“Understanding and Improving Data Repurposing”


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

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“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.

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

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“Linking Data Citation to Repository Visibility: An Empirical Study”


In today’s data-driven research landscape, dataset visibility and accessibility play a crucial role in advancing scientific knowledge. At the same time, data citation is essential for maintaining academic integrity, acknowledging contributions, validating research outcomes, and fostering scientific reproducibility. As a critical link, it connects scholarly publications with the datasets that drive scientific progress. This study investigates whether repository visibility influences data citation rates. We hypothesize that repositories with higher visibility, as measured by search engine metrics, are associated with increased dataset citations. Using OpenAlex data and repository impact indicators (including the visibility index from Sistrix, the h-index of repositories, and citation metrics such as mean and median citations), we analyze datasets in Social Sciences and Economics to explore their relationship. Our findings suggest that datasets hosted on more visible web domains tend to receive more citations, with a positive correlation observed between web domain visibility and dataset citation counts, particularly for datasets with at least one citation. However, when analyzing domain-level citation metrics, such as the h-index, mean, and median citations, the correlations are inconsistent and weaker. While higher visibility domains tend to host datasets with greater citation impact, the distribution of citations across datasets varies significantly. These results suggest that while visibility plays a role in increasing citation counts, it is not the sole factor influencing dataset citation impact. Other elements, such as dataset quality, research trends, and disciplinary norms, also contribute significantly to citation patterns.

https://arxiv.org/abs/2506.09530

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“A New Book From ACRL: The Open Science Cookbook; Open Access Edition Available”


“The Open Science Cookbook” provides a wide variety of lesson plans and learning activities for supporting collaborative, transparent, openly accessible, and reproducible research. In five sections, it has something for beginners to more advanced practitioners and for different audience sizes.

  • Program Development
  • Instruction
  • Outreach
  • Events
  • Collaborations and Partnerships . . . .

“The Open Science Cookbook” is available for purchase in print through the ALA Online Store and Amazon.com; by telephone order at (800) 621-2736 or (773) 702-7010; and as an open access edition.

https://tinyurl.com/569s7e46

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“Preservation and Digital Repositories: Connections, Possibilities, and Needs”


This chapter aims to explore certain aspects of the challenges of digital preservation and digital repositories, including their roles, significance, and associated costs. . . . Beginning with a necessary delineation of the relationship between digital preservation, digital repositories, and their digital assets, the chapter proceeds to conduct a brief analysis of the perceived needs for these components. These needs primarily encompass organizational aspects (policy, planning, actions), financial considerations (costs), and technological factors (standardization) crucial for supporting digital preservation and repositories.

https://tinyurl.com/5y5bfbdr

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“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.

https://doi.org/10.2218/ijdc.v19i1.1062

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“Request of Endocrinology and Metabolism Journals for Data Sharing Statements in Clinical Trial Reports: A Survey”


Background: To enhance reproducibility and transparency, the International Committee of Medical Journal Editors (ICMJE) required that all trial reports submitted after July 2018 must include a data sharing statement (DSS). Accordingly, emerging biomedical journals required trial authors to include a DSS in submissions for publication if trial reports were accepted. Nevertheless, it was unclear whether endocrinology and metabolism journals had this request for DSS of clinical trial reports. Therefore, we aimed to explore whether endocrinology and metabolism journals requested DSS in clinical trial submissions, and their compliance with the declared request in published trial reports.

Methods: Journals that were from the category of “Endocrinology & Metabolism” defined by Journal Citation Reports (JCR, as of June 2023) and published clinical trial reports between 2019 and 2022, were included for analysis. The primary outcome was whether a journal explicitly requested a DSS in its manuscript submission instructions for clinical trials, which was extracted and verified in December 2023. We also evaluated whether these journals indeed included a DSS in their published trial reports that were published between December 2023 and May 2024.

