Search

Core enterprises are entering into close collaborations with multi-level manufacturing service providers to create and share value. However, credible data sharing in collaborative manufacturing supply chains is rare, which can lead to uncontrollable production distortions and delayed operational adjustments. Driven by blockchain technology, this study proposes a systematic framework for credible manufacturing data sharing for a cross-enterprise collaborative manufacturing supply chain (CMSC). To develop the needed method, a blockchain network system with blockchain nodes, distributed ledger, and Raft-based distributed node ordering service system was first modelled. Then, two automatic data upload algorithms were studied to ensure the reliability of off-chain manufacturing data sources from core enterprises, suppliers, and customers. Then, two types of smart contracts were designed to ensure the standardisation and co-validation of manufacturing data storage and query processes in the blockchain network. A demonstrative case was studied to validate the proposed blockchain-based credible manufacturing data sharing method. The results show that our work is effective for credible data sharing in CMSC, which makes it easier for core enterprises to better operate the CMSC and further stimulate enterprises to create a credible social community. [ABSTRACT FROM AUTHOR]

This project convened a series of meetings and workshops to prioritize listening to multi-sector stakeholders from local government, advocacy, and industry in US cities where autonomous vehicles are operating. The objective was to listen and learn from all stakeholders, raise issues surrounding accessibility and equity, and to solicit responses. Key findings from the workshops include a consensus across the three sectors on the need for good channels of multi-stakeholder communication, and voices across all sectors agreed on the importance of disability access and serving diverse populations. Many parties, representing voices from all sectors, recognized that federal regulatory activities appear to be moving too slowly. Preventing any roadway incidents is a priority for many stakeholders, and some suggest a playbook for handling day-to-day roadway issues and common standards for first-responder interactions. Disability access is a high priority across all sectors, and there many see nearer term to accommodations for blind, hearing-impaired riders, but the timeline for providing service to people with non-folding wheelchairs is less clear. There is also ongoing debate surrounding the limits of regulatory purview, the role for cities, and how to actualize equitable expansion into rural areas. There is more work to do to advance a multi-sector dialogue around the role for local governments and community-based organizations in shepherding a safe, equitable and sustainable expansion of autonomous vehicles.

IntroductionFederal policies and guidelines have expanded the return of individual results to participants and expectations for data sharing between investigators and through repositories. Here, we report investigators' and study participants' views and experiences with data stewardship practices within frontotemporal lobal degeneration (FTLD) research, which reveal unique ethical challenges.MethodsSemi-structured interviews with (1) investigators conducting FTLD research that includes genetic data collection and/or analysis and (2) participants enrolled in a single site longitudinal FTLD study.ResultsAnalysis of the interviews identified three meta themes: perspectives on data sharing, experiences with enrollment and participation, and data management and security as mechanisms for participant protections.DiscussionThis study identified a set of preliminary gaps and needs regarding data stewardship within FTLD research. The results offer initial insights on ethical challenges to data stewardship aimed at informing future guidelines and policies.

The impact of preserved museum specimens is transforming and increasing by three-dimensional (3D) imaging that creates high-fidelity online digital specimens. Through examples from the openVertebrate (oVert) Thematic Collections Network, we describe how we created a digitization community dedicated to the shared vision of making 3D data of specimens available and the impact of these data on a broad audience of scientists, students, teachers, artists, and more. High-fidelity digital 3D models allow people from multiple communities to simultaneously access and use scientific specimens. Based on our multiyear, multi-institution project, we identify significant technological and social hurdles that remain for fully realizing the potential impact of digital 3D specimens.

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. [ABSTRACT FROM AUTHOR]

