Category: Open Science

We live in an age of unprecedented opportunities to use existing data for tasks not anticipated when those data were collected, resulting in widespread data repurposing. This commentary defines and maps the scope of data repurposing to highlight its importance for organizations and society and the need to study data repurposing as a frontier of data management. We explain how repurposing differs from original data use and data reuse and then develop a framework for data repurposing consisting of concepts and activities for adapting existing data to new tasks. The framework and its implications are illustrated using two examples of repurposing, one in healthcare and one in citizen science. We conclude by suggesting opportunities for research to better understand data repurposing and enable more effective data repurposing practices.

https://www.arxiv.org/abs/2506.09073

| Artificial Intelligence |
| Research Data Curation and Management Works |
| Digital Curation and Digital Preservation Works |
| Open Access Works |
| Digital Scholarship |

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.

https://doi.org/10.2218/ijdc.v19i1.889

| Artificial Intelligence |
| Research Data Curation and Management Works |
| Digital Curation and Digital Preservation Works |
| Open Access Works |
| Digital Scholarship |

Developing research data management (RDM) services has become an international trend in response to the movement promoting open science. There is an urgent need to establish support systems for universities and research institutions to strengthen governance. However, the diversity of RDM services and the absence of a universally applicable model create challenges in implementation. To address this, we propose a maturity model for organizational RDM services. By analyzing existing RDM service maturity models, we extract six key dimensions —awareness, data policy, budget, services, user needs, and IT infrastructure—and develop a structured evaluation framework with a five-level rating system. The model is validated through a step-by-step approach: author evaluation, domain evaluation, and practical setting evaluation via a national survey of Japanese institutions. The results demonstrate the model’s applicability across institutions of varying sizes and types, enabling RDM managers to quantitatively assess service maturity and compare progress against national benchmarks. Furthermore, we discuss the potential value and utilization of the framework through two case studies. This study provides an organizational benchmark for RDM services that is applicable to institutions of diverse sizes and natures. It also helps identify issues in the future implementation of organizational RDM services and highlights priority areas for investment.

https://doi.org/10.5334/dsj-2025-018

| Artificial Intelligence |
| Research Data Curation and Management Works |
| Digital Curation and Digital Preservation Works |
| Open Access Works |
| Digital Scholarship |

Open Science contributes to the collective building of scientific knowledge and societal progress. However, academic research currently fails to recognise and reward efforts to share research outputs. Yet it is crucial that such activities be valued, as they require considerable time, energy, and expertise to make scientific outputs usable by others, as stated by the FAIR principles. To address this challenge, several bottom-up and top-down initiatives have emerged to explore ways to assess and credit Open Science activities (e.g., Research Data Alliance, RDA) and to promote the assessment of a broad spectrum of research outputs, including datasets and software (e.g., Coalition for Advancing Research Assessment, CoARA). As part of the RDA-SHARC (SHAring Rewards and Credit) interest group, we have developed a set of recommendations to help implement various rewarding schemes at different levels. The recommendations target a broad range of stakeholders. For instance, institutions are encouraged to provide digital services and infrastructure, organise training and cover expenses associated with making data available for the community. Funders should establish policies requiring Open Access to data produced by funded research and provide corresponding support. Publishers should favour open peer-review models and Open Access to articles, data, and software. Government policymakers should set up a comprehensive Open Science strategy, as recommended by UNESCO and followed by a growing number of countries. The present work details different measures that are proposed to the stakeholders. The need to include sharing activities in research evaluation schemes as an overarching mechanism to promote Open Science practices is specifically emphasised.

https://doi.org/10.5334/dsj-2025-015

| Artificial Intelligence |
| Research Data Curation and Management Works |
| Digital Curation and Digital Preservation Works |
| Open Access Works |
| Digital Scholarship |

Building on their extensive expertise as both scholars and developers of methods for data circulation, Christine Borgman and Paul Groth (2005) highlight the crucial impact of sociotechnical dimensions in shaping attempts to bridge the distance between data creators and users, thereby making it possible to transfer and develop knowledge across contexts and domains. . . .

In this commentary, I take issue with one assumption that underlies Borgman and Groth’s arguments; that is, the idea that data reuse requires the sharing of data, and that transparency is therefore a key principle guiding data work, including the practices of data formatting, cleaning, filtering, modeling, curation, and visualization carried out by knowledge intermediaries. By contrast, I argue that data distance is sometimes so large and fraught with challenges, that a better way to facilitate data reuse is to employ intelligent methods of data governance and interpretation that do not involve the sharing of data. I focus on two such methods in this commentary: mining algorithms facilitating data analysis (sometimes also called ‘data visiting’ methods) and narratives (‘data stories’) forged to contextualize and interpret data in specific ways. These methods have a key characteristic in common: they require the explicit articulation of specific visions for prospective data use, thereby moving away from the quest to open data to any possible usage, and rather placing emphasis on the need to account for how choices are made when circulating data end up affecting—and, indeed, constraining—the interpretation of such data in new contexts. In this sense, these methods foster responsible approaches to data reuse, which take account of and potentially help address the scientific and social challenges involved in bridging data distance, while at the same time recognizing that there is no such thing as ‘neutral’ data processing. All data work unavoidably encompasses human judgments around how data may or may not be used, what phenomena they can help study, and how their interpretation may inform knowledge and decision-making; the best one can do to facilitate reliable data interpretation is to make such judgments explicit. I conclude that data management discussions need to move away from simplistic reliance on data sharing and related notions of transparency focused on disclosure (Elliott,2022; Rappert, 2025) and invest instead in skills, methods, and training to foster strategic forms of data mining and storytelling.

