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Abstract Background The impetus for policies promoting medical data sharing in China has gained significant traction. Nonetheless, the present legal and ethical framework governing the research use of medical data in China, is characterized by a more restrictive rather than permissive approach. The proportion of Chinese medical data being leveraged for scientific research still has room for improvement at present, indicating a significant untapped potential for advancing medical knowledge and improving healthcare outcomes. Building upon this research, we aim to delve deeper into the challenges researchers encounter in the sharing of medical data through focus group interviews. Methods We conducted two focus group interviews study with researchers representing diverse disciplines to explore their perspectives on 21 June 2021 and 28 July 2021. A total of seventeen researchers willingly participated in this study, representing various professional backgrounds. Similar codes were merged. Research team discussions were also utilized to select interviewees’ statements that were regarded as typical or representative. Results The respondents demonstrated a strong understanding that medical data should not be disseminated arbitrarily, recognizing the importance of sharing data in compliance with laws. Through the interview, we found that although respondents stressed the importance of careful consideration regarding if and when this information can be responsibly released, none of the respondents raised the issue of necessitating consent from data subjects for the research use of medical data. This observation sharply contrasts with the stringent separate consent provisions for secondary data use outlined in the PIPL. Conclusions The findings from the focus group studies shed light on researchers’ barriers and ethical challenges towards medical data sharing for scientific research, highlighting their deep concern for data security and cautious approach to sharing. The key objectives aimed at facilitating and enabling the reuse of medical data encompass enhancing interoperability, harmonizing data standards, improving data quality, safeguarding privacy, ensuring informed consent, incentivizing patients, and establishing explicit regulations pertaining to data access and utilization.

With the increasing demand for data sharing in the Internet of vehicles, reliable identity authentication protocols and scientifically rational incentive mechanisms have become the primary conditions for ensuring the stable operation of the Internet of vehicles network. Blockchain, as a decentralized distributed ledger, provides a technologically advanced data sharing platform for the Internet of vehicles, making the combination of blockchain technology and the Internet of vehicles a feasible new approach to data sharing. This paper summarizes the requirements of the Internet of vehicles, analyzes the blockchain-based Internet of vehicles architecture, and divides it into the cloud layer, mechanism layer and edge layer. It categorizes and compares the existing solutions for authentication protocols and incentive mechanisms in blockchain-based Internet of vehicles systems by summarizing relevant literature. It summarizes the workflow and implementation solutions of existing trustworthy authentication mechanisms by analyzing the distributed and centralized authentication architectures. It categorizes existing incentive mechanisms into value-based, trust-based and individual decision-based incentive mechanisms. Finally, this paper summarizes the existing problems and solutions of trustworthy authentication and incentive mechanisms from the perspectives of privacy protection and typical attacks. It provides an outlook on future research directions for blockchain-based Internet of vehicles, focusing on data sharing, multi-vehicle cooperation, and the integration of 6G technology.

Abstract Background The increasing availability of large volumes of personal data from diverse sources such as electronic health records, research programmes, commercial genetic testing, national health surveys and wearable devices presents significant opportunities for advancing public health, disease surveillance, personalized medicine and scientific research and innovation. However, this potential is hampered by a lack of clarity related to the processing and sharing of personal health data, particularly across varying national regulatory frameworks. This often leaves researcher stakeholders uncertain about how to navigate issues around secondary data use, repurposing data for different research objectives and cross-border data sharing. Method We analysed 37 data protection legislation across Africa to identify key principles and requirements for processing and sharing of personal health and genetic data in scientific research. On the basis of this analysis, we propose strategies that data science research initiatives in Africa can implement to ensure compliance with data protection laws while effectively reusing and sharing personal data for health research and scientific innovation. Results In many African countries, health and genetic data are categorized as sensitive and subject to stricter protection. Key principles guiding the processing of personal data include confidentiality, non-discrimination, transparency, storage limitation, legitimacy, purpose specification, integrity, fairness, non-excessiveness, accountability and data minimality. The rights of data subjects include the right to be informed, the right of access, the right to rectification, the right to erasure/deletion of data, the right to restrict processing, the right to data portability and the right to seek compensation. Consent and adequacy assessments were the most common legal grounds for cross-border data transfers. However, considerable variation exists in legal requirements for data transfer across countries, potentially creating barriers to collaborative health research across Africa. Conclusions We propose several strategies that data science research initiatives can adopt to align with data protection laws. These include developing a standardized module for safe data flows, using trusted data environments to minimize cross-border transfers, implementing dynamic consent mechanisms to comply with consent specificity and data subject rights and establishing codes of conduct to govern the secondary use of personal data for health research and innovation.

Objective To establish an information management platform for provincial rare blood group bank to achieve the interconnection and sharing of data such as rare blood group types, donor information and blood inventory, so as to enhance the supply security of rare blood group blood. Methods The information barriers of rare blood group management in each business link and among blood stations in Zhejiang province were analyzed using fishbone diagram. Data modeling technology was introduced based on the unified blood collection and supply business system and blood cloud platform of Zhejiang, the database view of rare blood group donors and rare blood group was constructed through demand analysis and model design, and a unified query platform was developed to achieve the sharing of rare blood group information. Results The system integrated, retrievaled and displayed rare blood type information, achieving the interconnection of information among various business links and blood stations within blood banks. Up to now, in addition to RhD negative blood group, there were 8 types of rare red blood cells in the bank, covering 7 blood group systems, 289 rare blood group donors and 216 units of frozen red blood cells of rare blood group. Conclusion The information system based on data modeling can improve the overall efficiency and collaboration of the system, and reduce the workload of system development and operation. It can provide reference for standardized data management and application, and also provide a model for establishing and improving of a national rare blood group information network.

Abstract In the envisioned 6G landscape, data sharing is expected to become increasingly prevalent, giving rise to digital marketplaces that foster cooperation among organizations for collecting, sharing, and processing data for analysis. These marketplaces serve as connectors between data producers and consumers, empowering multi-tenancy scenarios for seamless and secure data sharing both within and outside organizations. Given that 6G networks promise ultra-low latency, enhanced connectivity, and massive data throughput, the need for robust data access control mechanisms becomes imperative. These mechanisms ensure security and trust among entities, particularly in multi-tenant environments where multiple organizations share infrastructure and data resources. In this paper, we have designed and implemented a novel access control mechanism tailored for a distributed data streaming system developed by Nokia Bell Labs. Our approach leverages fine-grained policies, dynamic enforcement, and transparency mechanisms to enhance trust between data owners and consumers. By facilitating secure multi-tenancy data sharing, our solution contributes to the seamless exchange of data across diverse entities within the next-generation communication ecosystem. We demonstrate that our proposed access control mechanism incurs minimal overhead while ensuring data confidentiality and integrity. The introduction of such advancements in data sharing markets strengthens the overall ecosystem by providing heightened transparency and enhanced control over data, promoting collaboration and innovation in the 6G era.

An organization needs to effectively communicate its performance results to distinguish itself. This not only helps attract necessary resources but also addresses any challenges that may arise. In the context of the intense work environment within health organizations and numerous monitoring and inspection bodies, it is essential to empower health system managers and employees. Their active involvement in evaluating performance and disseminating health program results is crucial to the process.