Results: A total of 141 endocrinology and metabolism journals were included for analysis, among which 125 (88.7%) requested DSS in clinical trial submissions. Journals requesting DSS had a significantly lower JCR quartile and higher impact factor when compared with those journals without DSS request. Among the 90 journals requesting DSS, 14 (15.6%) journals indeed did not publish any DSS in their published trial reports between December 2023 and May 2024.

Conclusion: Over 10% of endocrinology and metabolism journals did not request DSS in clinical trial submissions. More than 15% of the journals declaring to request DSS from their submission instructions, did not publish DSS in their published trial reports. More efforts are needed to improve the practice of endocrinology and metabolism journals in requesting and publishing DSS of clinical trial reports.

https://doi.org/10.3389/fmed.2025.1518399

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“Open-Science Practices in Two Communication Sciences and Disorders Journals: A Systematic Review”


Open-science practices promote research transparency and efficiency, yet their use in communication sciences and disorders (CSD) is understudied. We reviewed the prevalence of open data, open materials, and preregistration in two CSD journals (2019-2024), assessing whether the adoption of open-science badges in one journal in 2022 increased their use. Among 462 empirical articles reviewed, 19.5% shared materials, 3.0% shared data, and 1.1% were preregistered. There was no evidence of increased open-science engagement after open-science badge implementation. Implications of relatively low levels of open-science practices and recommendations for increasing engagement in open-science practices among CSD researchers are discussed.

https://doi.org/10.35542/osf.io/6habn_v1

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“Launch of the UNESCO Open Science Toolkit Resources: Data Policies for Times of Crisis Facilitated by Open Science ”


The UNESCO Toolkit resource package includes the following:

  • Factsheet: A concise overview of the critical role data policies play in crisis situations and how open science can lead to more resilient, equitable, and coordinated data management in times of crisis.
  • Guidance Document: A structured framework designed to help stakeholders develop context-sensitive data policies informed by open science principles.
  • Checklist: A practical tool to support the design of data policies for times of crisis aligned with open science values.

https://tinyurl.com/2wmjvnnp

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“Maturity Model for Organizational Research Data Management Services”


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

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“Making Reproducibility a Reality By 2035? Enabling Publisher Collaboration for Enhanced Data Policy Enforcement”


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.

https://doi.org/10.2218/ijdc.v19i1.1064

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“AI and Open Science: Implications and Library Practice Recommendations”


With the increasing proliferation of artificial intelligence (AI) in higher education and science, technology, engineering, and mathematics research, what are the implications for open science? As the open science movement advocates for increased transparency and openness in the research process, where do AI and machine learning fit in? And where does that leave library and information science professionals in roles related to open science? This article explores several approaches and considerations for how AI impacts open science, including whether AI has sufficient openness and transparency to align with the goals of open science, whether AI can be used to further open science goals, and the effects of AI use on researcher and public attitudes and actions. The article provides recommendations for library practice, including knowledge-building, connections and advocacy, consultations and liaison work, licensing, and science communication and engagement.

https://dx.doi.org/10.1353/lib.2025.a961191

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OATP: “A Living Archive of the Open Access Movement Approaches Its Next Chapter”


The Open Access Tracking Project offers a real-time alert system for open access news and information. With its founder contemplating retirement, now is the time to strengthen its infrastructure, improve its user experience, and secure its long-term future. . . .

Previously funded by Arcadia (2011–2016) and the Arnold Foundation (2016–2018), OATP and TT are currently operating without dedicated funding, though Suber has described them as “eminently fundable” due to their critical contributions to the OA infrastructure (Suber, 2025). As Suber anticipates his eventual retirement, he is seeking to transfer both OATP and TT into a nonprofit or coalition-led model, with the aim of securing their long-term sustainability.

https://tinyurl.com/y53hsabs

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“Recommendations on Open Science Rewards and Incentives: Guidance for Multiple Stakeholders in Research”


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

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“What to Do About Data Distance? Responsible Alternatives to Data Sharing”


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

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“From Data Creator to Data Reuser: Distance Matters”


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

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“Accelerating Access to Research Results: New Implementation Date for the 2024 NIH Public Access Policy”


I am excited to announce that one of my first actions as NIH Director is pushing the accelerator on policies to make NIH research findings freely and quickly available to the public. The 2024 Public Access Policy, originally slated to go into effect on December 31, 2025, will now be effective as of July 1, 2025.

