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Data democratization (DD) is a new concept rapidly becoming a game-changer, enabling companies to innovate and maintain a competitive edge in a data-driven world. This paper explores the evolution of data accessibility, from the early days of manual record-keeping to the sophisticated data management systems of today. The evolution from transactional databases to data warehouses marked a shift toward centralized data management and specialized teams, supporting the standard principles of DD contexts such as data governance (DG), privacy, management, usability, accessibility, and literacy. This paper provides an overview of the evolution of data access, from manual record-keeping to the modern data management systems of today, focusing on the challenges related to data privacy and security, integration of legacy systems, and the cultural shift required to embrace a data-driven mindset. This paper also explores both universal and holistic approaches to DD, assessing the challenges, benefits, and possibilities of their applications. An overview of industry-specific cases is included in the paper to provide practical insights that would contribute to understanding the most effective approach to data democratization. [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]

Currently, with the advancement of artificial intelligence research, artificial intelligence is being widely adopted, and the increasing demand in areas such as data governance has led to growing awareness and concern for privacy protection, this has promoted the popularity of the federated learning (FL) framework. However, existing FL frameworks struggle to address heterogeneous issues and personalized user needs. In response to these challenges, methods of personalized federated learning (PFL) are studied and prospects are proposed. Firstly, the FL framework is outlined and its limitations are identified, leading to the research motivation for PFL based on FL scenarios. Subsequently, the analysis of statistical heterogeneity, model heterogeneity, communication heterogeneity, and device heterogeneity in PFL is conducted, and feasible solutions are proposed. Then, personalized algorithms in PFL such as client selection and knowledge distillation are categorized, and their innovations and shortcomings are analyzed. Finally, future research directions for PFL are discussed. [ABSTRACT FROM AUTHOR]

Elevating Indigenous leadership in conservation science is critical for social and ecological wellbeing. However, Indigenous knowledges are frequently undermined by persistent colonial research standards. In response, calls to implement ethical guidelines that advance Indigenous research and data governance are mounting. Despite this growing movement, most environmental studies continue to follow largely colonial, extractive models, presenting a widening gap between ethical guidelines and practical applications across diverse research contexts. To address this gap, our study aims to design and evaluate a wildlife conservation research project based on the Relational Science Model, which outlines guidance for improving research relations with Indigenous Peoples. To achieve this aim, we conducted a post-survey to evaluate the project from the perspectives of the intended beneficiaries of La Bendición in southwestern Guatemala, accompanied by researcher reflections and observations. The results revealed strong agreement between community research partner experience and Relational Science Model outcomes, including relevant and innovative knowledge generation, alongside improved trust in research collaborations. Respondents also outlined several areas of improvement, including a desire for more diverse community engagement, particularly regarding youth. Overall, this study outlines pathways and recommendations for researchers, institutions, and agencies to improve relational accountability in conservation science practice, supporting Indigenous conservation governance and environmental justice. [ABSTRACT FROM AUTHOR]

In response to the current issues such as low data availability, weak data sharing and service capabilities, and restricted dada efficiency release of joint operation teaching and training, the basic issues of joint operation teaching and training data governance is researched from the perspective of top-level design. Firstly, the concept of joint operation teaching and training data governance is proposed and the status of joint operation teaching and training data is analyzed deeply. Secondly, according to the actual situation of the institution, the model of joint operation teaching and training data governance is built to provide logical structure and action plan for joint operation teaching and training data governance activities. Finally, the platform of joint operation teaching and training data governance is preliminary designed to provide reliable support. [ABSTRACT FROM AUTHOR]