Abstract With the accelerating process of global industrialization, the automated fabrication industry, as an important part of the industry, is also developing rapidly. Due to the low efficiency and high resource consumption of traditional manufacturing, it is more important to improve the production efficiency of conventional manufacturing and realize the resource-saving development mode. Due to the characteristics of system digital-driven technology, it can promote the intelligent development of the manufacturing industry. Therefore, the deep integration of system digital-driven technology and manufacturing is conducive to the intelligent promotion of manufacturing. Comparing the difference between the traditional manufacturing industry and the intelligent manufacturing industry, the application potential of system digitalization technology in the manufacturing field is analyzed. Based on the analysis of the theoretical basis of the system digital-driven technology and the application of the system digital technology in the main fields, the importance of the technology for the improvement of the accuracy index in the manufacturing process is emphatically analyzed. The maximum increase can be 5.3%, and the minimum increase can be 3.3%. It can be seen that the deep integration of digital-driven technology and manufacturing can not only improve the intelligent level of manufacturing, realize the intensive development of the industry, but also realize the data sharing of the entire industrial chain.

Abstract Background Key discoveries and innovations in the field of human genetics have led to the foundation of molecular and personalized medicine. Here, we present the Genome Tunisia Project, a two-phased initiative (2022–2035) which aims to deliver the reference sequence of the Tunisian Genome and to support the implementation of personalized medicine in Tunisia, a North African country that represents a central hub of population admixture and human migration between African, European, and Asian populations. The main goal of this initiative is to develop a healthcare system capable of incorporating omics data for use in routine medical practice, enabling medical doctors to better prevent, diagnose, and treat patients. Methods A multidisciplinary partnership involving Tunisian experts from different institutions has come to discern all requirements that would be of high priority to fulfill the project’s goals. One of the most urgent priorities is to determine the reference sequence of the Tunisian Genome. In addition, extensive situation analysis and revision of the education programs, community awareness, appropriate infrastructure including sequencing platforms and biobanking, as well as ethical and regulatory frameworks, have been undertaken towards building sufficient capacity to integrate personalized medicine into the Tunisian healthcare system. Results In the framework of this project, an ecosystem with all engaged stakeholders has been implemented including healthcare providers, clinicians, researchers, pharmacists, bioinformaticians, industry, policymakers, and advocacy groups. This initiative will also help to reinforce research and innovation capacities in the field of genomics and to strengthen discoverability in the health sector. Conclusions Genome Tunisia is the first initiative in North Africa that seeks to demonstrate the major impact that can be achieved by Human Genome Projects in low- and middle-income countries to strengthen research and to improve disease management and treatment outcomes, thereby reducing the social and economic burden on healthcare systems. Sharing this experience within the African scientific community is a chance to turn a major challenge into an opportunity for dissemination and outreach. Additional efforts are now being made to advance personalized medicine in patient care by educating consumers and providers, accelerating research and innovation, and supporting necessary changes in policy and regulation.

Abstract Background The National Institute of Health and Social Care Research (NIHR) Health Informatics Collaborative (HIC) for Hearing Health has been established in the UK to curate routinely collected hearing health data to address research questions. This study defines priority research areas, outlines its aims, governance structure and demonstrates how hearing health data have been integrated into a common data model using pure tone audiometry (PTA) as a case study. Methods After identifying key research aims in hearing health, the governance structure for the NIHR HIC for Hearing Health is described. The Observational Medical Outcomes Partnership (OMOP) was chosen as our common data model to provide a case study example. Results The NIHR HIC Hearing Health theme have developed a data architecture outlying the flow of data from all of the various siloed electronic patient record systems to allow the effective linkage of data from electronic patient record systems to research systems. Using PTAs as an example, OMOPification of hearing health data successfully collated a rich breadth of datapoints across multiple centres. Conclusion This study identified priority research areas where routinely collected hearing health data could be useful. It demonstrates integration and standardisation of such data into a common data model from multiple centres. By describing the process of data sharing across the HIC, we hope to invite more centres to contribute and utilise data to address research questions in hearing health. This national initiative has the power to transform UK hearing research and hearing care using routinely collected clinical data.

Abstract The tree‐like morphology of neurons and glia is a key cellular determinant of circuit connectivity and metabolic function in the nervous system of essentially all animals. To elucidate the contribution of specific cell types to both physiological and pathological brain states, it is important to access detailed neuroanatomy data for quantitative analysis and computational modeling. NeuroMorpho.Org is the largest online collection of freely available digital neural reconstructions and related metadata and is continuously updated with new uploads. Earlier in the project, we released multiple datasets together yearly, but this process caused an average delay of several months in making the data public. Moreover, in the past 5 years, >80% of invited authors agreed to share their data with the community via NeuroMorpho.Org, up from

Abstract Camera trapping has revolutionized wildlife ecology and conservation by providing automated data acquisition, leading to the accumulation of massive amounts of camera trap data worldwide. Although management and processing of camera trap‐derived Big Data are becoming increasingly solvable with the help of scalable cyber‐infrastructures, harmonization and exchange of the data remain limited, hindering its full potential. There is currently no widely accepted standard for exchanging camera trap data. The only existing proposal, “Camera Trap Metadata Standard” (CTMS), has several technical shortcomings and limited adoption. We present a new data exchange format, the Camera Trap Data Package (Camtrap DP), designed to allow users to easily exchange, harmonize and archive camera trap data at local to global scales. Camtrap DP structures camera trap data in a simple yet flexible data model consisting of three tables (Deployments, Media and Observations) that supports a wide range of camera deployment designs, classification techniques (e.g., human and AI, media‐based and event‐based) and analytical use cases, from compiling species occurrence data through distribution, occupancy and activity modeling to density estimation. The format further achieves interoperability by building upon existing standards, Frictionless Data Package in particular, which is supported by a suite of open software tools to read and validate data. Camtrap DP is the consensus of a long, in‐depth, consultation and outreach process with standard and software developers, the main existing camera trap data management platforms, major players in the field of camera trapping and the Global Biodiversity Information Facility (GBIF). Under the umbrella of the Biodiversity Information Standards (TDWG), Camtrap DP has been developed openly, collaboratively and with version control from the start. We encourage camera trapping users and developers to join the discussion and contribute to the further development and adoption of this standard.

ObjectiveThe global sharing of pathogen genome sequences has been significantly expedited by the COVID-19 pandemic. This study aims to elucidate the global landscape of SARS-CoV-2 genome sharing between 2020 and 2023 with a focus on quantity, timeliness, and quality. Specifically, the characteristics of China are examined.MethodsSARS-CoV-2 genomes along with associated metadata were sourced from GISAID database. The genomes were analyzed to evaluate the quantity, timeliness, and quality across different countries/regions. The metadata characteristics of shared genomes in China in 2023 were examined and compared with the actual demographic data of China in 2023.ResultsFrom 2020 to 2023, European countries consistently maintained high levels of genomic data sharing in terms of quantity, timeliness, and quality. In 2023, China made remarkable improvements in sequence sharing, ranking among the top 3.89% globally for quantity, 22.78% for timeliness, and 17.78% for quality. The genome sharing in China in 2023 covered all provinces with Shanghai Municipality contributing the most genomes. Human samples accounted for 99.73% of the shared genomes and exhibited three distinct peaks in collection dates. Males constituted 52.06%, while females constituted 47.94%. Notably, there was an increase in individuals aged 65 and above within the GISAID database compared to China’s overall population in 2023.ConclusionThe global sharing of SARS-CoV-2 genomes in 2020–2023 exhibited disparities in terms of quantity, timeliness, and quality. However, China has made significant advancements since 2023 by achieving comprehensive coverage across provinces, timely dissemination of data, and widespread population monitoring. Strengthening data sharing capabilities in countries like China during the SARS-CoV-2 pandemic will play a crucial role in containing and responding to future pandemics caused by emerging pathogens.

