Resolving the value-for-risk dilemma by data (Governance) laws and other mechanisms

AGNER: Agile governance-oriented unified named entity recognition for continual learning with diffusion adaptation.

Government data accessibility and firm dynamics: Encouraging entrepreneurship or accelerating exit?

Public adoption of open government data: A game theoretical approach

Bridging divides with data: Open government data and ESG rating divergence

Enhancing data governance through transparency: An empirical study of the data trust model

Anonymising personal data under the data legislative acquis established by the Data Governance Act

The globalization of supply chains and the rise of digital technologies have made cross-border data flows central to international trade law. At the same time, growing perceptions of data as a national security concern – commonly referred to as ‘data securitization’ – have led states to adopt restrictive measures, including cross-border data flow regulations and local storage or processing requirements. This article examines how international trade law can and should address such measures and the balance it strikes between facilitating data flows and safeguarding national security. It first surveys national approaches to restricting cross-border transfers, highlighting both divergences and common patterns in the regulation of personal and non-personal data. It then analyses exceptions in preferential trade agreements (PTAs), focusing on Essential Security Interests (ESI) and Legitimate Public Policy Objective (LPPO) exceptions. The analysis shows that while these exceptions take different forms, each faces certain limitations, reflecting an inherent trade-off between respecting states’ discretion to safeguard security interests and ensuring predictability in trade relations. To address these limitations, the article proposes avenues for international cooperation: enhancing regulatory transparency, ensuring legitimacy and effectiveness of government access to privately held data, and developing shared understandings of national security concerns and data governance measures. By combining legal safeguards with cooperative frameworks, international trade law can better reconcile the tension between safeguarding security and enabling cross-border data flows.

To systematically map Irish national health datasets and policy frameworks relevant to children and young people (0-24years) and appraise their readiness for quality improvement, equity monitoring, and interoperable reuse. A Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) and the Joanna Briggs Institute (JBI)-guided scoping review will synthesise peer-reviewed and grey literature. Datasets will be benchmarked using World Health Organization Data Quality Review (DQR) domains, Findable, Accessible, Interoperable, Reusable (FAIR) principles, European Medicines Agency registry guidance and PROGRESS-Plus equity stratifiers. Outputs will be synthesised into structured matrices (national catalogue, key quality indicators and availability-variability layer) and an equity heat map. The review will characterise heterogeneity in coverage, coding, governance, equity stratification and linkage-readiness across perinatal, hospital, registry, surveillance and community datasets, identifying priority gaps for standardisation. Within the context of healthcare quality assurance, the synthesis will enable evidence-informed benchmarking across clinical domains, from perinatal outcomes to chronic disease management. The integration of DQR and FAIR appraisals will allow Irish health agencies to identify datasets that meet international standards of reliability, completeness and accessibility. Simultaneously, mapping PROGRESS-Plus variables will reveal where data gaps perpetuate inequities, informing targeted data-collection reforms. The resultant framework will provide a replicable model for how nations can align data governance with the continuous quality-improvement cycle central to the International Journal of Pharmaceutical Quality Assurance's mission - linking structure (data quality), process (data use) and outcomes (policy and patient benefit). This review will generate a decision-ready catalogue of Irish paediatric and young people's health datasets, highlighting strengths, gaps and opportunities for improvement. By appraising data quality, equity stratifiers and linkage readiness, it will provide actionable recommendations for standardisation and governance. Policymakers can use the outputs to align datasets with international best practice, clinicians can advocate for inclusion of outcome and patient-reported measures and researchers can identify priority areas for secondary analysis and linkage studies - supporting safer, fairer, and more effective child health services in Ireland. Strengthening child health data systems has direct societal benefits by enabling more equitable, transparent and evidence-based policy. By mapping available datasets and assessing equity stratifiers, this review will highlight gaps in capturing determinants such as ethnicity, deprivation and disability. Addressing these gaps will allow more accurate monitoring of health inequalities and ensure that vulnerable groups are not overlooked in service planning. The outputs will support a culture of accountability, inform public debate on data use and contribute to building a learning health system that promotes fairness, inclusivity and trust in healthcare for children and young people. This protocol delivers the first integrated, decision-ready framework to benchmark paediatric data ecosystems against international quality, equity and stewardship standards, enabling learning health systems and policy-relevant data governance in Ireland and comparable Organisation for Economic Co-operation and Development settings.