Data are undoubtedly a contested commodity. On the one hand, data commodification is largely under way including through the operation of law. This is notably visible with the new EU data policy (Data Strategy notably followed by the Data Act, the Data Governance Act) that aims to establish data markets in keeping with European values. On the other hand, this phenomenon is heavily contested based on a wide range of different arguments, which have not been subjected to a systematic clustering. Data commodification is often understood simplistically as a binary and monolithic phenomenon whereby data would be either 'commodified' or not. This leads to misunderstandings of this phenomenon and of the ways in which it manifests. For example, many conceptual misunderstandings surround the relationship between 'data access' or 'data sharing' and data commodification and markets. This paper clarifies the phenomenon of data commodification, by approaching it as a spectrum with degrees following M. J. Radin (Contested Commodities, 1996). Following Radin, the paper clusters the different data governance arguments – or even paradigms – found in the literature along a data commodification spectrum. A specific attention is paid to the importance of market discourses in commodification dynamics, especially on the law. The paper offers a novel systematic synthesis of the data governance normative arguments, including data commons, data trusts, data sharing, data intermediation, against the background of the data commodification phenomenon. This synthesis brings conceptual clarity and allows to bring together different strands of the data literature (especially welfare economics, law and economics, commons, critical data studies, infrastructure studies) comprehensively, while they have until now remained siloed. Data governance normative arguments would greatly benefit from taking into account (conceptual and/or normative) arguments found in other strands of the literature. The paper can also be used to evaluate how data governance arguments or legislations relate to data commodification and take into account the specificities of data, thus enabling for more systematic analysis. The paper finds that data is actually a very contested commodity: The very conceptualization of data as a commodity is ontologically contestable. The framework in which situations are conceptualized – whether as data market ones or not – can indeed play a powerful but often invisible discursive role on commodification dynamics. Finally, the very identification and regulation of 'data' alone necessarily brings about certain commodification affordances. [ABSTRACT FROM AUTHOR]

The European Data Strategy, published in 2020, aims to turn the EU into a society empowered by data which captures its benefits to generate improvements in health, well-being, the environment, transparent governance, and convenient public services. This article examines the European Data Strategy, focusing on the Data Act and Data Governance Act, and situates them within broader data governance discussions. It argues that the Strategy has at its centre a tension between fostering a robust, fair data market versus scepticism towards market-based data governance mechansims, and analyses to what extent the regulatory techniques the Data Strategy proposes can address this tension. [ABSTRACT FROM AUTHOR]

Copyright of Taiwan Law Review is the property of Angle Publishing Co., Ltd 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.)

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]

In its regulatory enterprise to improve the conditions of data sharing and reuse, the European Union has enacted new legislation: the Data Governance Act (DGA). The DGA envisages new forms of sharing public sector data (PSD). Based on a legal analysis of the DGA and an in-depth study of data governance literature, this paper highlights what is at stake in the new regulatory framework and argues that more than the mere openness of more PSD will be necessary to ensure that the European Union policy goals are achieved, especially those concerning enhancing innovation for the common good. From this perspective, the paper argues that the public data trust model of data governance and the Responsible Research and Innovation approach offer two powerful tools for public sector data governance. In this context, this paper contributes to the debate about new data governance models and discusses tools and frameworks enabling the use of data for the common good. It also provides insights to public administration practitioners aiming to implement a framework for increased and sustainable PSD sharing. [ABSTRACT FROM AUTHOR]

Copyright of Cyber Security & Data Governance is the property of Editorial Office of Information Technology & Network Security 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.)

Copyright of Cyber Security & Data Governance is the property of Editorial Office of Information Technology & Network Security 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.)