Blockchain has emerged as an innovative solution for organisations’ data-sharing. This paper utilises bibliometric content analysis to identify key keywords (identity management, privacy, and security issues) related to developments in blockchain adoption. We analysed 105 studies using VOSviewer software, which revealed three significant clusters. Our focus was primarily on the cluster of identity management and data sharing. While the existing literature has addressed privacy and security concerns, our study revealed a major emphasis on privacy issues, with less attention being given to the security aspects associated with potential blockchain consideration. Our discussion delineated the major themes that have arisen, around identity management, data sharing, privacy, security, and consortium blockchain. This paper reveals the link between enterprise blockchain and identity management, including the underlying factors and their interrelationships that highlight the significance of knowledge worthy of attention. These findings have implications for advancing the adoption of enterprise blockchain in a variety of businesses and promote further research. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a new well‐structured emerging technology with communication of smart devices using the 5G technology, infrastructures of roads, vehicles, smart cities, traffic systems and user applications. The IoT applications facilitate providing prompt emergency responses, and improved quality of vehicles, and road services, with cost‐effective activities in the intelligent transportation systems. Federated Learning (FL) enhances privacy and security in intelligent transportation systems and the Internet of Vehicles (IoV), using advanced prediction methods. Integrating blockchain with IoT, particularly in FL for transportation systems and IoV, bolsters security and data integrity. This approach keeps data local while only sharing model updates, enhancing privacy. Blockchain's transparency aids in efficient IoT collaboration, crucial for accountability. Its consensus algorithms further ensure network integrity, validating transactions and updates across devices, protecting against attacks, and fostering a transparent, collaborative environment. This comprehensive review paper delves into the innovative integration of blockchain technology with federated learning and the dynamic domain of IoV. It extensively analyzes the primary concepts, methodologies, and challenges associated with the deployment of FL in IoVs. This review presents a novel categorization examining three main types of blockchain‐based FL approaches vertical, horizontal, and decentralized each tailored to specific IoV communication scenarios like Vehicle‐to‐Vehicle (V2V), Vehicle‐to‐Infrastructure (V2I), and Vehicle‐to‐Cloud (V2C). It highlights FL applications in cyber‐attack detection, data sharing, traffic prediction, and privacy, considering Quality of Service factors. Finally, some main challenges and new open issues are discussed and assessed for federated machine learning approaches in the IoV. [ABSTRACT FROM AUTHOR]

To meet the increasing electricity demand and achieve the goals of "carbon peaking" and "carbon neutrality", it is urgent to develop a new power system with new energy as the main body and large‐scale integration of power intelligent electronic devices. However, the large‐scale access to intelligent devices will bring new network security issues. In response to network security threats, a power grid network security situational awareness system is established to assess the security situation of the power grid and strengthen the network security. Cloud data sharing enables security monitoring data to flow faster and be fully used to facilitate security situational research and judgment. Nevertheless, in reality, issues such as privacy leakage, data tampering, and unauthorized access hinder the application of cloud data sharing. To solve the above problems, we propose a new secure sharing scheme for power system security monitoring data based on batch verification ring signature. In this scheme, we design a batch verification ring signature algorithm to guarantee users' anonymity and promote the efficiency of data sharing. Then, we formally prove that our scheme achieves three security features: anonymity, confidentiality, and unforgeability. Finally, experimental simulations show that our scheme's computational cost is less than half that of other schemes, which also has lower communication overhead. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The purpose of this invited paper is to show the learning disabilities field what LDbase is, why it's important for the field, what it offers the field, and examples of how you can leverage LDbase in your own work. [ABSTRACT FROM AUTHOR]

Deep learning has impacted defect prediction in additive manufacturing (AM), which is important to ensure process stability and part quality. However, its success depends on extensive training, requiring large, homogeneous datasets--remaining a challenge for the AM industry, particularly for small- and medium-sized enterprises (SMEs). The unique and varied characteristics of AM parts, along with the limited resources of SMEs, hamper data collection, posing difficulties in the independent training of deep learning models. Addressing these concerns requires enabling knowledge sharing from the similarities in the physics of the AM process and defect formation mechanisms while carefully handling privacy concerns. Federated learning (FL) offers a solution to allow collaborative model training across multiple entities without sharing local data. This article introduces an FL framework to predict section-wise heat emission during laser powder bed fusion (LPBF), a vital process signature. It incorporates a customized long short-term memory (LSTM) model for each client, capturing the dynamic AM process's time-series properties without sharing sensitive information. Three advanced FL algorithms are integrated--federated averaging (FedAvg), FedProx, and FedAvgM--to aggregate model weights rather than raw datasets. Experiments demonstrate that the FL framework ensures convergence and maintains prediction performance comparable to individually trained models. This work demonstrates the potential of FL-enabled AM modeling and prediction where SMEs can improve their product quality without compromising data privacy. [ABSTRACT FROM AUTHOR]

A brief history of events surrounding the conceptualization and original implementation of the Alzheimer's Disease Neuroimaging Initiative (ADNI) as a public–private partnership (PPP) is provided from the perspective of three individuals directly involved from the outset. Potential barriers and how they were addressed are summarized, especially the decision to make all data freely accessible in real‐time. Decisions made at the beginning of ADNI are revisited in light of what has been learned over the past 20 years, especially the importance of the investment in cerebrospinal fluid (CSF) and blood measures and the commitment to data sharing. The key elements of ADNI's success from the authors' perspective are also summarized. Highlights: Informal interactions among colleagues were the beginning of something big.An NIH Director's personal decision on open data sharing has had perhaps the greatest impact of any single decision in the past several decades in terms of advancing clinical biomarker research.After 20 years, blood‐based biomarkers of brain disease may soon take the place of brain imaging for purposes of diagnosis and drug development. [ABSTRACT FROM AUTHOR]