To be clear, maximum transparency regarding the research we support is our default position. Since the release of NIH’s 2008 Public Access Policy, more than 1.5 million articles reporting on NIH-supported research have been made freely available to the public through PubMed Central. While the 2008 Policy allowed for an up to 12-month delay before such articles were required to be made publicly available, in 2024, NIH revised the Public Access Policy to remove the embargo period so that researchers, students, and members of the public have rapid access to these findings.

https://tinyurl.com/nudx2rej

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“Effect of Perceived Preprint Effectiveness and Research Intensity on Posting Behaviour”


Open science is increasingly recognised worldwide, with preprint posting emerging as a key strategy. This study explores the factors influencing researchers’ adoption of preprint publication, particularly the perceived effectiveness of this practice and research intensity indicators such as publication and review frequency. Using open data from a comprehensive survey with 5,873 valid responses, we conducted regression analyses to control for demographic variables. Researchers’ productivity, particularly the number of journal articles and books published, greatly influences the frequency of preprint deposits. The perception of the effectiveness of preprints follows this. Preprints are viewed positively in terms of early access to new research, but negatively in terms of early feedback. Demographic variables, such as gender and the type of organisation conducting the research, do not have a significant impact on the production of preprints when other factors are controlled for. However, the researcher’s discipline, years of experience and geographical region generally have a moderate effect on the production of preprints. These findings highlight the motivations and barriers associated with preprint publication and provide insights into how researchers perceive the benefits and challenges of this practice within the broader context of open science.

https://arxiv.org/abs/2504.18896

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“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.

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

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“Electronic Health Data Reuse Purposes”


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

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“Reusing Chemical Data Across Disciplines: Initiatives and Common Challenges”


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

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“Enabling Factors and Opportunities to Maximize Health Data Reuse”

This chapter looks at future developments and maximization of health data reuse in public health. The digital maturity of healthcare systems is, for example, a crucial factor in enabling the availability of electronic health data and their sharing through interconnected databases. The frontiers opened by artificial intelligence to improve health surveillance, disease detection, and resource allocation are changing public health programmes and population well-being by enabling targeted health promotion efforts, identifying high-risk populations, enhancing communication strategies tailored to specific patient subgroups, optimizing logistics in healthcare delivery, and supporting professionals’ decision-making processes. The common data spaces, which are going to be built in the EU to promote data sharing and innovation, are sustained and strengthened by important reforms, such as the European for Health Data Space Regulation, which aims to standardize eHealth data exchange, empower individuals, and facilitate the secondary use of health data for research, innovation, and policy making by providing precise rules for health data governance, interoperability, and safe data sharing across EU Member States.

https://doi.org/10.1007/978-3-031-88497-9_3

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“Improving the Transparency of Data Access Conditions in the SSH Domain: Recommendations Based on a Small-Scale Analysis of the Conditions Applied to Restricted Access Datasets”


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

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“Do Data Management Policies Become More Open over Time?”


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

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Paywall: “Does the Open Science Environment Enhance the Impact of Academic Papers? An Analysis of Indicator Relationships Using Causal Inference”


It is found that there is a causal relationship between the open science environment and academic paper impact, significantly enhancing both the societal and academic impact of academic papers. Specifically, in the case of OA, the open science environment on average increases academic paper mentions by 3.50 times on News platforms, 89.82 times on X, 42.53 readings on Mendeley and 28.74 citations on Dimensions.

https://doi.org/10.1108/GKMC-07-2024-0414

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