IMPACT: To foster data-driven policy-making practices there is a need for capacity-building within the public administration (PA) to collect, access, manage and reuse data. This article draws from focus groups and interviews to propose measures to make dataspaces scalable and accessible for smaller actors so that they can participate in data-driven policy-making—both in terms of their capacity to input data in a dataspace and to reuse existing data. This article speaks to PA and data governance scholars, as well as PA and data management practitioners—particularly at the local level in Europe. This article assesses the obstacles and best practices in data gathering and reuse for public administrations (PAs). Through a qualitative analysis, it finds that smaller PAs often lack the resources to harness data in policy-making. This means that data-driven policy-making is currently more applicable to larger organizations. Theoretically, this article critically challenges the idea that pooling data from various sources expands the boundaries of rationality through collective rationality, as several institutional actors remain excluded from data-driven policy-making due to lack of resources. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to address the generalised lack of guidance on ethical treatment of corporate (e.g. non-research) data in higher education institutions, by focusing on the case of the University of Queensland (Brisbane, Australia). No actionable framework is currently available in the country to govern the ethical usage of corporate data. As such, this research takes a stakeholder-centred approach to data ethics; the lived experience of the stakeholders involved coupled with a theory-based ethical framework allowed the authors build to build a framework to guide ethical data practice. Design/methodology/approach: Adopting a revised canonical action research approach focused on intervention on the context, the authors conducted a review of the literature on ethical usage of data in higher education institutions; administered one survey to university students (n = 168); and facilitated three workshops with professional staff (two) and students (one). Findings: Collected data highlighted how, among other themes, the role and ethical importance of transparency was the dominant claim among all stakeholder groups. Findings helped the authors develop an Enhanced Enterprise Data Ethics Framework (EEDEF) emphasising transparency and stakeholder-centricity. Practical implications: Legislation is the driver to regulate the use of corporate data in higher education; however, this can be problematic because legislation is retrospective, lacks normativity and offers scarce directions for cases that do not exactly follow within the legislative mandate. In light of these regulatory limitations, the authors' EEDEF offers operators guidance on how to ethically manage corporate data in the higher education environment. Originality/value: This study fills gaps in praxis and theory; that is the lack of literature and guiding ethical frameworks to inform data practice in higher education. This research fosters a more ethical data management by virtue of genuine and authentic engagement with stakeholders and emphasises the importance of strategic decision-making and maturity of data culture in the higher education sector. [ABSTRACT FROM AUTHOR]

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]

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.

In response to the current issues such as low data availability, weak data sharing and service capabilities, and restricted dada efficiency release of joint operation teaching and training, the basic issues of joint operation teaching and training data governance is researched from the perspective of top-level design. Firstly, the concept of joint operation teaching and training data governance is proposed and the status of joint operation teaching and training data is analyzed deeply. Secondly, according to the actual situation of the institution, the model of joint operation teaching and training data governance is built to provide logical structure and action plan for joint operation teaching and training data governance activities. Finally, the platform of joint operation teaching and training data governance is preliminary designed to provide reliable support.

Grace Crossette-Thambiah, Daniel Berleant, Ahmed AbuHalimeh Department of Information Science, University of Arkansas at Little Rock, Little Rock, AR, USACorrespondence: Daniel Berleant, Email jdberleant@ualr.eduIntroduction: Data and information quality play a critical role in the managed healthcare sector, where accurate and reliable information is crucial for optimal decision-making, operations, and patient outcomes. However, managed care organizations face significant challenges in ensuring information quality due to the complexity of data sources, regulatory requirements, and the need for effective data management practices. The goal of this article is to develop and justify an information quality framework for managed healthcare, thereby enabling the sector to better meet its unique information quality challenges.Methods: The information quality framework provided here was designed using other information quality frameworks as exemplars, as well as a qualitative survey involving interviews of twenty industry leaders structured around 17 questions. The responses were analyzed and tabulated to obtain insights into the information quality needs of the managed healthcare domain.Results: The novel framework we present herein encompasses strategies for data integration, standardization and validation, and is followed by a justification section that draws upon existing literature and information quality frameworks in addition to the survey of leaders in the industry.Discussion: Emphasizing objectivity, utility, integrity, and standardization as foundational pillars, the proposed framework provides practical guidelines to empower healthcare organizations in effectively managing information quality within the managed care model.Keywords: data quality, information quality, information quality framework, managed healthcare, managed health care, data management, data governance