Data sharing, the act of making scientific research data available to others, can accelerate innovation and discoveries, and ultimately enhance public health. The National Cancer Institute Implementation Science Centers in Cancer Control convened a diverse group of research scientists, practitioners, and community partners in three interactive workshops (May–June 2022) to identify and discuss factors that must be considered when designing research for equitable data sharing with a specific emphasis on implementation science and social, behavioral, and population health research. This group identified and operationalized a set of seven key considerations for equitable data sharing—conceptualized as an inclusive process that fairly includes the perspectives and priorities of all partners involved in and impacted by data sharing, with consideration of ethics, history, and benefits—that were integrated into a framework. Key data-sharing components particularly important for health equity included: elevating data sharing into a core research activity, incorporating diverse perspectives, and meaningfully engaging partners in data-sharing decisions throughout the project lifecycle. As the process of data sharing grows in research, it is critical to continue considering the potential positive and adverse impact of data sharing on diverse beneficiaries of health data and research. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Zhengzhou University (Natural Science Edition) is the property of Journal of Zhengzhou University (Natural Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Invoking a property rights approach, we examine how competition between advertising exchanges influences the targeting options that these exchanges make available to advertisers. When advertisers have strong property rights over data regarding consumers' active purchase interests, competition between ad exchanges leads to too little sharing of data. This may harm consumers, who receive too few pertinent ads, and advertisers themselves can also be harmed due to a situation resembling a prisoner's dilemma. We find that reallocating property rights to consumers, that is, giving them the right to opt out of tracking may also benefit consumers who allow tracking by altering the incentives of ad exchanges to offer improved targeting options. In addition, we show that initiatives by Apple and Google to limit third-party tracking and to introduce alternative tracking systems such as Google's Topics might benefit consumers by weakening the data property rights of advertisers. Because more data are shared by default under such systems, this can be true even if these systems are less accurate than third-party tracking systems. This paper was accepted by Joshua Gans, business strategy. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

AbstractOpen science (OS) is widely recognized as a new method of knowledge sharing using digital technologies. Despite the potential benefits OS offers, there is a low level of inspiration toward adopting it among researchers in higher learning institutions. The purpose of this study was to assess the faculty adoption of OS and their practices toward data sharing at Health Sciences University in Tanzania. The study employed a cross-sectional design, whereby a total of 252 faculties were systematically sampled from two purposefully selected health universities. Using a survey, the study employed SPSS to generate descriptive statistics and a coding strategy using ATLAS.ti software to analyze qualitative resultant data. Moreover, regression analysis determined the predicting factors that hindered faculty adoption of OS and data-sharing practices. The findings demonstrate that faculty had poor data-sharing practices triggered by a lack of institutional data-sharing policies, fear of data misinterpretation, and parasitic use of clinical research datasets with other researchers. However, the presence of a data repository, journal guidelines, and intellectual property rights are instrumental variables that allow faculty to practice OS and data sharing. Therefore, university libraries should take affirmative action to create awareness of the importance of practicing OS and data sharing among faculty and researchers. Such undertakings will allow other researchers to carry out comprehensive follow-up research to meet the increasing demand for innovative health research output in Tanzania. [ABSTRACT FROM AUTHOR]

In view of the security and privacy protection issues faced in sharing sensing information data between sensing devices in the internet of things, this study proposes a blockchain based multi domain data sharing mechanism. The mechanism treats the data sharing problem as a data transaction problem and introduces a trusted regulator to guarantee the interests of both buyers and sellers of the transaction. The mechanism provides a data publishing method in which buyers can securely publish data to a cloud storage service and do not disclose information about the data during the publishing process. A ring signature is used to sign and verify the buyer's licence in order to protect the buyer's privacy from being compromised during the data transaction. In addition, the mechanism provides an anonymous evaluation method that allows buyers to evaluate transactions and monitor seller behaviour without revealing their identity. The security analysis and simulation experiments results show that the mechanism is secure and efficient, meets the requirements for secure data sharing in the internet of things. [ABSTRACT FROM AUTHOR]

