with TROs imposes no restrictions on how computational artifacts are packaged or otherwise shared, and aims to be interoperable with, rather than to replace, current and future Research Object standards, archival formats, and repository layouts.
Copyright (c) 2025 Meng Li, Timothy McPhillips, Craig Willis, Nikolaus Parulian, Bertram Ludäscher, Kacper Kowalik, Lars Vilhuber, Thu-Mai Lewis, Mandy Gooch http://creativecommons.org/licenses/by/4.0
Data Management Plans: a Resource to Shape Institutional Data Management Services
At KU Leuven, a university in the Flemish region of Belgium, data management plans have become an important resource to drive and shape the development of data management support, services, and training. With 8,000 researchers and 7,000 PhD students in fundamental and applied research across a comprehensive range of disciplines, KU Leuven is the largest university in Belgium. Public research funding is provided by the federal and regional governments, mainly via the Research Foundation Flanders (FWO) and via research funding allocated to universities based on excellence criteria through the Special Research Fund (BOF) and the Industrial Research Fund (IOF). Since 2018, FWO and BOF-IOF incorporated data management into their policies, requiring researchers to submit Data Management Plans (DMPs) to their institutional research office. Since then, the number of DMPs that are developed each year has increased exponentially, from 150 in 2018 to nearly 700 per year now. The Research Coordination Office at KU Leuven decided to review all DMPs to provide feedback to ensure high-quality plans. To manage the submission, monitoring, review, and preservation of this volume of DMPs efficiently, an online platform was developed that is integrated with the university’s research information systems. Initially, the focus of the DMP review was on supporting the development of DMPs, as this was a new concept for researchers. The review process has significantly improved the quality of DMPs. Later, support shifted to provide advice on best practices in data management. Reviews of over 2600 DMPs provide a rich source of information to develop services and training. Based on findings from DMP reviews, the IT department developed an interactive storage guide; ethical and legal compliance in research projects can be monitored; new data management training modules are developed; and a collection of example DMPs has been developed. In addition, the growing DMP collection is a rich source of information on researchers’ data practices, providing the baseline information to develop further services. Future plans include implementing artificial intelligence in DMP reviews to automate problem detection and exploring machine-actionable DMPs for an institutional data register.
Copyright (c) 2025 Willeke de Haan, Veerle Van den Eynden http://creativecommons.org/licenses/by/4.0
In October 2020, a data archive for social sciences and digital humanities was launched at the Belgian national archives institution, the State Archives of Belgium. This new infrastructure, called the Social Sciences and Digital Humanities Archive (SODHA), is meant to accrue and make available datasets in any of the disciplines that constitute social sciences and digital humanities. To support this endeavor, the online platform relies on the open-source software for digital repository management Dataverse, developed by the Institute for Quantitative Social Science (IQSS) of Harvard University. IQSS have made it possible to customise installations of their software to a large extent, especially the formal, outer aspects. This makes for interesting design choices, not just for branding an installation but also to translate any organisation’s communication strategy with both broad and minute modifications. We contend that working on the design—that is, the formal aspects of an application—does not amount to “cosmetic” work; rather, it empowers the administrators of a platform to shape their front end so that it best conveys what its core functions and purposes—in other words, its content—are all about. We present the context in which SODHA was launched, how we elaborated a particular communication strategy calibrated for researchers, and the ensuing design choices. Examples of changes and adaptations are provided and contextualised.