ABSTRACT Alternative data governance proposals have emerged across policy, civil society, and academic spheres in response to the harms of data extractivism. Yet, many of them either rely on individual decision-making or presume that existing public institutions possess the capacity for deep democratic ordering. Such approaches remain analytically and normatively oblivious to the ongoing crisis of democracy under financialised capitalism and the countervailing dynamics necessary for meaningful political and technological transformation in this context. This article argues for situating alternative data governance proposals within a dynamic account of democracy's crisis. Drawing on Nancy Fraser's neo-Marxian framework, we examine how this crisis is expressed, deepened, and reproduced by data extractivism and assess the capacity of various proposals to countervail these dynamics. On this basis, we argue for governance alternatives that embed countervailing forces to address democracy's crisis dynamics by (1) disrupting the mutually reinforcing relationship between data extractivism and the erosion of public authority, and (2) expanding and reorienting the political sphere. In doing so, we invite a broader inquiry into how democracy's renewal and alternative data governance approaches might reinforce one another.

Across the built asset lifecycle, information governance and exchange processes are typically administered by humans. Bandwidth has been gained through standardisation of processes and tooling, notably by way of ISO 19650 and the Common Data Environment (CDE), but data volumes continue to increase. This constrains the sector’s ability to capitalise on information-intensive innovations such as robotics and agential artificial intelligence (AI). This paper presents a neuro-symbolic framework for AI augmentation of the CDE, capable of semi-autonomous information governance, retrieval and exchange. The framework employs AI-driven knowledge mining of built environment datasets to transform them into navigable, well-organised knowledge bases. Building on this foundation, the framework introduces ontology-based data access and model context protocol integration, enabling AI agents to navigate multi-platform technology ecosystems with human-like contextual reasoning. The framework offers immediate practical benefits, reducing information management costs and addressing skills shortages, while unlocking transformative capabilities for the future. By enabling the system to process complex, multi-faceted queries autonomously and return contextually appropriate responses to both human and machine requestors, the framework removes manual information processing constraints. The result is a vendor-agnostic, security-preserving framework that enhances existing data governance while dramatically expanding the sector's capacity for information-intensive innovation and inter-organisational collaboration.

Artificial Intelligence (AI) is increasingly shaping agribusiness transformation in Nepal by improving productivity, sustainability, and market integration within a largely smallholder-based agricultural economy. This article examines the current status, applications, opportunities, and constraints of AI adoption in Nepal’s agribusiness sector using a comprehensive secondary desk review of academic literature, policy documents, and development reports. Key AI applications include precision farming, satellite-based crop classification, predictive analytics, and digital advisory services across crop, livestock, and supply chain management. Initiatives such as GeoKrishi, Connect Kisan AI, and the Omdena-UNWFP collaboration illustrate AI’s potential to enhance resource efficiency and climate resilience. However, adoption remains limited due to infrastructure gaps, high costs for smallholders, data scarcity and localization challenges, low digital literacy, and evolving policy and regulatory frameworks. Drawing on insights from other emerging markets, the study adapts the extended Technology Acceptance Model-Technology-Organization-Environment (TAM-TOE) framework to contextualize AI adoption in Nepal. The article concludes that inclusive and scalable AI-driven agribusiness transformation will require coordinated stakeholder engagement, targeted investments in digital infrastructure, skills development, and robust data governance mechanisms.

To analyse the landscape of active US National Institutes of Health (NIH) artificial intelligence (AI) health research grants, with emphasis on studies conducted in low- and middle-income countries (LMICs), to characterise use cases, health challenges addressed and gaps relevant to the ethical and responsible application of AI-enabled health science. Descriptive portfolio analysis of NIH-funded AI health research grants. NIH research portfolio analysis, with a focus on global health studies in LMICs. None. Data are derived from active NIH-funded grants involving AI applications in health research, as of 31 January 2025. Not applicable (portfolio analysis). Primary measures included the proportion and funding of AI health research grants focused on LMICs and their thematic use cases. Secondary measures compared LMIC-focused and high-income country (HIC)-focused grants by research focus and health area and identified gaps relevant to ethical and responsible AI use in global health. Of 1850 active NIH AI health research grants, 97 (5.2%) focused on LMICs, representing US$40.2 million (2.4%) of the total US$1.66 billion portfolio. compared with HICs, LMIC-based studies emphasised diagnostics and treatment (72.2% vs 66.8%), health system optimisation (18.6% vs 15.6%), disease surveillance and outbreak response (14.4% vs 8.8%), and telemedicine and remote care (7.2% vs 4.4%). HIC-based grants more frequently addressed public health education (10.4% vs 8.2%) and ethics and data governance (12.8% vs 7.2%). All settings emphasised data science training and capacity strengthening, as well as basic research and early-stage AI-augmented tools. LMIC-based studies most often targeted non-communicable diseases (39%), communicable diseases (30%) and health system strengthening (24%). 31 awards were made directly to LMIC-based principal investigators (1.7% of the portfolio), most commonly in South Africa, Kenya and Uganda. NIH investment in peer-reviewed AI-enabled health research is expanding globally. LMIC-focused studies prioritise areas aligned with pressing global health needs, including outbreak detection, disease surveillance, diagnostics and treatment, health system optimisation and remote care. Greater attention to ethics, data governance and public health communication, alongside support for digital infrastructure and meaningful collaboration, may help strengthen the relevance and sustainability of AI-enabled research for population health.