Open science practices are now at the forefront of discussions in the applied linguistics research community. Proponents of open science argue for its potential to enhance research quality and accessibility while promoting a collaborative and equitable environment. Winke advocates integrating open science into language assessment research to enhance research quality, accessibility, and collaboration. This response introduces two additional perspectives to support open science practices. The first is a framework, which identifies five schools of thought on open science that emphasize understanding the various goals of open science and the scientific methods and tools that are used to pursue them. Second, I highlight two additional characteristics of open science: the need for community and the costs of open science. These additional perspectives underscore the significance of making research processes transparent and inclusive, extending beyond traditional academic boundaries to engage the public and industry stakeholders. By integrating these considerations, this response aims to offer a nuanced view of the challenges and opportunities that open science presents in the field of language assessment, suggesting ideas for how researchers outside and inside the language assessment industry can work toward improving open science practices in language assessment research. [ABSTRACT FROM AUTHOR]

Different geographical locations have different time series and types of earthquake early warning data of hydropower stations, and the packet loss rate in data sharing is high. In this regard, a real-time sharing algorithm of earthquake early warning data of hydropower stations based on deep learning is proposed. The compressed sensing method is used to collect the seismic data of the hydropower station, and the dictionary learning algorithm based on ordered parallel atomic updating is introduced to improve the compressed sensing process and to sparse the seismic data of the hydropower station. Combining FCOS and DNN, the seismic velocity spectrum is picked up from the collected seismic data and used as the input of the convolutional neural network. The real-time sharing of earthquake early warning data is realized using the CDMA1x network and TCP data transmission protocol. Experiments show that the algorithm can accurately pick up the regional seismic velocity spectrum of hydropower stations, the packet loss rate of earthquake early warning data transmission is low, and the sharing results contain a variety of information, which can provide a variety of data for people who need information and has strong practicability. [ABSTRACT FROM AUTHOR]

Current statistical query pricing models have not considered the problem that query results reveal sensitive attributes of datasets, making it difficult to incentivize sharing by compensating data providers accordingly. Therefore, this paper proposed a pricing model based on attribute privacy. Firstly, the model calculated query sensitivity based on the relaxed approximation Wasserstein mechanism (RAWM) proposed, improving efficiency by directly calculate the relaxed upper bound of output distribution pairs. Then, with bounding privacy loss, the model compensated data providers based on query sensitivity, noise variance and compensation parameters. Finally, by using cost-plus pricing on compensation, this paper designed several arbitrage-free pricing functions, which could be used in scenarios such as single compensation costs and multiple marginal costs. The experiment results show that the running time of calculating query sensitivity is reduced from linear complexity to constant complexity, with a utility cost of only 0.52% when data volume is 100 million. The pricing model provides fine-grained compensation to incentivize sharing. Pricing functions satisfy arbitrage freeness. [ABSTRACT FROM AUTHOR]

Large data-intensive health research programmes benefit from collaboration amongst researchers who may be located in different institutions and international contexts. However, complexities in navigating privacy frameworks and data protection laws across various jurisdictions pose significant challenges to researchers seeking to share or transfer data outside of institutional boundaries. Research on the awareness of data protection and privacy laws amongst stakeholders is limited. Our qualitative study, drawn from a larger project in Singapore, revealed insights into stakeholders' perceptions of the role of law in cross-national health data research. Stakeholders in our study demonstrated a range of perceptions regarding the role of data protection law in governing the collection and transfer of health data for research. The main criticisms included inadequate legal protection to data and lack of uniformed data protection standards. Despite these criticisms, participants recognised the importance of data protection law in supporting cross-border data transfers and proposed measures to improve perceived limitations of existing laws. These measures include strengthening existing legal framework, establishing contractual agreements and imposing severe punishments for data misuse. [ABSTRACT FROM AUTHOR]

Global health emergencies often lead to a proliferation of health-related research and resultant data, which is shared across borders to help control the outbreak of disease and support decision-making regarding public health interventions. However, efforts to share data can be hindered by diverse international ethical and legal frameworks. The frameworks aim to govern coordinated processing, sharing and transfer of health data across borders thus placing burdens on researchers who are willing or obligated to share data. In this paper, we investigate ethical values and legal principles that can be applied to ensure a more streamlined approach in the governance of cross-border health data sharing during global health emergencies. A scoping review approach was used to better understand emerging evidence about issues related to the ethical and legal governance of cross-border health data sharing. We first highlight the challenges of sharing health data across borders. We then discuss how the challenges might be overcome through the application of ethical values and legal principles that have been identified in literature. This is followed by a proposal for possible ways of streamlining the governance of health data sharing from ethical and legal perspectives in a bid to better prepare for future global health emergencies and promote the responsible sharing of health data. [ABSTRACT FROM AUTHOR]