Copyright (c) 2025 Benjamin Peuch, Laura Van den Borre, Elias Kruithof, Jens Doms, Jean-Paul Sanderson, Youssef Ouahalou, Johan Van der Eycken, Rolande Depoortere https://creativecommons.org/licenses/by/4.0
The “Event Notifications in Value-Adding Networks” specification provides an interoperable fabric that can be used in scholarly communication to exchange messages among data nodes that make scholarly artifacts available to the network and service nodes that add value to these artifacts. For example, a data repository can have a request-response conversation with a long-term archive that results in the latter relaying the coordinates of an archived version of the dataset to the repository. The push-oriented notification protocol is based on W3C Recommendations, both regarding the messaging protocol and payloads. Implementations of the protocol are in various stages of maturity, the most advanced being the COAR Notify effort that focuses on overlay peer review as a service. An important consequence, and actual design goal, of the conversational interoperability approach, is the ability it provides to bi-directionally interlink the scholarly artifact and the service result in real-time, providing an attractive alternative to current interlinking approaches that by and large are heuristic-based and generate results with significant delays. Another consequence is the ability to publish an Event Log for each scholarly artifact that lists all event notifications that were exchanged about it, providing full transparency about its entire life cycle, including where and how it was registered, archived, reviewed, commented upon, etc. This paper describes essential aspects of the Event Notification protocol and illustrates it using a scenario. It then describes the Event Logs concept and illustrates it by means of that same scenario. It then gives an overview of challenges related to specifying Event Logs that are currently under investigation and largely relate to equipping them with affordances to make them verifiable and trustworthy.
Copyright (c) 2025 Patrick Hochstenbach, Ruben Verborgh, Herbert Van de Sompel http://creativecommons.org/licenses/by/4.0
In Sharing We Trust. Taking Advantage of a Diverse Consortium to Build a Transparent Data Service in Catalonia
The Consorci de Serveis Universitaris de Catalunya (CSUC) is a consortium that serves 13 universities and 33 research centers in Catalonia and neighboring communities. In 2017 the Consortium created an Open Science department to collaborate with universities and research centers on facilitating the adoption of Open Science requirements. Even though CSUC also offers services to researchers directly (for example, its supercomputing resources), this report will focus on CSUC’s work with its member institutions to create and offer data management services. We will explain how CSUC has led the creation of a robust shared governance system, and how it takes advantage of the diversity of its members to create useful, high quality, and transparent services for all researchers in the Catalan research system. Through sharing each other’s experiences, values and priorities, the result is better than separate ad-hoc solutions. The process also creates a community of practitioners that develop expertise together with the help of professional development opportunities organized by CSUC, like recurrent self-learning labs focused on data curation tools, techniques and processes.
Copyright (c) 2025 Clara Llebot, Mireia Alcalá, Lluís M. Anglada i de Ferrer http://creativecommons.org/licenses/by/4.0
Researchers and Research Data: Improving and Incentivising Sharing and Archiving
There has been a lot of discussion within the scientific community around the issues of reproducibility in research, with questions being raised about the integrity of research due to failure to reproduce or confirm the findings of some of the studies. Researchers need to adhere to the FAIR (findable, accessible, interoperable, and reusable) principles to contribute to collaborative and open science, but these open data principles can also support reproducibility and issues around ensuring data integrity. This article uses observations and metrics from data sharing and research integrity related activities, undertaken by a Research Integrity and Data Specialist at the Francis Crick Institute, to discuss potential reasons behind a slow uptake of FAIR data practices. We then suggest solutions undertaken at the Francis Crick institute which can be followed by institutes and universities to improve the integrity of research from a data perspective. One major solution discussed is the implementation of a data archive system at the Francis Crick Institute to ensure the integrity of data long term, comply with our funders’ data management requirements, and to safeguard our researchers against any potential research integrity allegations in the future.
Copyright (c) 2025 Minna Ventsel, Beth Montague-Hellen http://creativecommons.org/licenses/by/4.0
Realising Open Data Principles In UK Research Institutions
We report on the state of open research data (ORD) policy and practice across UK research institutions through the STAR (Sustainable & TrAnsparent Research data) project. Through qualitative interviews, focus groups, and workshops involving 52 university staff across 21 UK institutions, we investigated the progress and challenges in ORD practices since 2016 publication of the Concordat on Open Research Data. We observed that while institutions have made progress establishing ORD specialist roles, developing policies, and creating repository infrastructures, systematic monitoring processes and widespread adoption remain stalled. Key challenges include capacity constraints in institutional repositories, limited workload recognition, insufficient funding for long-term archiving, and varying disciplinary interpretations of ORD relevance. Based on workshops with participants, we recommend recognition of ORD in academic career frameworks, development of disciplinary-relevant data sharing practices, improved infrastructure for monitoring ORD practices, and enhanced support for external disciplinary repositories. The study emphasizes the need for a values-driven rather than compliance-driven approach to ORD implementation, calling for deeper engagement with diverse academic communities to ensure ORD requirements remain meaningful and relevant across disciplines. These findings provide insights for research institutions and funding bodies in developing more effective and inclusive ORD policies.