Predicting the ocean state in a reliable and interoperable way, while ensuring high-quality products, requires forecasting systems that synergistically combine science-based methodologies with advanced technologies for timely, user-oriented solutions. Achieving this objective necessitates the adoption of best practices when implementing ocean forecasting services, resulting in the proper design of system components and the capacity to evolve through different levels of complexity. The vision of OceanPrediction Decade Collaborative Center, endorsed by the UN Decade of Ocean Science for Sustainable Development 2021-2030, is to support this challenge by developing a “predicted ocean based on a shared and coordinated global effort” and by working within a collaborative framework that encompasses worldwide expertise in ocean science and technology. To measure the capacity of ocean forecasting systems, the OceanPrediction Decade Collaborative Center proposes a novel approach based on the definition of an Operational Readiness Level (ORL). This approach is designed to guide and promote the adoption of best practices by qualifying and quantifying the overall operational status. Considering three identified operational categories - production, validation, and data dissemination - the proposed ORL is computed through a cumulative scoring system. This method is determined by fulfilling specific criteria, starting from a given base level and progressively advancing to higher levels. The goal of ORL and the computed scores per operational category is to support ocean forecasters in using and producing ocean data, information, and knowledge. This is achieved through systems that attain progressively higher levels of readiness, accessibility, and interoperability by adopting best practices that will be linked to the future design of standards and tools. This paper discusses examples of the application of this methodology, concluding on the advantages of its adoption as a reference tool to encourage and endorse services in joining common frameworks.

The routine genomic surveillance of pathogens in diverse geographical settings and equitable data sharing are critical to inform effective infection control and therapeutic development. The coronavirus disease 2019 (COVID-19) pandemic highlighted the importance of routine genomic surveillance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to detect emerging variants of concern. However, the majority of high-income countries sequenced >0.5% of their COVID-19 cases, unlike low- and middle-income countries. By the end of 2022, many countries around the world had managed to establish capacity for pathogen genomic surveillance. Notably, Beta and Omicron; 2 of the 5 current SARS-CoV-2 variants of concern were first discovered in Africa through an aggressive sequencing campaign led by African scientists. To sustain such infrastructure and expertise beyond this pandemic, other endemic pathogens should leverage this investment. Therefore, countries are establishing multi-pathogen genomic surveillance strategies. Here we provide a catalog of the current landscape of sequenced and publicly shared pathogens in different countries in Africa. Drawing upon our collective knowledge and expertise, we review the ever-evolving challenges and propose innovative recommendations.

The Ebola Data Platform (EDP) was developed to strengthen knowledge and capacity across health, research, and humanitarian communities to reduce the impact of Ebola through responsible data use. This collaborative initiative was established by West African governments, NGOs, academic organisations, and intra-governmental health organisations directly involved in the 2013–2016 West African Ebola outbreak. The platform was established to provide a centralised, standardised dataset of individual patient data collected during the outbreak for the purpose of research to improve Ebola treatment and control, and includes over 13,600 patient records of individuals infected and treated from 22 different Ebola treatment centres across Guinea, Sierra Leone, Liberia, and Nigeria. Patient data are available from treatment centre triage and admission, inpatient clinical observations, and outcomes, with outpatient follow-up available for some datasets. Data include signs and symptoms, pre-existing comorbidities, vital signs, laboratory testing, treatments, complications, dates of admission and discharge, mortality, viral strains, and other data. This publication describes characteristics of the EDP dataset, its architecture, methods for data access and tools for utilising the dataset.

Background Data reusability is the driving force of the research data life cycle. However, implementing strategies to generate reusable data from the data creation to the sharing stages is still a significant challenge. Even when datasets supporting a study are publicly shared, the outputs are often incomplete and/or not reusable. The FAIR (Findable, Accessible, Interoperable, Reusable) principles were published as a general guidance to promote data reusability in research, but the practical implementation of FAIR principles in research groups is still falling behind. In biology, the lack of standard practices for a large diversity of data types, data storage and preservation issues, and the lack of familiarity among researchers are some of the main impeding factors to achieve FAIR data. Past literature describes biological curation from the perspective of data resources that aggregate data, often from publications. Methods Our team works alongside data-generating, experimental researchers so our perspective aligns with publication authors rather than aggregators. We detail the processes for organizing datasets for publication, showcasing practical examples from data curation to data sharing. We also recommend strategies, tools and web resources to maximize data reusability, while maintaining research productivity. Conclusion We propose a simple approach to address research data management challenges for experimentalists, designed to promote FAIR data sharing. This strategy not only simplifies data management, but also enhances data visibility, recognition and impact, ultimately benefiting the entire scientific community.

The sharing of pathological data is highly important in various applications, such as remote diagnosis, graded diagnosis, illness treatment, and specialist system development. However, ensuring reliable, secure, privacy-preserving, and efficient sharing of pathological data poses significant challenges. This paper presents a novel solution that leverages blockchain technology to ensure reliability in pathological data sharing. Additionally, it employs conditional proxy re-encryption (C-PRE) and public key encryption with equality test technology to control the scope and preserve the privacy of shared data. To assess the practicality of our solution, we implemented a prototype system using Hyperledger Fabric and conducted evaluations with various metrics. We also compared the solution with relevant schemes. The results demonstrate that the proposed solution effectively meets the requirements for pathological data sharing and is practical in production scenarios.

In today's data-centric landscape, effective data stewardship is critical for facilitating scientific research and innovation. This article provides an overview of essential tools and frameworks for modern data stewardship practices. Over 300 tools were analyzed in this study, assessing their utility, relevance to data stewardship, and applicability within the life sciences domain.

While global marine science data sharing plays a crucial role in advancing scientific research and knowledge dissemination, it also brings with it two major ethical conflicts: those between individual interests and the public interest and those between data privacy and data sharing, which are rooted in conflict of interest. In the context of globalization and rapid technological progress, current ethical governance frameworks are inadequate in regulating these conflicts. On the one hand, governance cannot meet the needs of rapidly evolving technologies; on the other hand, the complexity and diversity of stakeholders involved in governance make it more difficult. By introducing the theory of legal hierarchy and innovatively proposing the theory of ethical hierarchy, which classifies the ethical principles to be followed according to their importance and applicability, the priority and scope of application between different ethical principles can be clearly defined so that various stakeholders can make the right ethical choices. Using research methods such as literature review, case studies, and comparative analysis, we propose to construct an ethical governance framework that consists of a stakeholder consultation and communication network in terms of governance structure and clarifies the ethical responsibilities of each party in data sharing in terms of governance measures. This framework not only provides a platform for communication and cooperation among different stakeholders but also helps to balance the interests of different stakeholders and improve the level and extent of global marine science data sharing.