Neuroimaging databases for neuro‐psychiatric disorders enable researchers to implement data‐driven research approaches by providing access to rich data that can be used to study disease, build and validate machine learning models, and even redefine disease spectra. The importance of sharing large, multi‐center, multi‐disorder databases has gradually been recognized in order to truly translate brain imaging knowledge into real‐world clinical practice. Here, we review MRI databases that share data globally to serve multiple psychiatric or neurological disorders. We found 42 datasets consisting of 23,293 samples from patients with psychiatry and neurological disorders and healthy controls; 1245 samples from mood disorders (major depressive disorder and bipolar disorder), 2015 samples from developmental disorders (autism spectrum disorder, attention‐deficit hyperactivity disorder), 675 samples from schizophrenia, 1194 samples from Parkinson's disease, 5865 samples from dementia (including Alzheimer's disease), We recognize that large, multi‐center databases should include governance processes that allow data to be shared across national boundaries. Addressing technical and regulatory issues of existing databases can lead to better design and implementation and improve data access for the research community. The current trend toward the development of shareable MRI databases will contribute to a better understanding of the pathophysiology, diagnosis and assessment, and development of early interventions for neuropsychiatric disorders. [ABSTRACT FROM AUTHOR]

EHRs sharing systems provide a secure and efficient way for patients and doctors to share information in smart healthcare. Due to the concern about data confidentiality and authorized access, the exploitation of attribute-based encryption (ABE) is widely adopted for EHRs sharing in cloud storage. However, most ABE-based EHRs sharing system only considered unilateral access control or supported non-flexible bilateral access control. Hence, we propose a lightwight and flexible healthcare data sharing system, LiVeRe, which enables fine-grained bilateral access control and moreover supports efficient user revocation and ciphertext integrity verification for the access control property. Technically, we employ the dual-policy framework to specify the access policy and pre-decryption techniques to alleviate the computational burden, and efficient revocation of user access rights by the KUNode algorithm. We also provide formal security models and correspondingly prove its security. Moreover, we conduct experiments on the cloud to demonstrate the practicality of our LiVeRe scheme. [ABSTRACT FROM AUTHOR]

The Alzheimer's Disease Neuroimaging Initiative (ADNI) has revolutionized the landscape of Alzheimer's research through its Informatics Core, which has facilitated unprecedented data standardization and sharing. Over 20 years, ADNI established a robust informatics framework, enabling the validation of biomarkers and supporting global research efforts. The Informatics Core, centered at the Laboratory of Neuro Imaging (LONI), provides a comprehensive data hub that ensures data quality, accessibility, and security, fostering over 5600 publications and significant scientific advancements. By embracing open data sharing principles, ADNI set a gold standard in data transparency, allowing over 26,000 investigators from 169 countries to access and download a wealth of multimodal data. This collaborative approach not only accelerated biomarker discovery and drug development and advanced our understanding of Alzheimer's disease but also has served as a model for other research initiatives, demonstrating the transformative potential of carefully designed informatics models and shared data in driving global scientific progress. Highlights: Accelerating biomarker discovery and drug development for Alzheimer's disease.Alzheimer's Disease Neuroimaging Initiative's (ADNI's) open data sharing drives scientific progress.Data exploration and coupled analytics to data archives. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Purpose: This study has been focused on assessing the Open Science scenario of cancer research during the period 2011–2021, in terms of the derived scientific publications and raw data dissemination. Methods: A cancer search equation was executed in the Science Citation Index-Expanded, collecting the papers signed by at least one Spanish institution. The same search strategy was performed in the Data Citation Index to describe dataset diffusion. Results: 50,822 papers were recovered, 71% of which belong to first and second quartile journals. 59% of the articles were published in Open Access (OA) journals. The Open Access model and international collaboration positively conditioned the number of citations received. Among the most productive journals stood out Plos One, Cancers, and Clinical and Translational Oncology. 2693 genomics, proteomics and metabolomics datasets were retrieved, being Gene Expression Omnibus the favoured repository. Conclusions: There has been an increase in oncology publications in Open Access. Most were published in first quartile journals and received higher citations than non-Open Access articles, as well as when oncological investigation was performed between international research teams, being relevant in the context of Open Science. Genetic repositories have been the preferred for sharing oncology datasets. Further investigation of research and data sharing in oncology is needed, supported by stronger Open Science policies, to achieve better data sharing practices among three scientific main pillars: researchers, publishers, and scientific organizations. [ABSTRACT FROM AUTHOR]