Sextech is currently experiencing a golden age, promising technological innovation to improve sexual health and well-being. However, the privacy and security vulnerabilities of smart sex toys have been the subject of media attention. Dating apps, menstrual trackers and sex toy companies have paid millions in compensation for non-consensually collecting or sharing intimate data. In this article, we share findings from a research workshop with prospective sextech industry professionals about how they approach data governance. The conversations reveal disconnections between the emancipatory, collective and rights-based possibilities offered by feminist and queer tech cultures, broader public interest in data commons and the technosolutionist narratives of start-up cultures. We conclude that there is a need for collaborations between industry, community and researchers to develop approaches to governance that reorganise, redistribute and decentralise the data economy of sex tech. [ABSTRACT FROM AUTHOR]

Abstract This special issue explores the relationship between digital trade and security, emphasizing the geopolitical implications for the global economy in the digital age. The rapid growth of digital trade has introduced significant challenges and opportunities, necessitating robust data governance to balance national security interests with the free flow of goods, services, and data across borders. The papers explore the different models of data governance championed by the US, China, and the EU, highlighting the complexities of cross-border data flows and their impact on international relations. Through detailed analyses of various international agreements and frameworks, this special issue provides a comprehensive overview of the current landscape of digital trade and its security implications, with a particular focus on China's evolving approach to data governance and its global influence.

Search engines match user queries with webpages. Search efficiency, increases when data-driven network effects attract more users and generate economies of scale and scope in data aggregation. Network effects enhance user welfare but also trigger antitrust concerns because they may "tip" the market towards a single dominant search engine – Google Search. The EU Digital Markets Act imposes an obligation on very large gatekeepers search engines to share their user data with smaller search engines to facilitate market entry and competition. The available empirical literature on search engine efficiency shows that asymmetric data sharing, from larger to smaller search engines, may increase competition but reduce user welfare because it fragments the user data pool that is crucial for efficiency, in particular for rare queries. Tension between competition and user welfare can be overcome by symmetric and mutual data sharing between all search engines, irrespective of market share. [ABSTRACT FROM AUTHOR]

The importance of data in current societal activities cannot be overstated, yet we know little about data governance and application. Using the Chinese Government Data Empowerment Initiative, this paper examines the process of data-driven business innovation. Using the staggered DiD model, we found that government data points effectively facilitate firms' product innovation, with higher-quality data correlating with better innovation performance. Government-authorized data points aid firms in accumulating experience in applying and managing data, thereby enhancing their production performance. Furthermore, these data help firms improve the quality of their product innovations and achieve iterative product upgrades. We also found that government-authorized data points not only generate product innovations for government use but also stimulate the output of commercial product innovations. This research provides important insights into data governance and enterprise data management decisions. [ABSTRACT FROM AUTHOR]

Copyright of Modern Educational Technology is the property of Editorial Board of Modern Educational Technology, Tsinghua University 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.)

Copyright of Modern Educational Technology is the property of Editorial Board of Modern Educational Technology, Tsinghua University 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.)

Copyright of Cyber Security & Data Governance is the property of Editorial Office of Information Technology & Network Security 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.)

This special issue explores the relationship between digital trade and security, emphasizing the geopolitical implications for the global economy in the digital age. The rapid growth of digital trade has introduced significant challenges and opportunities, necessitating robust data governance to balance national security interests with the free flow of goods, services, and data across borders. The papers explore the different models of data governance championed by the US, China, and the EU, highlighting the complexities of cross-border data flows and their impact on international relations. Through detailed analyses of various international agreements and frameworks, this special issue provides a comprehensive overview of the current landscape of digital trade and its security implications, with a particular focus on China's evolving approach to data governance and its global influence. [ABSTRACT FROM AUTHOR]