Copyright (c) 2025 Pen-Yuan Hsing, Jessica Wheeler, Lorna Duncan, Rosalind Strang, Neil Jacobs http://creativecommons.org/licenses/by/4.0
Two Decades, Same Story? Insights and Future Directions in Long Tail Data Curation
This paper examines the evolution of the concept of long tail research data in the scholarly literature. The “long tail” concept, originally used to describe “niche” digital products that have a significant market share when taken as an aggregate, was first applied to research data in 2007 to refer to a vast array of smaller, heterogeneous data collections that cumulatively represent a substantial portion of scientific knowledge. These datasets are frequently overlooked due to inadequate data management practices and institutional support. Bridging the discussions on data curation in library & information science (LIS) and domain-specific contexts, this paper identifies several themes in these discussions and offers insights, or provocations, that encourage researchers to rethink the existing frameworks and methods and find new approaches that would help both researchers and data professionals. This review seeks to enhance understanding of long tail data as both a concept and a field, while also informing current and future research and practice.
Copyright (c) 2025 Inna Kouper, Gretchen Stahlman http://creativecommons.org/licenses/by/4.0
Developing Specialized Data Curation Curricula to Meet Growing Demands: A Community-based and Evolving Approach
Data curation is “the encompassing work and actions taken by curators of a data repository in order to provide meaningful and enduring access to data” (Johnston et al., 2018a). It can be multifaceted and complex based on the types of data, the expertise of the curator, disciplinary expectations, and repository policies. With evolving data sharing practices and standards, ensuring data curators and stewards have access to high-quality, extensible instruction on specific data types is essential for supporting the goals of open research and accessible data sharing, particularly in the landscape where funders (National Institutes of Health, 2020) and journals (Naughton & Kernohan, 2016) are mandating data publication for the purpose of reproducibility, reuse, and external validation. In brief, data need to be curated for effective re-use and in alignment with the FAIR principles (Wilkinson et al., 2016). The Data Curation Network (DCN) (Johnston et al., 2018b) has been actively developing education and training programs to expand capacity in data curation along multiple axes. This paper will explore the progression of the DCN’s education program based primarily within the United States, highlighting a recent effort to develop specialized data curation education for four specialized data types. We will conclude with lessons learned, reflections on growth of education efforts in the DCN more broadly, and potential next steps.
Copyright (c) 2025 Sophia Lafferty-Hess, Seth Erickson, Neggin Keshavarzian, Wanda Marsolek, Jennifer Moore, Mikala Narlock http://creativecommons.org/licenses/by/4.0
Traveling on the same road while navigating different terrain: Institutional data services and repositories across the US
This paper describes the Repository Readiness initiative and subsequent Summit for Academic Institutional Readiness in Data Sharing (STAIRS). These efforts examined the current state of institutional research data services and repositories at US academic institutions. Using federal memos and directives published in 2022 as a foundation, members of the Data Curation Network hosted a virtual learning series to identify areas of collaboration across institutions. The themes of this learning series led to STAIRS, which brought representatives from 32 institutions, to discuss the need for and potential benefits of deeper institution-wide engagement and increasing cross-institutional collaborations to support research data sharing efforts. Through discussions at the summit, three key themes emerged: difficulties in scaling services, need for shared resources and training materials, and importance of cross-institutional collaboration. The participant discussions, combined with data from pre- and post-summit surveys, suggest gaps in staffing, resources, and formal policies in data management in institutional settings. The authors conclude with recommendations for funding agencies to support institutional data services through incentives for collaboration, improved communication with program officers, and additional research into data sharing requirements across different institutional types.