Data trading enables data owners and data requesters to sell and purchase data. With the emergence of blockchain technology, research on blockchain-based data trading systems is receiving a lot of attention. Particularly, to reduce the on-chain storage cost, a novel paradigm of blockchain and cloud fusion has been widely considered as a promising data trading platform. Moreover, the fact that data can be used for commercial purposes will encourage users and organizations from various fields to participate in the data marketplace. In the data marketplace, it is a challenge how to trade the data securely outsourced to the external cloud in a way that restricts access to the data only to authorized users across multiple domains. In this paper, we propose a cross-domain bilateral access control protocol for blockchain-cloud based data trading systems. We consider a system model that consists of domain authorities, data senders, data receivers, a blockchain layer, and a cloud provider. The proposed protocol enables access control and source identification of the outsourced data by leveraging identity-based cryptographic techniques. In the proposed protocol, the outsourced data of the sender is encrypted under the target receiver's identity, and the cloud provider performs policy-match verification on the authorization tags of the sender and receiver generated by the identity-based signature scheme. Therefore, data trading can be achieved only if the identities of the data sender and receiver simultaneously meet the policies specified by each other. To demonstrate efficiency, we evaluate the performance of the proposed protocol and compare it with existing studies. [ABSTRACT FROM AUTHOR]

Data curation is crucial for data reusability. New possibilities for digital data sharing are an urgent concern for data curators, who must keep historical datasets and present data collections always ready to meet unknown future data needs. This calls for a more nuanced understanding of the temporal horizons of data curation in Information Systems research. Based on a qualitative interpretive case study of data management in an environmental monitoring infrastructure, we characterise three data curation practices to support data reuse. These practices follow three interleaving temporal perspectives: retrospective (by upgrading historical datasets), present-oriented (by monitoring ongoing data collections), and future-looking (by disseminating data). We conceptualise this work as anticipatory generification, involving continuous and temporally oriented data curation to maintain data sufficiently open-ended to anticipate future data reusability. Anticipatory generification is essential for the sustainable evolution of environmental data infrastructures. Our study contributes to the Information Systems literature by further theorising the temporal perspectives of data infrastructures and providing additional insight into how the future is anticipated in practice. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Sharing large digital-first datasets, including for purposes for which they were not originally intended, is a hallmark of the “big data revolution”. Through their routine operations, private commercial companies collect massive amounts of diverse data from their customers, some of which may interest those working in the public sector, such as health researchers. Researchers and government agencies worldwide have been increasingly using data from commercial entities (such as Google, Microsoft, Apple, Facebook/Meta, Twitter/X and Amazon, among others) to generate health-related insights. This article explores ethical issues raised by the practice of commercial companies sharing consumer data with third parties for the purposes of promoting health in the sub-Saharan African (SSA) context. First, as an illustrative example, it examines some of the ways telecommunication (telecom) companies in SSA shared mobility data from cellphone users with public health researchers during the COVID-19 pandemic. Second, it examines a recent debate about the ethical responsibilities of companies that collect, process and share user-generated data, drawing implications for the SSA context. Finally, since this is a relatively understudied subject, we point out some areas where future conceptual and empirical work could contribute to the development of relevant ethics guidance and regulatory governance in SSA. [ABSTRACT FROM AUTHOR]

With the rapid advancement of information technology, the application of mobile technology in enterprise management has become increasingly widespread. From mobile office solutions to intelligent decision support systems, the proliferation of mobile technology has not only enhanced operational efficiency but also provided more flexible and convenient management methods. Existing research indicates that the adoption of mobile office solutions enables enterprises to overcome limitations of time and space, improving work efficiency and employee satisfaction. Additionally, the implementation of intelligent decision support systems allows enterprises to make faster and more accurate business decisions. However, current studies often focus on individual mobile technologies or application scenarios, lacking a systematic exploration of mobile office data sharing and intelligent decision-making collaboration. Moreover, most research overlooks privacy protection issues in the enterprise management decision-making process. To address these gaps, this paper proposes a scheme for mobile office data sharing and intelligent decision-making collaboration and examines the integration of privacy protection in the decision-making process. The findings not only enrich the theoretical framework of management studies but also provide enterprises with a systematic and practical solution for mobile technology applications, enhancing management efficiency and intelligence while effectively safeguarding data privacy and security. [ABSTRACT FROM AUTHOR]