Historically, scholarly editions of music have identified a managing editor and individual volume editors. Other contributors might receive an acknowledgement with a vague description of activities, with institutional support often appearing in the printed metadata. This model’s emphasis on single scholars perpetuates a myth, relying heretofore upon predominantly invisible labour. Digital editions require interdisciplinary collaboration combining musicological skills, technical skills, and infrastructural resources, thus challenging this model’s ability to endure. Moreover, digital editions proffer opportunities for reconsidering the roles, workflows, and knowledge structures involved in critical musical scholarship.Situated at the intersection of currently running and recently completed digital projects, continuously emergent tools, sociology, and philosophy, this essay reflects on the role of the editor in the digital age. Beginning with Howard Becker’s art worlds model of creative communities, it suggests a model based on FAIR (Findable, Accessible, Interoperable, and Reusable) and CARE (Collective Benefit, Authority to Control, Responsibility, and Ethics) principles and the Music Encoding Initiative (MEI). MEI affords expansive metadata recording for contributors – composers, librettists, performers, editors, researchers, funders, etc. – to a work and its embodiments, which in turn enables broader visibility and empowers greater acknowledgement of the labour involved in such projects. Alongside other digital projects, this essay pays particular attention to how these technological and team-focused concepts are at- and in-play in the Reger-Werkausgabe Online. [ABSTRACT FROM AUTHOR]

Does the current dialogue on the development of statistical systems provide adequate scope for transforming official statistics to deliver their social role? Can statistical systems, as currently defined, provide opportunities to people and non-state institutions to influence "what" statistics and "how" should be produced and used? This paper provides a sociological framework to investigate these questions within a broader understanding of the social functions of official statistics as part of public statistics required for a democratic society. [ABSTRACT FROM AUTHOR]

The article builds on an up-to-date overview of the data governance framework developed by the People's Republic of China to gauge its influence over digital trade policy at the global stage. China has developed a peculiar data governance architecture, predicated upon domestic security. The party-state has been open to discuss only trade-related aspects of information flows while shielding national autonomy from international harmonization. This strategy is coupled with the effort to project China's data governance approach externally both through China's role in international standard setting and via new digital infrastructure, through the Digital Silk Road initiative. In this respect, China's attempts to facilitate penetration by its own companies in foreign digital infrastructure needs to be assessed against the large data access power enjoyed by the state-party vis-à-vis domestic big tech companies. The article argues that such a strategy appears in contrast with the explicit commitment to protect national self-determination, especially when host countries lack adequate legal and administrative safeguards to tackle the economic and data protection challenges brought by digitalization. [ABSTRACT FROM AUTHOR]

This paper conducts a comparative analysis of data governance mechanisms concerning the secondary use of health data in Taiwan and the European Union (EU). Both regions have adopted distinctive approaches and regulations for utilizing health data beyond primary care, encompassing areas such as medical research and healthcare system enhancement. Through an examination of these models, this study seeks to elucidate the strategies, frameworks, and legal structures employed by Taiwan and the EU to strike a delicate balance between the imperative of data-driven healthcare innovation and the safeguarding of individual privacy rights. This paper examines and compares several key aspects of the secondary use of health data in Taiwan and the EU. These aspects include data governance frameworks, legal and regulatory frameworks, data access and sharing mechanisms, and privacy and security considerations. This comparative exploration offers invaluable insights into the evolving global landscape of health data governance. It provides a deeper understanding of the strategies implemented by these regions to harness the potential of health data while upholding the ethical and legal considerations surrounding its secondary use. The findings aim to inform best practices for responsible and effective health data utilization, particularly in the context of medical AI applications. [ABSTRACT FROM AUTHOR]

As part of its European Data Strategy, the EU wants to create a market for non-personal data and unlock the sharing of industrial data between companies. This theory-building case study suggests that businesses' data governance preferences are determined by their position in the data value chain and thus whether firms monetise data as data holders (producers) in the upstream or as data reusers (access seekers) in the downstream segment. While the latter favour legal rules for governing data sharing, the former prefer only contractual agreements. The data value chain theory draws on concepts of the role of data in value creation in the digital economy and on case evidence from the automotive and financial sectors, which are typical cases of digital disruption. The theory helps to better understand coalition building in business lobbying activities, the potential dynamics between private self-regulation and public policies, and the probable feedback effects of digital policies on corporate power sources. [ABSTRACT FROM AUTHOR]

Copyright of Ethnologie Française is the property of Presses Universitaires de France 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.)