Federated learning has recently emerged as a privacy-preserving distributed machine learning approach. Federated learning enables collaborative training of multiple clients and entire fleets without sharing the involved training datasets. By preserving data privacy, federated learning has the potential to overcome the lack of data sharing in the renewable energy sector which is inhibiting innovation, research and development. Our paper provides an overview of federated learning in renewable energy applications. We discuss federated learning algorithms and survey their applications and case studies in renewable energy generation and consumption. We also evaluate the potential and the challenges associated with federated learning applied in power and energy contexts. Finally, we outline promising future research directions in federated learning for applications in renewable energy.

This paper describes the requirements analysis phase towards the establishment and implementation of a municipal data utility (KDW = Kommunales Datenwerk, German) to facilitate data sharing between intra- and intermunicipal stakeholders. Against the backdrop of increasing digitisation and the growing importance of data-driven decision making in municipal governance, this paper aims to address the pressing need for efficient data management solutions within and across municipalities. Based on a structured self-developed methodology, the authors use a qualitative research approach: the paper examines the experiences and challenges encountered during the requirements phase, the design phase, and the development phase of the KDW. The findings indicate that the establishment of a robust KDW requires (1) extensive stakeholder engagement, (2) clear governance structures, and (3) a robust technical infrastructure. In addition, the study highlights the critical importance of establishing a sound legal framework that addresses data ownership, privacy, security and regulatory compliance. Addressing legal and regulatory barriers to data sharing is paramount to the successful implementation and operation of the KDW. The paper concludes by highlighting the potential benefits of KDWs and outlining future work. The overall methodology, approach, and outcome are validated within the city of Mainz, and the lessons learned are accommodated in the insights presented in the rest of the paper.

Abstract With the swift advancement of the global digital economy, data has emerged as a critical component in fostering the integration of large enterprises with small and medium-sized enterprises (SMEs). Nevertheless, due to disparities in resources and capabilities between these entities, there is a deficiency in the willingness to share data, hindering the full actualization of data’s potential value. Hence, it is imperative to facilitate a novel cooperative development paradigm wherein platforms enable data sharing among large enterprises and SMEs. In this paper, we construct a tripartite evolutionary game model encompassing large enterprises, SMEs, and digital platforms, examine the evolutionary stable strategies adopted by these entities in the data sharing, and use numerical simulation to explore the system’s evolutionary stability under various influencing factors. Contrasting with prior research, this study considers the heterogeneity of enterprise scale and delves into the data sharing dynamics between large enterprises and SMEs. Simultaneously, it positions the digital platform as a player in the game, examining its impact on data sharing among the enterprises. Findings indicate that: (1) SMEs exhibit greater eagerness for data sharing compared to large enterprises, which display a U-shaped influence during the process; (2) Digital platforms are particularly sensitive to costs, with the platform’s initiative and the service quality will affect enterprises strategic choices; (3) Government subsidies positively encourage tripartite cooperation, and robust data security governance framework is crucial for enterprises. Finally, based on the results of the study and combining with the current situation of digital economy development, it puts forward the suggestions for promoting platforms to empower large enterprises and SMEs to realize data sharing and the prospects for future research.

In order to solve the problems of difficult data integration, complex governance and low sharing efficiency, this paper proposes a scheme for construction of railway locomotive maintenance data integration and sharing platform. The author first realized the automatic and periodic data acquisition from different systems and saving of the same in a data warehouse through multi-source heterogeneous data acquisition technology, and then classified the data in the data warehouse for standardized management and forming service themes and thematic libraries. At the same time, the author also provided real-time and offline data sharing modes under security control, realizing data sharing in different scenarios. This platform, after being constructed in the field, saves more than 70% of the planned time for locomotive pre-scheduling and maintenance, and reduces the average downtime of locomotive maintenance by about 20 minutes. This shows the platform can help to promote multi-system data fusion and improve the informatization level, production efficiency and management ability of railway locomotive maintenance.

In today’s world, Electronic Health Records (EHR) are highly segregated and available only within the organization with which the patient is associated. If a patient has to visit another hospital there is no secure way for hospitals to communicate and share medical records. Hence, people are always asked to redo tests that have been done earlier in different hospitals. This leads to monetary, time, and resource loss. Even if the organizations are ready to share data, there are no secure methods for sharing without disturbing data privacy, integrity, and confidentiality. When health data are stored or transferred via unsecured means there are always possibilities for adversaries to initiate an attack and modify them. To overcome these hurdles and secure the storage and sharing of health records, blockchain, a very disruptive technology can be integrated with the healthcare system for EHR management. This paper surveys recent works on the distributed, decentralized systems for EHR storage in healthcare organizations.

Abstract Background The provision of data sharing statements (DSS) for clinical trials has been made mandatory by different stakeholders. DSS are a device to clarify whether there is intention to share individual participant data (IPD). What is missing is a detailed assessment of whether DSS are providing clear and understandable information about the conditions for data sharing of IPD for secondary use. Methods A random sample of 200 COVID-19 clinical trials with explicit DSS was drawn from the ECRIN clinical research metadata repository. The DSS were assessed and classified, by two experienced experts and one assessor with less experience in data sharing (DS), into different categories (unclear, no sharing, no plans, yes but vague, yes on request, yes with specified storage location, yes but with complex conditions). Results Between the two experts the agreement was moderate to substantial (kappa=0.62, 95% CI [0.55, 0.70]). Agreement considerably decreased when these experts were compared with a third person who was less experienced and trained in data sharing (“assessor”) (kappa=0.33, 95% CI [0.25, 0.41]; 0.35, 95% CI [0.27, 0.43]). Between the two experts and under supervision of an independent moderator, a consensus was achieved for those cases, where both experts had disagreed, and the result was used as “gold standard” for further analysis. At least some degree of willingness of DS (data sharing) was expressed in 63.5% (127/200) cases. Of these cases, around one quarter (31/127) were vague statements of support for data sharing but without useful detail. In around half of the cases (60/127) it was stated that IPD could be obtained by request. Only in in slightly more than 10% of the cases (15/127) it was stated that the IPD would be transferred to a specific data repository. In the remaining cases (21/127), a more complex regime was described or referenced, which could not be allocated to one of the three previous groups. As a result of the consensus meetings, the classification system was updated. Conclusion The study showed that the current DSS that imply possible data sharing are often not easy to interpret, even by relatively experienced staff. Machine based interpretation, which would be necessary for any practical application, is currently not possible. Machine learning and / or natural language processing techniques might improve machine actionability, but would represent a very substantial investment of research effort. The cheaper and easier option would be for data providers, data requestors, funders and platforms to adopt a clearer, more structured and more standardised approach to specifying, providing and collecting DSS. Trial registration The protocol for the study was pre-registered on ZENODO ( https://zenodo.org/record/7064624#.Y4DIAHbMJD8 ).