Introduction Methods Results Conclusion Sharing patient health information and biospecimens can improve health outcomes and accelerate breakthroughs in medical research. But patients generally lack understanding of how their clinical data and biospecimens are used or commercialized for research. In this mixed methods project, we assessed the impact of communication materials on patient understanding, attitudes, and perceptions.Michigan Medicine patients were recruited for a survey (n = 480) or focus group (n = 33) via a web‐based research study portal. The survey assessed the impact of mode of communication about health data and biospecimen sharing (via an informational poster vs. a news article) on patient perceptions of privacy, transparency, comfort, respect, and trust. Focus groups provided in‐depth qualitative feedback on three communication materials, including a poster, FAQ webpage, and a consent form excerpt.Among survey respondents, the type of intervention (poster vs. news) made no statistically significant difference in its influence on any characteristic. However, 95% preferred that Michigan Medicine tell them about patient data and biospecimen research sharing versus hearing it from the news. Focus group participants provided additional insights, discussing values and perceptions of altruism and reciprocity, concerns about commercialization, privacy, and security; and the desire for consent, control, and transparency.Developing our understanding of patient data‐sharing practices and integrating patient preferences into health system policy, through this work and continued exploration, contributes to building infrastructure that can be used to support the development of a learning health system across hospital systems nationally. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In the Internet of Medical Things (IoMT), the vulnerability of federated learning (FL) to single points of failure, low‐quality nodes, and poisoning attacks necessitates innovative solutions. This article introduces a FL‐driven dual‐blockchain approach to address these challenges and improve data sharing and reputation management. Our approach comprises two blockchains: the Model Quality Blockchain (MQchain) and the Reputation Incentive Blockchain (RIchain). MQchain utilizes an enhanced Proof of Quality (PoQ) consensus algorithm to exclude low‐quality nodes from participating in aggregation, effectively mitigating single points of failure and poisoning attacks by leveraging node reputation and quality thresholds. In parallel, RIchain incorporates a reputation evaluation, incentive mechanism, and index query mechanism, allowing for rapid and comprehensive node evaluation, thus identifying high‐reputation nodes for MQchain. Security analysis confirms the theoretical soundness of the proposed method. Experimental evaluation using real medical datasets, specifically MedMNIST, demonstrates the remarkable resilience of our approach against attacks compared to three alternative methods. [ABSTRACT FROM AUTHOR]

Public sector data associated with health are a highly valuable resource with multiple potential end-users, from health practitioners, researchers, public bodies, policy makers, and industry. Data for infectious disease agents are used for epidemiological investigations, disease tracking and assessing emerging biological threats. Yet, there are challenges in collating and re-using it. Data may be derived from multiple sources, generated and collected for different purposes. While public sector data should be open access, providers from public health settings or from agriculture, food, or environment sources have sensitivity criteria to meet with ethical restrictions in how the data can be reused. Yet, sharable datasets need to describe the pathogens with sufficient contextual metadata for maximal utility, e.g. associated disease or disease potential and the pathogen source. As data comprise the physical resources of pathogen collections and potentially associated sequences, there is an added emerging technical issue of integration of omics 'big data'. Thus, there is a need to identify suitable means to integrate and safely access diverse data for pathogens. Established genomics alliances and platforms interpret and meet the challenges in different ways depending on their own context. Nonetheless, their templates and frameworks provide a solution for adaption to pathogen datasets. [ABSTRACT FROM AUTHOR]

Biobanking plays a pivotal role in biomedical research by providing standardized processing, precise storing, and management of biological sample collections along with the associated data. Effective data management is a prerequisite to ensure the integrity, quality, and accessibility of these resources. This review provides a current landscape of data management in biobanking, discussing key challenges, existing strategies, and potential future directions. We explore multiple aspects of data management, including data collection, storage, curation, sharing, and ethical considerations. By examining the evolving technologies and methodologies in biobanking, we aim to provide insights into addressing the complexities and maximizing the utility of biobank data for research and clinical applications. [ABSTRACT FROM AUTHOR]

Mobile devices are pervasive in today's world, and the ability to create ad-hoc networks among these devices enables increased connectivity. Peer-to-peer (P2P) Mobile Ad-hoc Network (P2P MANET) offer flexibility and scalability. Devices can join or leave the network seamlessly, and the network can adapt to changing conditions. This makes them suitable for scenarios with varying numbers of devices or where the network topology changes frequently. Moreover, the P2P network is a robust, fault-tolerant, and distributed network for sharing the resources like CPU, memory, files, etc. However, solid mobility, malicious behavior and short wireless transmission range more risk to searching the mobile P2P network resources. In this paper, a novel Vulture-based Deep Belief Model (VbDBM) is designed to enhance the performance of resource search in a mobile P2P network. Also, it will neglect the attacks and errors are presented during the transmission stage. In addition, the VbDBM framework is implemented in the NS-2 tool, and there are nine nodes created for resource search. Initially, the developed technique analyzes the user needs and identifies the neighboring peer of the user location with the help of vulture fitness. At next, the proposed technique provides the resource to the volunteer node (neighboring peer). Finally, the volunteer node offers the resource to the user. The attained performance metrics of developed VbDBM are compared with other existing techniques in terms of search rate, simulation time, data transfer rate, number of Data, and throughput. From the comparison proposed algorithm has improves 350 kbps throughput and 28% search rate respectively. [ABSTRACT FROM AUTHOR]