Overview: Data Collaboratives have gained traction as interorganizational partnerships centered on data exchange. They enhance the collective capacity of responding to contemporary societal challenges using data, while also providing participating organizations with innovation capabilities and reputational benefits. Unfortunately, data collaboratives often fail to advance beyond the pilot stage and are therefore limited in their capacity to deliver systemic change. The governance setting adopted by a data collaborative affects how it acts over the short and long term. We present a governance design model to develop context-dependent data collaboratives. Practitioners can use the proposed model and list of key reflective questions to evaluate the critical aspects of designing a governance model for their data collaboratives. Practitioners can develop a detailed understanding of the governance factors that influence the successful creation of data collaboratives. The governance design process model offers practitioners a tangible roadmap and concrete activities to help them revamp an existing data collaborative or create a new one. The list of reflective questions can guide practitioners' initial design efforts and help them assess the robustness of their data collaboratives' current governance models. [ABSTRACT FROM AUTHOR]

Advances in neuroscience and other disciplines are producing large-scale brain data consisting of datasets from multiple organisms, disciplines, and jurisdictions in different formats. However, due to the lack of an international data governance framework brain data is currently being produced under various contextual ethical and legal principles which may influence key stakeholders involved in the generation, collection, processing and sharing of brain data thereby raising ethical and legal challenges. In addition, despite the demand for a brain data governance framework that accounts for culture, there is a gap in empirical research and actions to understand how key stakeholders around the world view these issues using neuroscientists who are affected by these ethical and legal principles. Therefore, using the research question how do ethical and legal principles influence data governance in neuroscience? we attempt to understand the perceptions of key actors on the principles, issues and concerns that can arise from brain data research. We carried out interviews with 21 leading international neuroscientists. The analytical insights revealed key ethical and legal principles, areas of convergence, visibility, and the contextual issues and concerns that arise in brain data research around these principles. These issues and concerns circulate around intimately connected areas which include ethics, human rights, regulations, policies and guidelines, and participatory governance. Also, key contextual insights around animal research and ethics were identified. The research identifies key principles, issues, and concerns that need to be addressed in advancing the development of a framework for global brain data governance. By presenting contextual insights from neuroscientists across regions, the study contributes to informing discussions and shaping policies aimed at promoting responsible and ethical practices in brain data research. The research answers the call for a cross cultural study of global brain data governance and the results of the study will assist in understanding the issues and concerns that arise in brain data governance. [ABSTRACT FROM AUTHOR]

Copyright of China Terminology is the property of China Terminology Magazine 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.)

In the digital era, multimodal behavioral research has emerged as a pivotal discipline, integrating diverse data sources to comprehensively understand human behavior. This paper defines and distinguishes data governance from mere data management within this context, highlighting its centrality in assuring data quality, ethical handling, and participant protection. Through a meticulous review of the literature and empirical experience, we identify key implementation strategies and elucidate the benefits and risks of data governance frameworks in multimodal research. A demonstrative case study illustrates the practical applications and challenges, revealing enhanced data reliability and research integrity as tangible outcomes. Our findings underscore the critical need for robust data governance, pointing to future advancements in the field, including the development of adaptive governance frameworks, innovative big data analytics solutions, and user-friendly tools. These enhancements are poised to amplify the utility of multimodal data, propelling behavioral science forward. [ABSTRACT FROM AUTHOR]

Copyright of Cyber Security & Data Governance is the property of Editorial Office of Information Technology & Network Security 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.)