AbstractPurpose: For individuals with atopic dermatitis (AD), interpreting scientific papers that present clinical outcomes including the Eczema Area and Severity Index (EASI) and Investigators Global Assessment may be difficult. When compared to tabulated data and graphs, images from before and after treatment are often far more meaningful to these patients that ultimately will be candidates for the treatment. This systematic review focused on determining the frequency of clinical image sharing in AD research.Materials and methods: Conducted in accordance with PRISMA guidelines, the review concentrated on randomized controlled trials that investigated predefined and available systemic treatments for AD. The search was performed in the MEDLINE database for studies published from the inception until 21 December 2023.Results: The review included 60 studies, encompassing 17,799 randomized patients. Across these studies, 16 images representing 6 patients were shared in the manuscripts, leading to a sharing rate of 0.3‰.Conclusions: The almost missing inclusion of patient images in clinical trial publications hinders patient understanding. Adding images to scientific manuscripts could significantly improve patients’ comprehension of potential treatment outcomes. This review highlights the need for authors, the pharmaceutical industry, study sponsors, and publishers to enhance and promote patient information through increased use of visual data.

Background: While open datasets are adopting FAIR principles to improve their discovery and use, restricted data—those only accessible via request or application—have fallen behind. Metadata is not an inherent characteristic of restricted data, which limits its ability to be found and used. To better understand discoverability and accessibility of restricted data, this study reviewed restricted health data sources to determine how they describe their datasets and access procedures, what descriptive commonalities exist across data sources, and to what extent the commonalities we found can be accommodated within existing metadata schemas. Methods: This study extracted dataset and access information provided by a sample of 48 restricted data sources, identified commonalities across these data sources to develop possible metadata elements for restricted data, and mapped these metadata elements to existing metadata schemas (e.g., DataCite) to evaluate how well they accommodate information supplied by restricted data sources. Results: Restricted data sources describe their datasets (35 commonalities) and access procedures (27 commonalities) in similar ways. Dataset descriptions aligned with existing metadata schemas, with the DDI-Lifecycle and -Codebook schemas receiving 91.4% and 85.7% exact matches respectively with the dataset elements we identified. Access procedures did not align with metadata available in existing schemas. Discussion: While descriptive dataset metadata for restricted data sources will make their data more findable, the accessibility of these datasets could be significantly improved by structured metadata capturing data access information. Presently, metadata schemas do not accommodate the level of detail restricted data sources provide about access procedures and requirements.

Research Infrastructures (RIs) are strategic assets facilitating innovation and knowledge advancement across all scientific disciplines. They provide researchers with advanced tools and resources that go beyond individual or institutional capacities and promote collaboration, community-building and the application of scientific standards. Remote and virtual access to RIs enables scientists to use these essential resources without the necessity of being physically present. The COVID-19 pandemic restrictions where a catalyst for the expansion and further development of remote and virtual access models, particularly in fields where physical access had been the predominant model. The eRImote project explores pathways for digital and remote RI access through targeted surveys, stakeholder workshops, expert groups discussions, and the analysis of specific use cases. This paper provides a definition of remote and virtual access and remote training and explores their implementation across various RIs, highlighting the implications for their operational processes and the dynamics of interaction between RIs and their user communities. It presents the identified advantages, obstacles, and best-practices, alongside strategies and recommendations to navigate and mitigate challenges effectively. Key issues and recommendations are summed up separately for remote access, virtual access, and remote training, complemented by general recommendations for facilitating remote and virtual access to RIs. These relate to budgeting and funding, the balancing of RI access models, the need for regulatory frameworks for sample shipments, collaboration among RIs, impact assessment of remote and virtual access on user interactions, operational efficiency and the environment footprint of RIs, and the adaption of data sharing policies. Stakeholders are broadly invited to give their feedback on the paper’s findings and conclusions, which will be integrated into improved versions of this paper.

Abstract Open science (OS) awareness and skills are increasingly becoming an essential part of everyday scientific work as e.g., many journals require authors to share data. However, following an OS workflow can seem challenging at first. Thus, instructions by journals and other guidelines are important. But how comprehensive are they in the field of ecology and evolutionary biology (Ecol Evol)? To find this out, we reviewed 20 published OS guideline articles aimed for ecologists or evolutionary biologists, together with the data policies of 17 Ecol Evol journals to chart the current landscape of OS guidelines in the field, find potential gaps, identify field‐specific barriers for OS and discuss solutions to overcome these challenges. We found that many of the guideline articles covered similar topics, despite being written for a narrow field or specific target audience. Likewise, many of the guideline articles mentioned similar obstacles that could hinder or postpone a transition to open data sharing. Thus, there could be a need for a more widely known, general OS guideline for Ecol Evol. Following the same guideline could also enhance the uniformity of the OS practices carried on in the field. However, some topics, like long‐term experiments and physical samples, were mentioned surprisingly seldom, although they are typical issues in Ecol Evol. Of the journals, 15 out of 17 expected or at least encouraged data sharing either for all articles or under specific conditions, e.g. for registered reports and 10 of those required data sharing at the submission phase. The coverage of journal data policies varied greatly between journals, from practically non‐existing to very extensive. As journals can contribute greatly by leading the way and making open data useful, we recommend that the publishers and journals would invest in clear and comprehensive data policies and instructions for authors.

Objective: The objective of this study was to evaluate the discoverability of supporting research materials, including supporting documents, individual participant data (IPD), and associated publications, in US federally funded COVID-19 clinical study records in ClinicalTrials.gov (CTG). Methods: Study registration records were evaluated for (1) links to supporting documents, including protocols, informed consent forms, and statistical analysis plans; (2) information on how unaffiliated researchers may access IPD and, when applicable, the linking of the IPD record back to the CTG record; and (3) links to associated publications and, when applicable, the linking of the publication record back to the CTG record. Results: 206 CTG study records were included in the analysis. Few records shared supporting documents, with only 4% of records sharing all 3 document types. 27% of records indicated they intended to share IPD, with 45% of these providing sufficient information to request access to the IPD. Only 1 dataset record was located, which linked back to its corresponding CTG record. The majority of CTG records did not have links to publications (61%), and only 21% linked out to at least 1 results publication. All publication records linked back to their corresponding CTG records. Conclusion: With only 4% of records sharing all supporting document types, 12% sufficient information to access IPD, and 21% results publications, improvements can be made to the discoverability of research materials in federally funded, COVID-19 CTG records. Sharing these materials on CTG can increase their discoverability, therefore increasing the validity, transparency, and reusability of clinical research.

The scientific method is predicated on transparency—yet the pace at which transparent research practices are being adopted by the scientific community is slow. The replication crisis in psychology showed that published findings employing statistical inference are threatened by undetected errors, data manipulation and data falsification. To mitigate these problems and bolster research credibility, open data and preregistration practices have gained traction in the natural and social sciences. However, the extent of their adoption in different disciplines is unknown. We introduce computational procedures to identify the transparency of a research field using large-scale text analysis and machine learning classifiers. Using political science and international relations as an illustrative case, we examine 93 931 articles across the top 160 political science and international relations journals between 2010 and 2021. We find that approximately 21% of all statistical inference papers have open data and 5% of all experiments are preregistered. Despite this shortfall, the example of leading journals in the field shows that change is feasible and can be effected quickly.