Copyright (c) 2025 Mikala Narlock, Jake Carlson, Wind Cowles, Leslie Delserone, Joel Herndon, Wendy Kozlowski, Jonathan Petters http://creativecommons.org/licenses/by/4.0
From Building a First-Generation Digital Library Infrastructure to Reimagining Discovery
Twenty-five years ago, Harvard University was in the early stages of a project to build a first-generation digital library infrastructure. The project was carefully named the Library Digital Initiative (LDI), signifying that ‘digital’ would be an integral and integrated aspect of ‘library’ and not a separate entity. The initiative aimed to develop knowledge and expertise relating to digital objects, as well as technical infrastructure to create, curate, access and preserve them, and to integrate the new digital collections with Harvard’s extensive tangible collections. Today, we still benefit from the foresight of this first-generation development and the subsequent ones it spawned, but we are also at a pivotal point of reflecting on lessons learned and opportunities to be seized as we rebuild and reimagine our digital infrastructure and services in a vastly expanded data ecosystem. Predicting what libraries will look like two decades ahead is always conjecture. What we do know, however, is that while the themes and challenges from the past two decades endure, the way we are tackling them is different. This paper examines what has changed since early library digital initiatives, and the imperatives we see for the future.
Copyright (c) 2025 Stuart Snydman, Martha Whitehead http://creativecommons.org/licenses/by/4.0
Making reproducibility a reality by 2035?
This paper describes a project which identified practical and pragmatic ways to increase the FAIRness and reproducibility of published research. Academic journals have supported Open Science through the implementation of data sharing policies for over ten years; some evidence has since emerged on the additional time, resources and expertise that policy enforcement requires as part of an editorial workflow. A series of publisher workshops facilitated by the EC-funded TIER2 project aimed to identify the key checks needed to enforce strengthened journal data sharing policies and to understand which editorial roles have the capacity to undertake such enforcement. The intended outcome of this work was to establish the workflows and resourcing which can support academic journals to enforce stronger data sharing policies in future.
Copyright (c) 2025 Rebecca Taylor-Grant, Matthew Cannon, Allyson Lister, Susanna-Assunta Sansone http://creativecommons.org/licenses/by/4.0
Improving the Transparency of Data Access Conditions in the SSH Domain
The benefits of making data available for reuse are recognised by many. While some datasets can be openly available, others contain sensitive or personal data that needs to be protected. To allow the sharing of the latter datasets, many trustworthy digital repositories provide options to publish data restricted access. However, detailed standardised information about the access conditions for these restricted access datasets is often lacking from the metadata. Researchers interested in reusing these datasets can thus not judge whether they are eligible to reuse data and under what conditions. To get a better understanding of how we can increase the transparency of access conditions, this paper aimed to investigate the access conditions and procedures that are commonly applied by depositors within the (Dutch) Social Sciences and Humanities (SSH) research community. The results of a survey that was conducted (n=45) indicated that various conditions are applied, and while some datasets can have few restrictions, for others reuse is highly restricted. Most respondents limit reuse to research purposes and prohibit commercial use. Some datasets are available for students or teaching, but often with additional requirements. A large majority of respondents required a motivation letter to evaluate before allowing reuse. Notably, respondents often chose ‘It depends’ when asked whether a specific condition was applied, showing a lot of nuances in the conditions and the evaluation of access requests. An important result from our survey was that clear procedures and decision-making guidelines seem to be lacking for many respondents. Requests are often evaluated ad-hoc and through email. Decisions are said to require an evaluation of the quality of the application, yet the evaluation criteria seem to be rarely specified and explicitly communicated at the time of data deposit. Based on the results of the small-scale survey, we conclude with a set of six recommendations directed at data owners and repositories outlining how information about access conditions and procedures can be made more transparent in the future. This work should be seen as a starting point to improve the accessibility and reusability of restricted access datasets in the SSH domain.
Copyright (c) 2025 Deborah Thorpe, Ricarda Braukmann, Angelica Maineri http://creativecommons.org/licenses/by/4.0