Copyright of Cyber Security & Data Governance is the property of Editorial Office of Information Technology & Network Security 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.)

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department 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.)

In today's competitive construction industry, companies are under increasing pressure to enhance efficiency and productivity. This research examines how digitalization can address issues such as market instability, low productivity, lack of investment in innovation, workforce issues, and management deficiencies. It explores the potential of technologies like Building Information Modeling (BIM) and Lean Construction (LC) to improve project management. The "House of COANFI" framework, integrating Lean principles with strategy, process, projects, and people, is proposed as a solution for enhancing project management, promoting organizational coherence, continuous improvement, and technological adoption. The methodology includes a literature survey, stakeholder workshops, developing an information system, and validation through case studies. Key findings highlight the benefits of COANFI implementation, including better data management, improved productivity, collaborative integration, and organizational learning. However, challenges such as resistance to change, data quality issues, and integration complexity must be addressed. The study concludes that digitalization, supported by frameworks like COANFI, can significantly enhance efficiency and competitiveness. Future research should validate these methodologies in real-world applications, explore strategies for managing organizational change, and investigate the impact of digital technologies on sustainability, helping the construction sector achieve long-term growth and sustainability. [ABSTRACT FROM AUTHOR]

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Maritime shipping is considered the backbone of the global supply chain. It is expected that future fully automated smart ports will be data-driven, harvesting power from advanced analytics and artificial intelligence. Under the hood, Internet of Things (IoT) technologies are considered as an enabling foundation of new data sources for innovations and high-level automation. Thus, enterprise data management becomes critical when implementing IoT projects. However, the implementation of data management needs to be governed. Although the IoT adoption in smart ports receives significant interest from the port authority, the effectiveness of the IoT adoption lacks proper evaluation and support of comprehensive data management policy and guidelines. By using the systematic literature review and a case study to analyze the current gaps in IoT data governance research, this research shows that IoT data governance still needs to catch up in smart ports. In particular, compared to other industries, data lifecycle management and data quality management are two key data governance areas that should receive increasing attention from the port authorities. It is believed that with adequate data governance, smart ports can truly harvest the power of IoT to improve operational efficiency and sustainability with an increasing level of automation. [ABSTRACT FROM AUTHOR]

Over six million people were hospitalized with COVID-19, leading to modifications in nursing care nationwide. The pandemic revolutionized healthcare, prioritizing technology-driven, innovative, and interdisciplinary care approaches to improve quality and safety and connect patients and families. Nursing leaders' knowledge of managing healthcare change provided stability and validated the nursing profession. • Modern technology has been increasingly integrated into health care. • Patient care advances associated with AI in healthcare can enhance clinical outcomes. • When introducing AI, consider data-related factors to ensure systems are effective, safe, and aligned with standards, compliance requirements, and the patient's needs. • AI data must be validated to avoid biased decision-making. • Nurses will require guidance to ensure AI's ethical, compassionate, and safe use. [ABSTRACT FROM AUTHOR]

In the era of big data, maximizing the circulation and sharing of data on the basis of ensuring its security is a challenge. Data security classification provides an effective solution for data security protection. This paper proposes a two-stage data classification method based on nine CIA (Confidentiality, Integrity, Availability) indicators, and introduces an expert scoring mechanism to achieve quantitative and precise data classification. In the empirical research part, this paper applies the new method to G Securities Company and first classifies the company's data into 76 categories. Then the second stage of grading is carried out based on the scores of each index of different categories. The results show that the proposed method is more accurate and robust in terms of data protection and in terms of increasing the value of data sharing and circulation. [ABSTRACT FROM AUTHOR]