Background Data sharing in developmental science is increasingly encouraged, supported by funder and publisher mandates for open data access. Data sharing can accelerate discovery, link researchers with high quality analytic expertise to researchers with large datasets and democratise the research landscape to enable researchers with limited funding to access large sample sizes. However, there are also significant privacy and security concerns, in addition to conceptual and ethical considerations. These are particularly acute for developmental science, where child participants cannot consent themselves. As we move forward into a new era of data openness, it is essential that we adequately represent the views of stakeholder communities in designing data sharing efforts. Methods We conducted a comprehensive survey of the opinions of 195 parents on data sharing in developmental science. Survey themes included how widely parents are willing to share their child’s data, which type of organisations they would share the data with and the type of consent they would be comfortable providing. Results Results showed that parents were generally supportive of curated, but not open, data sharing. In addition to individual privacy and security concerns, more altruistic considerations around the purpose of research were important. Parents overwhelmingly supported nuanced consenting models in which preferences for particular types of data sharing could be changed over time. This model is different to that implemented in the vast majority of developmental science research and is contrary to many funder or publisher mandates. Conclusions The field should look to create shared repositories that implement features such as dynamic consent and mechanisms for curated sharing that allow consideration of the scientific questions addressed. Better communication and outreach are required to build trust in data sharing, and advanced analytic methods will be required to understand the impact of selective sharing on reproducibility and representativeness of research datasets.

Data-driven computational analysis is becoming increasingly important in biomedical research, as the amount of data being generated continues to grow. However, the lack of practices of sharing research outputs, such as data, source code and methods, affects transparency and reproducibility of studies, which are critical to the advancement of science. Many published studies are not reproducible due to insufficient documentation, code, and data being shared. We conducted a comprehensive analysis of 453 manuscripts published between 2016–2021 and found that 50.1% of them fail to share the analytical code. Even among those that did disclose their code, a vast majority failed to offer additional research outputs, such as data. Furthermore, only one in ten articles organized their code in a structured and reproducible manner. We discovered a significant association between the presence of code availability statements and increased code availability. Additionally, a greater proportion of studies conducting secondary analyses were inclined to share their code compared to those conducting primary analyses. In light of our findings, we propose raising awareness of code sharing practices and taking immediate steps to enhance code availability to improve reproducibility in biomedical research. By increasing transparency and reproducibility, we can promote scientific rigor, encourage collaboration, and accelerate scientific discoveries. We must prioritize open science practices, including sharing code, data, and other research products, to ensure that biomedical research can be replicated and built upon by others in the scientific community.

The Oxford COVID-19 Vaccine Hesitancy Scale is a 7-item psychometric scale developed by Freeman and colleagues a year after detecting the first case of the disease in 2019. The scale assesses people's thoughts, feelings, and behavior toward COVID-19 vaccines. A comprehensive search of major electronic databases, including Scopus, Clarivate Analytics, and PubMed, was conducted to extract eligible articles for inclusion in this meta-analysis. This paper reports information on data collected for a reliability generalization meta-analysis of the Oxford COVID-19 Vaccine Hesitancy Scale. The dataset incorporates information on the average reliability of the scale as measured with Cronbach's alpha in 20 studies included in the meta-analysis. Several benefits can be derived from the dataset. In particular, the research community would find this dataset beneficial as it can enhance their understanding of the health challenges of COVID-19, helping them come up with better solutions to eradicate the disease.

The amount of data generated across various disciplines has been steadily increasing and is projected to experience exponential growth in the foreseeable future. This underscores the pressing need for proficient and streamlined data management. Data has proven to be a crucial tool in addressing complex societal challenges on a global scale. However, the challenge of producing and openly disseminating data that are easily discoverable, accessible, interoperable, and reusable (FAIR) has emerged as a significant concern for policymakers. The potential for data to be repurposed for advancing scientific research and innovation across different disciplines is contingent on its willingness to be shared. This paper employs a systematic literature review to investigate the motivating factors, advantages, and obstacles associated with open data sharing. Additionally, it explores governance frameworks that can create unique opportunities for implementing FAIR principles in real-time scientific research.

Abstract Background Timely access to outbreak related data, particularly in the early events of a spillover, is important to support evidence based control measures in response to outbreaks of zoonotic Emerging Infectious Diseases (EID). Yet, this is impeded by several barriers that need to be understood to promote timely sharing of data. Using the MERS epidemic as a model for a zoonotic EID outbreak, this study sought to provide an in-depth understanding of data sharing practices. Methods Semi-structured interviews with 25 experts were conducted, along with Focus Group Discussions with 15 additional experts. A root-cause analysis was performed to examine the causal relationships between barriers. Enablers were mapped to the root-cause analysis to understand their influence on the barriers. Finally, root causes were placed in context of core dilemmas identified from the qualitative analysis. Findings Eight barriers to data sharing were identified, related to collaboration, technical preparedness, regulations, and (conflict of) interests, and placed in the context of six dilemmas inherent to the multi-stakeholder collaboration required for a zoonotic outbreak response. Fourteen identified enablers showed the willingness of stakeholders to overcome or circumvent these barriers, but also indicated the inherent trial and error nature of implementing such enablers. Interpretation Addressing the barriers requires solutions that must consider the complexity and interconnectedness of the root causes underlying them, and should consider the distinct scopes and interests of the different stakeholders. Insights provided by this study can be used to encourage data sharing practices for future outbreaks Funding Wellcome Trust and UK Aid; EU-H2020 Societal Challenges (grant agreement no. 643476), Nederlandse Organisatie voor Wetenschappelijk Onderzoek (VI.Veni.201S.044)

La regolazione europea in materia di dati sembra cambiare paradigma: l’enfasi non è più solo sul profilo della protezione e del controllo, ma anche della condivisione. In questa direzione vanno, in particolare, il Data Governance Act e il Data Act. Il primo, infatti, promuove il riutilizzo di dati protetti detenuti da enti pubblici, l’intermediazione e il c.d. altruismo dei dati. Il secondo si incentra sui temi dell’accesso ai dati, del diritto di condividere i dati con i terzi e infine sull’obbligo di mettere i dati di soggetti privati a disposizione di enti pubblici per necessità eccezionali. Insomma, la nuova stagione regolatoria europea libera nuovi flussi di dati tra settore pubblico e settore privato (Business to Government e Government to Business). Il contributo propone una rilettura in chiave critica di tali normative e promuove l’idea dei data for common good: un regime speciale per i dati detenuti da soggetti privati ma di pubblico interesse, che vada ad integrare le policy di sostenibilità sociale delle grandi imprese.

The paper addresses data sharing contracts for AI development and training, entered into by parties participating in AI value chains, in order to assess the incentives for responsible data sharing under European data law. As the governance of AI training data is mainly entrusted to party autonomy and the principles of the GDPR, the analysis first assesses whether contract theory, as it is traditionally conceptualized, is an appropriate tool to promote responsible data sharing. Then the paper delves into the impact of the mandatory data protection framework on data sharing contracts, to conclude that the principle of accountability in its organizational dimension requires the parties to 'contractualize' good data sharing practices. Lastly, the analysis comprises a brief overview of the relevant provisions of the AI Act that deal with data governance and AI value chains, to highlight their impact on the relationship between the entities participating in these value chains.