Data sharing is increasingly an expectation in health research as part of a general move toward more open sciences. In the United States, in particular, the implementation of the 2023 National Institutes of Health Data Management and Sharing Policy has made it clear that qualitative studies are not exempt from this data sharing requirement. Recognizing this trend, the Palliative Care Research Cooperative Group (PCRC) realized the value of creating a de-identified qualitative data repository to complement its existing de-identified quantitative data repository. The PCRC Data Informatics and Statistics Core leadership partnered with the Qualitative Data Repository (QDR) to establish the first serious illness and palliative care qualitative data repository in the U.S. We describe the processes used to develop this repository, called the PCRC-QDR, as well as our outreach and education among the palliative care researcher community, which led to the first ten projects to share the data in the new repository. Specifically, we discuss how we co-designed the PCRC-QDR and created tailored guidelines for depositing and sharing qualitative data depending on the original research context, establishing uniform expectations for key components of relevant documentation, and the use of suitable access controls for sensitive data. We also describe how PCRC was able to leverage its existing community to recruit and guide early depositors and outline lessons learned in evaluating the experience. This work advances the establishment of best practices in qualitative data sharing. [ABSTRACT FROM AUTHOR]

The present manuscript reports on the progress made toward the official announcement of the first World Conference on Cellular Communication and Signaling. This conference is made possible by the Association for research on biosignaling and communication initiative, which was originally launched in 2020 and revitalized during the 12th International Workshop on the Cell Communication Network family of genes in Oslo (June 20–23, 2024). The aim of this conference is to facilitate interactions among the members of societies interested in all aspects of research on Biosignaling and Communication. It is intended to provide a platform for collaborative efforts aimed at unraveling and understanding the functioning of biological pathways in both normal and pathological conditions. [ABSTRACT FROM AUTHOR]

Copyright of Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Many important questions in health professions education require datasets that are built from several sources, in some cases using data collected for a different purpose. In building and maintaining these datasets, project leaders will need to make decisions about the data. While such decisions are often construed as technical, there are several normative concerns, such as who should have access, how the data will be used, how products resulting from the data will be shared, and how to ensure privacy of the individuals the data is about is respected, etc. Establishing a framework for data governance can help project leaders in avoiding problems, related to such matters, that could limit what can be learned from the data or that might put the project (or future projects) at risk. In this paper, we highlight several normative challenges to be addressed when determining a data governance framework. Drawing from lessons in global health, we illustrate three kinds of normative challenges for projects that rely on data from multiple sources or involved partnerships across institutions or jurisdictions: (1) legal and regulatory requirements, (2) consent, and (3) equitable sharing and fair distribution. [ABSTRACT FROM AUTHOR]

In the financial system, the customers' willingness to share their data is pivotal, because otherwise, banks and insurance companies are powerless to build on customer data. The key step now is to understand whether there is such willingness and what form it takes. In this study, we investigate how willing customers are to share various kinds of data (on physical health, home, driving style, travel, family, social networks) with their insurance company, in return for different rewards (customised products and services, reduced insurance claims risk and insurance premiums adjusted to personal habits and behaviour). Applying the privacy calculus framework to 1501 responses in a web-based survey, we found that rewards, especially when financial, such as insurance premium benefits, play a pivotal role in driving customer decisions about sharing data. Furthermore, customers associate the data they are asked to share with different levels of privacy, influencing their willingness to share. We also found that, when customers are asked to share various kinds of data in return for different rewards, their own personal innovativeness comes into play. Our findings suggest that, in the data-driven insurance business, different rewards offered in return for specific types of data could help companies minimise the "data acquisition cost" and maximise the data collected. In the era of open data, insurers can explore the many opportunities for segmentation, but new kinds of financial exclusion could emerge, resulting in potential biases and thus misinterpretations should analytics and artificial intelligence models be built upon these premises. [ABSTRACT FROM AUTHOR]