A Secure Data Enclave is a system that allows data owners, such as governments and private firms, to control data access and ensure data security while facilitating approved uses of data by other parties. This model of data use offers additional protections and technical controls for the data owner compared to the more commonly used approach of transferring data from the owner to another party through a data sharing agreement. Under the data use model, the data owner retains full transparency and auditing over the other party's access, which can be difficult to achieve in practice with even the best legal instrument for data sharing. We describe the key technical requirements for a Secure Data Enclave, provide a reference architecture for its implementation on Amazon Web Services using managed cloud services, and describe four use cases of this architecture in partnerships with state governments to control access to sensitive administrative data. [ABSTRACT FROM AUTHOR]

This paper begins by discussing the need to actively manage data as a valuable enterprise asset. It explores what an enterprise data framework (EDF) looks like, before identifying the elements that are key to creating scalability. These are: 1) speaking the language of the executive and gaining longterm investment and continued support; and 2) designing-in scalability through understanding the landscape, hooking into existing practices, ensuring the right data ownership, roles and responsibilities, avoiding the fool's path by designing-in flexibility, and having strong triage processes. The paper then acknowledges that scalability is only possible through successful adoption of the framework. The paper finishes by examining who leads the implementation and what a successful chief data officer (CDO) looks like, how the creation of a movement is essential and how key culture is. It concludes that implementing an EDF into a live commercial environment is a matter of iterations and that moving through the maturity curve requires adaptability and resilience by not just the framework but also the CDO and the data function. Three guiding principles are identified: 1) relevance; 2) impact; and 3) being a good ancestor. [ABSTRACT FROM AUTHOR]

In this article, we examine the significance of establishing participatory and consentful recordkeeping practice in the face of ubiquitous use of records beyond their original intent. Among such secondary uses is the decontextualisation of data as part of the 'industrialisation' of access and use of 'historical' records within current transactional contexts, together with a wide range of data sharing practices arising from contemporary data science paradigms. To situate the call to action for consentful recordkeeping practice, we begin the article by exploring how human ability to navigate through the perpetual twilight of records becomes increasingly murky when a wholesale approach to data collection and governance is applied by machine learning practitioners. We then re-frame some classical archival principles to align them with participatory approaches; specifically, by expanding the scope of Jenkinsonian 'moral defence' as an imperative for proactive engagement with the Archival Multiverse. We then describe a case study of consentful recordkeeping in practice, using the example of the AiLECS Lab's newly developed collection acquisition and management system. This principles-based framework informs our practices for collecting and curating datasets for machine learning research and development and aims to privilege the ongoing consent of those represented in records to their use. In the context of this work, our core premise is that technologies designed to prevent exploitation of children should aim to avoid underlying data practices that are themselves exploitative (of children or adults). [ABSTRACT FROM AUTHOR]

Background Sources of public and private data and ways to link them continue to evolve. This offers new opportunities for research, and new reasons for data-holding organisations to form partnerships. While research using these data can be beneficial, there is also a potential for negative consequences for some individuals or groups, including unintended or unanticipated effects. It is important to consult the public on how we might achieve both opportunities to link different types of data for research purposes, and protections against the misuse of data and the possibility of negative consequences. Methods Combining data sources for research was the topic of four days of deliberation held in British Columbia, Canada in late 2019. Public deliberation events bring diverse groups of people together to give direct input to policy makers, through carefully structured in-depth discussion on issues that are controversial and/or a source of public concern. Participants discussed whether data from electronic medical records should be used for research purposes, whether it is acceptable to combine data from public and private sources, who should authorise its use in research, and how a public advisory group on data use might be structured. Results Over four days, 29 residents of BC developed 17 deliberative conclusions that can be grouped into four broad topic areas: balancing benefit and potential harms when linking data; the protections that are expected to govern use of data; the type of authorisation required; and how the public should be involved in an ongoing way. Overall, the public is very supportive of research as long as oversight and controls are in place, including ongoing input from members of the public. Conclusion Deliberative conclusions from this event provide essential public input on the use of linked data for research, in particular when those data come from multiple sources. This is important information as policy-makers continue to develop legislation and practices around the use and linkage of both public and private sources of data.