The current age is witnessing an unprecedented dependence on data originating from humans through the devices that comprise the Internet of Things. The data collected by these devices are used for many purposes, including predictive maintenance, smart analytics, preventive healthcare, disaster protection, and increased operational efficiency and performance. However, most applications and systems that rely on user data to achieve their business objectives fail to comply with privacy regulations and expose users to numerous privacy threats. Such privacy breaches raise concerns about the legitimacy of the data being processed. Hence, this paper reviews some notable techniques for transparently, securely, and privately separating and sharing personally identifiable and non-personally identifiable information in various domains. One of the key findings of this study is that, despite various advantages, none of the existing techniques or data sharing applications preserve data/user privacy throughout the data life cycle. Another significant issue is the lack of transparency for data subjects during the collection, storage, and processing of private data. In addition, as privacy is unique to every user, there cannot be a single autonomous solution to identify and secure personally identifiable information for users of a particular application, system, or people living in different states/countries. Therefore, this research suggests a way forward to prevent the leakage of personally identifiable information at various stages of the data life cycle in compliance with some of the common privacy regulations around the world. The proposed approach aims to empower data owners to select, share, monitor, and control access to their data. In addition, the data owner is a stakeholder and a party to all data sharing contracts related to his personal data. The proposed solution has broad security and privacy controls that can be tailored to the privacy needs of specific applications.

Blockchain technology has fascinated and significantly sparked research activities and industries due to its transparency in transactions and data sharing. The striking proliferation of blockchain technology is used to confront the challenges associated with trust, transparency, security, centralization, supply chain traceability, and regulatory compliance. The promising technology has the potential to enable several boons across diverse fields like healthcare, supply chain management, manufacturing, cross-border payments, finance, and energy trading. Telemedicine primarily aims to facilitate the transmission of healthcare data through electronic channels, enabling users to access medical services. It supports healthcare services around the globe, aids in early diagnosis and treatment, and assists with remote care by provisioning effective healthcare that is safe, secure, and reliable. However, there are challenges associated with Personal Health Records (PHR) due to lack of ownership of the data, accuracy, reliability, and data transaction security. The architecture presented in this work enables us to benefit from telemedicine and securely share PHR. The proposed model offers a permissioned network-based Hyperledger Fabric blockchain framework for secure PHR sharing amongst healthcare providers. The Byzantine Fault Tolerance consensus mechanism protects patient privacy, and the IPFS protocol is used to store the data off-chain. Additionally, the smart contract is utilized for providing patients with granular access control over PHR data. Hyperledger Caliper is used as a benchmarking tool to test this technology and determine the average latency rate for viewing and updating the PHR by healthcare providers along with analysis of CPU and memory utilization. As a result, our goals in telemedicine to improve secure sharing and give the patient access control are achieved.

Data that are restricted are historically challenging for researchers to find and even more difficult to access. While efforts to support open data have expanded in Canada, the same cannot be said for restricted data. To better understand the landscape of restricted data in Canada, this study aimed to accomplish two primary goals: (1) identify data sources where data were restricted and (2) assess a subset of health sciences data sources to determine how well they make their data discoverable and accessible. Our study identified 137 Canadian data sources, where 48 health sciences sources were evaluated for discoverability/accessibility. Data sources received poor grades with respect to data discovery due to a lack of metadata standards (38/48, 79%), an inability to find datasets through searching and browsing (32/46, 70%), and a lack of data documentation to support reuse (27/48, 56%). The absence of pricing information (31/48, 65%) and opaque dataset restrictions (25/48, 52%) were identified as key barriers to the data access request process. This study highlights significant room for improvement with respect to improving the discovery of and access to restricted data in Canada and makes recommendations for how to better support restricted data sources on a national scale.

To share data, more and more companies work together in interorganizational networks called data ecosystems (DEs). Research on DEs has grown significantly over the past ten years. Owing to the research field’s recent growth, interested scholars and practitioners might find it difficult to keep up to date with the diverse research field dealing with DEs. In this article, we conduct the first bibliometric study on DE research and collect and quantitatively analyze the research within this field. As a result, we come up with a synthesized overview of the most relevant research constituents. Furthermore, we analyze the thematic structure of DE research and show the most frequently addressed topics and key themes. We found that DE research can be divided into five different thematic clusters. Based on these results, we finally develop a future research agenda that can help advance research on DEs and guide interested scholars. As such, our article summarizes and consolidates the existing knowledge on DEs and provides suggestions for its further development.

The current publishing landscape grapples with opacity in the review process. In response, a proposal for a blockchain-driven system is put forth to establish transparent and auditable records for evaluations. However, despite its decentralized nature, concerns persist regarding confidentiality and secure data sharing, crucial for fostering future collaborations. To address these challenges, this study advocates for the implementation of an access control mechanism (ACM) to safeguard confidentiality. Under this mechanism, only the assigned reviewer would have access to the confidential manuscript, ensuring the integrity of the review process. In scientific collaborations, the imperative for confidential data sharing extends beyond reproducibility to encompass vital collaborative endeavors such as publications, Memorandums of Understanding (MoUs), grants, and funding. While hierarchical ACM may prove insufficient in defending confidential data, a more nuanced approach is proposed, leveraging a fine-grained access control model that considers contextual opinions, embodied in the concept of Zero Trust Architecture. Additionally, an incentivization mechanism based on author feedback is proposed to bolster reviewer engagement and credibility. In summary, this study aims to tackle trust and confidentiality concerns within the review system, facilitating secure data sharing for future collaborations while enhancing the credibility of reviewers. By advocating for a transition towards decentralized scientific collaboration and review processes, this work underscores the importance of integrating confidential review and data sharing practices, thereby fortifying the scholarly ecosystem.

Connecting smart industrial components to computer networks revolutionizes business operations. However, in the Industrial Internet of Things (IIoT), the sharing of data has bandwidth, computational, and privacy issues. Researchers presented cloud computing and fine-grained access control to overcome these challenges. However, traditional centralized computing systems involve single points of failure. To mitigate these challenges, we have proposed a secure and incentive-based data-sharing framework for IIoT systems using blockchain technology. We leverage blockchain due to its ability to provide secure and tamper-resistant data storage and sharing as participants store their data on a distributed ledger (DL), preventing unauthorized access. A security protocol is designed that utilizes the properties of elliptic curve cryptography (ECC). Moreover, Shapley value is employed to calculate revenue and distribute it fairly. To perform the formal security evaluation, we have conducted extensive simulations using the Automated Validation of Internet Security Protocols and Applications (AVISPA) and Scyther protocol simulation tools, which demonstrated that our protocol is robust against various adversarial attacks. The experimental results show that the proposed incentive distribution framework demonstrated fairness in the distribution of revenue among participants.