Purpose: This study examines when and how green entrepreneurial orientation (GEO) influences business model innovation (BMI) in start-ups, focusing on boundary-spanning search (BSS) as a conversion mechanism and big data capability (BDC) as a boundary condition. Design/methodology/approach: Grounded in resource-based theory and organisational search theory, the research employs an empirical approach using survey data collected from 307 start-ups. The study examines the mediating effect of BSS and the moderating role of BDC through quantitative analysis. Findings/results: The analysis reveals three key findings: (1) GEO has a positive impact on BMI. (2) Boundary-spanning search mediates the relationship between GEO and BMI. (3) Big data capability positively moderates the link between BSS and BMI. Practical implications: For start-ups, the results imply that ‘going green’ is more likely to lead to BMI when firms design a focused external-search portfolio and build minimum viable data capabilities (e.g. data governance, cross-functional information sharing and decision-linked analytics) to reduce information overload and accelerate experimentation. Originality/value: The study advances an orientation–conversion perspective by explaining heterogeneous BMI outcomes amongst green-oriented ventures and highlighting the contingent value of BSS. The findings are particularly informative for start-ups in emerging-market contexts (including South Africa and many African economies), where resource constraints and uneven digital infrastructure can make the conversion of sustainability intent into a scalable business model change highly contingent.

AI tech gets thoroughly rooted in company functions, and when it starts blending into human resource stuff, big changes happen to organizations, but it's mostly on the micro effects of singular AI tools. It doesn't look at how many org conditions together would affect change. This study bridges this gap by probing how configurations, made up of embedded technology depth, the cross-disciplinary area of human resources, organizational support structure, and data governance development, bring about those good organizational results. Using fuzzy-set qualitative comparative analysis on 6 different companies from different sectors, the research found that there are 3 equifinal pathways to substantial transformation: strategically led deep transformation, business-collaborative agile evolution, or data-driven progressive improvement. Organizational support became a necessary foundation condition. Strong cross-function collaboration and strong data governance can make up for a slightly shallower technology embedding. And provides configurational theories to the knowledge of AI-HRM literature and helps the managers make changes in organizations with AI.

Digital health systems are increasingly central to health system performance, yet many low- and middle-income countries continue to struggle with integrating proliferating platforms into coherent national infrastructures. These challenges are often attributed to technical or capacity constraints, but less attention has been paid to how digital health integration is governed in practice. Ghana is frequently cited as an early adopter of digital health in sub-Saharan Africa, but persistent fragmentation raises questions about how national digital health integration is executed. We conducted a scoping review in accordance with PRISMA-ScR guidelines to examine governance arrangements, regulatory frameworks, and interoperability provisions that shape the integration of Ghana's national digital health systems. Four bibliographic databases and two search engines were searched for peer-reviewed and grey literature, with no restriction on publication date. Eligible sources included empirical studies, policy documents, strategies, technical reports, and programme evaluations addressing digital health governance, regulation, data governance, or interoperability in Ghana. Twenty-two sources met the inclusion criteria. Authority for digital health governance is distributed across multiple health and non-health institutions, with no single body exercising comprehensive stewardship or enforcement. Legal and policy instruments articulate principles for coordination, interoperability, data protection, and cybersecurity, but interoperability is consistently framed as a strategic objective rather than a mandatory condition for system deployment or continuation. In practice, governance and interoperability arrangements operate unevenly, characterized by parallel platforms, partial data flows, manual workarounds, and variable compliance with security safeguards. Persistent gaps are associated with fragmented mandates, weak enforcement mechanisms, donor-driven implementations, and constrained subnational capacity. Ghana's digital health integration challenges reflect a governance execution gap rather than an absence of strategies or technologies. The findings highlight the need to shift from strategy-led coordination toward enforceable, rule-based governance, with broader relevance for countries seeking sustainable digital health integration.