https://doi.org/10.1162/99608f92.e95b5c26

| Artificial Intelligence |
| Research Data Curation and Management Works |
| Digital Curation and Digital Preservation Works |
| Open Access Works |
| Digital Scholarship |

Sharing research data is necessary, but not sufficient, for data reuse. Open science policies focus more heavily on data sharing than on reuse, yet both are complex, labor-intensive, expensive, and require infrastructure investments by multiple stakeholders. The value of data reuse lies in relationships between creators and reusers. By addressing knowledge exchange rather than mere transactions between stakeholders, investments in data management and knowledge infrastructures can be made more wisely. Drawing upon empirical studies of data sharing and reuse, we develop the metaphor of distance between data creator and data reuser, identifying six dimensions of distance that influence the ability to transfer knowledge effectively: domain, methods, collaboration, curation, purposes, and time and temporality. We explore how social and socio-technical aspects of these dimensions may decrease – or increase – distances to be traversed between creators and reusers. Our theoretical framing of the distance between data creators and prospective reusers leads to recommendations to four categories of stakeholders on how to make data sharing and reuse more effective: data creators, data reusers, data archivists, and funding agencies. ‘It takes a village’ to share research data – and a village to reuse data. Our aim is to provoke new research questions, new research, and new investments in effective and efficient circulation of research data, and to identify criteria for investments at each stage of data and research life cycles.

https://doi.org/10.1162/99608f92.35d32cfc

| Artificial Intelligence |
| Research Data Curation and Management Works |
| Digital Curation and Digital Preservation Works |
| Open Access Works |
| Digital Scholarship |

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.

https://doi.org/10.2218/ijdc.v19i1.1052

| Artificial Intelligence |
| Research Data Curation and Management Works |
| Digital Curation and Digital Preservation Works |
| Open Access Works |
| Digital Scholarship |

This chapter elaborates on several fields of electronic health data (EHD) reuse in healthcare, mainly for public interest reasons. Real-life examples of EHD reuse in epidemiology, including insights into how EHD is applied in surveillance and occupational health, are provided in the first section. The second section elaborates how EHD can be reused in supporting institutional activities and policy making: project examples carried out by eminent health institutions around the globe, such as the global World Health Organization (WHO), the continental European Centre for Disease Prevention and Control (ECDC), the American Centres for Disease Control and Prevention (CDC), and some regional institutions, such as the National Institute for Health and Care Excellence (NICE), are illustrated. The third section explores the application of EHD reuse for improving healthcare systems and for carrying out research activities. Specifically, some of the related areas covered include how EHD can be reused in learning healthcare systems, how to advance personalized medicine, how to improve healthcare quality and safety, and how to carry out various research activities. Finally, the fourth section is dedicated to the reuse of EHD for the artificial intelligence (AI) market, which has been experiencing an expansion in healthcare, addressing relevant topics such as administrative costs and associated burden reduction but also training and developing innovative AI-based tools for telemedicine to identify patients at risk for other reuses.

https://doi.org/10.1007/978-3-031-88497-9_2

| Artificial Intelligence |
| Research Data Curation and Management Works |
| Digital Curation and Digital Preservation Works |
| Open Access Works |
| Digital Scholarship |

This work discusses reuse of chemical data across disciplines and the role of various data initiatives and projects including PARC, NORMAN-SLE, MassBank, WorldFAIR, PSDI and NFDI4Chem to facilitate increased data sharing. Improved machine-readable chemical data supports global research and interdisciplinary methodologies crucial for sustainable development and achievement of UNESCO’s Open Science priorities and the UN Sustainability Development Goals. Examples of success and ongoing approaches include integrating toxicology and chemical exposure data using ontologies, linking specialised chemical data collections with larger repositories such as PubChem, and developing IUPAC International Chemicals Identifier (InChI) extensions for nanomaterials and mixtures. National data infrastructure projects in the UK and Germany focus on digitising and standardising chemical research data management workflows, aiding scientists in data collection, storage, processing, analysis, disclosure, and reuse. These global initiatives aim to enhance chemical data interoperability to solve real-world problems, foster collaboration, and promote innovation while considering sustainable data resources beyond individual projects.

https://doi.org/10.1515/ci-2025-0203

| Artificial Intelligence |
| Research Data Curation and Management Works |
| Digital Curation and Digital Preservation Works |
| Open Access Works |
| Digital Scholarship |

The benefits of making data available for reuse are recognized 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 standardized information about the access conditions for these restricted access datasets are 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 researchers and infrastructure providers 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.

https://doi.org/10.2218/ijdc.v19i1.1048

| Artificial Intelligence |
| Research Data Curation and Management Works |
| Digital Curation and Digital Preservation Works |
| Open Access Works |
| Digital Scholarship |

Research data management policies are ubiquitous in UK Higher Education Institutions, and are often written and managed by, or with, the library team. RDM policies attempt to balance the requirements of keeping data safe and secure when necessary and opening up data to allow reuse and to support research integrity. This article uses a framework analysis approach on 134 policies to investigate whether the UK RDM policies have become more open over time in terms of policy points and language. The investigation shows that recent policies have shown an increased likelihood of being more open in several areas: how long data should be archived for, sharing of software, and the mandatory inclusion of data availability statements in journal articles. Language around FAIR data terms have increased, as has using research integrity as a key reason to manage data according to best practices.

https://doi.org/10.31235/osf.io/gd4hp_v1

| Artificial Intelligence |
| Research Data Curation and Management Works |
| Digital Curation and Digital Preservation Works |
| Open Access Works |
| Digital Scholarship |