Relying on the low latency, high reliability, and large bandwidth of 5G networks, the Internet of Things (IoT) can more efficiently promote information interaction and data utilization across remote locations. However, publicly accessible IoT environments involve risks of data and privacy leakage, which threatens the security and feasibility of the cloud-assisted IoT system. Although attribute-based encryption technology provides a fine-grained and secure data sharing solution, it is still challenging to employ dynamic management of multiple groups when device nodes operate cooperatively in dynamic groups. Thus, we propose a dynamic multi-group-supporting policy-hidden secure data sharing scheme in 5G-enabled IoT. Specifically, we exploit a group key management scheme based on the Chinese Remainder Theorem to achieve efficient dynamic group management in IoT and implement concealment of data owners’ access policies to enhance privacy preservation. Moreover, an edge computing layer has been introduced in our 5G-enabled IoT system to entrust decryption to edge servers and cloud servers, thereby mitigating the calculation burden of data users and assisting to fortify resilience against potential collusion attacks. To improve the availability of our system, mechanisms for validating the correctness of the final ciphertext were also incorporated. We further proved the security of the proposed scheme and conducted experimental simulations, which affirm its efficiency and practicability.

According to the current situation of deep aging globally, how to provide low-cost and high-quality medical services has become a problem that the whole society needs to consider. To address these challenges, we propose an e-healthcare management system leveraging the integration of the Internet of Things (IoT) and blockchain technologies. Our system aims to provide comprehensive, reliable, and secure one-stop services for patients. Specifically, we introduce a blockchain-based searchable encryption scheme for decentralized storage and real-time updates of electronic health records (EHRs). This approach ensures secure and efficient data traceability across medical equipment, drug supply chains, patient health monitoring, and medical big data management. By improving information processing capabilities, our system aspires to advance the digital transformation of e-healthcare services.

Data spaces are conceptualised as a trusted and secure distributed data ecosystem through which to exchange resources in the Web. Several efforts define guidance toward data space implementation, such as reference architectures and frameworks. As yet, the proposed data space solutions do not provide common and mature implementation options yet, and this gap between concept and implementation risks confusing users and developers. However, well-recognised organisations have been developing solutions and standards that address interoperability and good data exchange practices for decades, especially in the domain of geospatial information and remote sensing. Therefore, this paper compares the available solutions, providing the mapping and integration of the proposed blueprints to available interoperable standards. This concrete mapping, followed by a discussion with experts, results in a proposal of integrated reference data space building blocks, and an overview of the related standards and solutions. It is designed to support the effective practical implementation of data spaces and to guide future solution developments. This work can form the base for effective collaboration among different organisations, clearly identifying their scopes. A key role is apparent for standards and use cases from the remote sensing and geospatial domains, which have achieved wide adoption and maturity over the past years.

The cyber–physical systems (CPSs) of various stakeholders from the mobility, logistics, and security sectors are needed to enable smart and secure situational awareness operations in a port environment. The motivation for this research arises from the challenges caused by some unexpected events, such as accidents, in such a multi-stakeholder critical environment. Due to the scale, complexity, and cost and safety challenges, a simulation-based approach was selected as the basis for the study. Prototype-level experimental solutions for dataspaces for secure data sharing and visualization of situational awareness were developed. The secure data-sharing solution relies on the application of verifiable credentials (VCs) to ensure that data consumers have the required access rights to the data/information shared by the data prosumer. A 3D virtual digital twin model is applied for visualizing situational awareness for people in the port. The solutions were evaluated in a simulation-based execution of an accident scenario where a forklift catches fire while loading a docked ship in a port environment. The simulation-based approach and the provided solutions proved to be practical and enabled the smooth study of disaster-type situations. The realized concept of dataspaces is successfully applied here for both daily routine operations and information sharing during accidents in the simulation-based environment. During the evaluation, needs for future research related to perception, comprehension, projection, trust, and security as well as performance and quality of experience were detected. Especially, distributed and secure viewpoints of objects and stakeholders toward real-time situational awareness seem to require further studies.

Summary: Background: To receive the best care, people share their health data (HD) with their health practitioners (known as sharing HD for primary purposes). However, during the past two decades, sharing for other (i.e., secondary) purposes has become of great importance in numerous fields, including public health, personalized medicine, research, and development. We aimed to conduct the first comprehensive overview of all studies that investigated people's HD sharing attitudes—along with associated barriers/motivators and significant influencing factors—for all data types and across both primary and secondary uses. Methods: We searched PubMed, MEDLINE, PsycINFO, Web of Science, EMBASE, and CINAHL for relevant studies published in English between database inception and February 28, 2023, using a predefined set of keywords. Studies were included, regardless of their design, if they reported outcomes related to attitudes towards sharing HD. We extracted key data from the included studies, including the type of HD involved and findings related to: HD sharing attitudes (either in general or depending on type of data/user); barriers/motivators/benefits/concerns of the study participants; and sociodemographic and other variables that could impact HD sharing behaviour. The qualitative synthesis was conducted by dividing the studies according to the data type (resulting in five subgroups) as well as the purpose the data sharing was focused on (primary, secondary or both). The Newcastle–Ottawa Scale (NOS) was used to assess the quality of non-randomised studies. This work was registered with PROSPERO, CRD42023413822. Findings: Of 2109 studies identified through our search, 116 were included in the qualitative synthesis, yielding a total of 228,501 participants and various types of HD represented: person-generated HD (n = 17 studies and 10,771 participants), personal HD in general (n = 69 studies and 117,054 participants), Biobank data (n = 7 studies and 27,073 participants), genomic data (n = 13 studies and 54,716 participants), and miscellaneous data (n = 10 studies and 18,887 participants). The majority of studies had a moderate level of quality (83 [71.6%] of 116 studies), but varying levels of quality were observed across the included studies. Overall, studies suggest that sharing intentions for primary purposes were observed to be high regardless of data type, and it was higher than sharing intentions for secondary purposes. Sharing for secondary purposes yielded variable findings, where both the highest and the lowest intention rates were observed in the case of studies that explored sharing biobank data (98% and 10%, respectively). Several influencing factors on sharing intentions were identified, such as the type of data recipient, data, consent. Further, concerns related to data sharing that were found to be mutual for all data types included privacy, security, and data access/control, while the perceived benefits included those related to improvements in healthcare. Findings regarding attitudes towards sharing varied significantly across sociodemographic factors and depended on data type and type of use. In most cases, these findings were derived from single studies and therefore warrant confirmations from additional studies. Interpretation: Sharing health data is a complex issue that is influenced by various factors (the type of health data, the intended use, the data recipient, among others) and these insights could be used to overcome barriers, address people's concerns, and focus on spreading awareness about the data sharing process and benefits. Funding: None.

Data sharing is increasingly important across various industries. However, issues such as data integrity verification during sharing, encryption key leakage, and difficulty sharing data between different user groups have been identified. To address these challenges, this study proposes a multi-group data sharing network model based on Consortium Blockchain and IPFS for P2P sharing. This model uses a dynamic key encryption algorithm to provide secure data sharing, avoiding the problems associated with existing data transmission techniques such as key cracking or data leakage due to low security and reliability. Additionally, the model establishes an IPFS network for users within the group, allowing for the generation of data probes to verify data integrity, and the use of the Fabric network to record log information and probe data related to data operations and encryption. Data owners retain full control over access to their data to ensure privacy and security. The experimental results show that the system proposed in this study has wide applicability.