The segmented cultivation program that integrates higher vocational education with undergraduate education is an important institutional arrangement for improving the modern vocational education system and cultivating high-level technical and skilled talents. Its sustainable development relies on a scientific, systematic, and closed-loop quality assurance and evaluation feedback mechanism. This paper examines the entire integrated education process, first identifying the primary quality challenges in five dimensions: cultivating objectives, curriculum system, faculty development, evaluation standard, and school-enterprise collaboration. Subsequently, drawing upon stakeholder coordination theory and total quality management, it constructs a dual-drive model comprising a collaborative quality assurance system and an assessment feedback improvement system. Finally, it elaborates on operational mechanisms across five dimensions: standard setting, process management, multidimensional evaluation, data governance, and continuous improvement, and policy recommendations are proposed. The findings offer valuable reference for educational administrative authorities, institutions, and industry enterprises in refining the integrated education system.

Regional decision-making in urban planning, environmental management, and related fields requires the reliable and extensible integration of diverse geographic data and models. Traditional expert-driven approaches rely mainly on the manual configuration of data and models by human users, which is inefficient and suffers from knowledge gaps. Agents based on large language models (LLMs) offer the potential to integrate resources and automate computational processes, but face challenges in handling heterogeneous geospatial data and models and often produce unreliable outputs. We propose the regional decision-making agent (RDMA), which is a multiagent collaborative framework that enhances LLM-based agents with structured resource management and reliability mechanisms to support complex, multistep decision-making processes. The RDMA comprises three modules: data governance, model governance, and agent scheduling. To mitigate LLM hallucinations, three strategies are implemented: knowledge-enhanced retrieval-augmented generation (RAG), confidence self-assessment, and system transparency. Performance evaluations demonstrate high success rates and accuracy across diverse tasks at both the model configuration and workflow composition levels. Ablation studies confirm the essential contributions of quality control and knowledge-enhanced retrieval mechanisms. Case studies in urban planning and stormwater management validate RDMA’s ability to deliver actionable insights. This work advances AI-driven geographic analysis and provides a scalable solution for sustainable regional governance.

The integration of Fourth Industrial Revolution (4IR) technologies into South African local government, as proposed by Ndamase et al. (2025), presents not only operational and technical challenges but also profound legal and regulatory dilemmas. While the potential for enhanced service delivery, citizen engagement, and transparency is significant, the current legal ecosystem governing municipal functions is ill-equipped to address issues of data sovereignty, algorithmic governance, cybersecurity liability, and digital inclusion. This conceptual study employs a doctrinal legal research methodology to identify the critical legal gaps impeding the responsible adoption of 4IR technologies in South African municipalities. Through a systematic review of international best practices from jurisdictions like the European Union, Estonia, and Singapore, and an analysis of South Africa's existing constitutional and legislative framework, this paper proposes a structured legal framework. This framework is built upon four foundational pillars: (1) Data Governance and Citizen Data Sovereignty, (2) Algorithmic Accountability and Transparency, (3) Mandatory Digital Inclusion and Accessibility, and (4) Adaptive Regulation through Regulatory Sandboxes. The study argues that without proactive and sophisticated legal instrumentation, the deployment of 4IR technologies risks exacerbating existing inequalities, infringing on fundamental rights, and creating new forms of unaccountable automated governance. The proposed framework aims to provide legislators and municipal officials with a roadmap for developing laws that not only enable innovation but also safeguard democracy, equity, and the rule of law in the digitalizing local state.

High-quality datasets are increasingly recognized as foundational inputs to economic development, industrial upgrading, and public governance. A rigorous evaluation system for data asset quality is therefore needed to improve data governance and to enable value realization in circulation. Focusing on three representative circulation scenarios—data interaction, data exchange, and data trading—this study develops an indicator system from technical, business, and benefit-oriented dimensions. The Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is used to identify causal relationships among indicators and key drivers. To integrate multi-expert judgments under uncertainty, hesitant linguistic variables and evidence theory are adopted, and the Best–Worst Method (BWM) is applied to derive more consistent indicator weights. The resulting weights are combined with the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) to obtain a comprehensive ranking of data asset quality with scenario-adjustable emphasis. A traffic-flow dataset from a data technology enterprise is used to demonstrate applicability and effectiveness. The proposed framework advances scenario-adaptive data quality evaluation and supports enterprise data governance, data transaction pricing, and the implementation of high-quality dataset policies.