Public agencies routinely collect administrative data that, when shared and integrated, can form a rich picture of the health and well-being of the communities they serve. One major challenge is that these datasets are often siloed within individual agencies or programs and using them effectively presents legal, technical, and cultural obstacles. This article describes work led by the North Carolina Department of Health and Human Services (NCDHHS) with support from university-based researchers to establish enterprise-wide data governance and a legal framework for routine data sharing, toward the goal of increased capacity for integrated data analysis, improved policy and practice, and better health outcomes for North Carolinians. We relied on participatory action research (PAR) methods and Deliberative Dialogue to engage a diverse range of stakeholders in the co-creation of a data governance process and legal framework for routine data sharing in NCDHHS. Four key actions were taken as a result of the participatory research process: NCDHHS developed a data strategy road map, created a data sharing guidebook to operationalize legal and ethical review of requests, staffed the Data Office, and implemented a legal framework. In addition to describing how these ongoing streams of work support data use across a large state health and human services agency, we provide three use cases demonstrating the impact of this work. This research presents a successful, actionable, and replicable framework for developing and implementing processes to support intradepartmental data access, integration, and use.

Roughness of the surface underlying the atmospheric boundary layer causes departures of the near-surface scalar and momentum transport in comparison with aerodynamically smooth surfaces. Here, we investigate the effect of $56\times 56$ homogeneously distributed roughness elements on bulk properties of a turbulent Ekman flow. Direct numerical simulation in combination with an immersed boundary method is performed for fully resolved, three-dimensional roughness elements. The packing density is approximately $10\,\%$ and the roughness elements have a mean height in wall units of $10 \lesssim H^+ \lesssim 40$. According to their roughness Reynolds numbers, the cases are transitionally rough, although the roughest case is on the verge of being fully rough. We derive the friction of velocity and of the passive scalar through vertical integration of the respective balances. Thereby, we quantify the enhancement of turbulent activity with increasing roughness height and find a scaling for the friction Reynolds number that is verified up to $Re_\tau \approx 2700$. The higher level of turbulent activity results in a deeper logarithmic layer for the rough cases and an increase of the near-surface wind veer in spite of higher $Re_\tau$. We estimate the von Kármán constant for the horizontal velocity $\kappa _{m}=0.42$ (offset $A=5.44$) and for the passive scalar $\kappa _{h}=0.35$ (offset $\mathbb {A}=4.2$). We find an accurate collapse of the data under the rough-wall scaling in the logarithmic layer, which also yields a scaling for the roughness parameters $z$-nought for momentum ($z_{0{m}}$) and the passive scalar ($z_{0{h}}$).

In the era of the Industrial Revolution 4.0 (IR 4.0), the adequacy of training models for industrial needs is being challenged. Africa is a skills hub, threatened by unemployment among young people, especially graduates, competition, and the sustainability of industrial fabrics. By carrying out a systematic literature review, this article aims to highlight the aspects and outcomes of the educational revolution that must accompany IR 4.0. The results show that IR 4.0 offers new careers, and that training is a key barrier to the successful digital transformation of the industry. University 4.0 is the conversion needed to overcome this barrier. This article explains this new academic model generating skills, which refers to the ability to perform activities effectively with high technical, digital, and flexible management capacities. Faced with the low adoption of IR 4.0, and the lack of a systematic literature review, this article offers a significant platform for the research community, both academic and industrial.

Enabling private sector trust stands as a critical policy challenge for the success of the EU Data Governance Act and Data Act in promoting data sharing to address societal challenges. This paper attributes the widespread trust deficit to the unmanageable uncertainty that arises from businesses’ limited usage control to protect their interests in the face of unacceptable perceived risks. For example, a firm may hesitate to share its data with others in case it is leaked and falls into the hands of business competitors. To illustrate this impasse, competition, privacy, and reputational risks are introduced, respectively, in the context of three suboptimal approaches to data sharing: data marketplaces, data collaboratives, and data philanthropy. The paper proceeds by analyzing seven trust-enabling mechanisms comprised of technological, legal, and organizational elements to balance trust, risk, and control and assessing their capacity to operate in a fair, equitable, and transparent manner. Finally, the paper examines the regulatory context in the EU and the advantages and limitations of voluntary and mandatory data sharing, concluding that an approach that effectively balances the two should be pursued.

The momentum surrounding the use of data for the public good has grown over the past few years, resulting in several initiatives, and rising interest from public bodies, intergovernmental organizations, and private organizations. The potential benefits of data collaboratives (DCs) have been proved in several contexts, including health, migration, pandemics, and public transport. However, these cross-sectoral partnerships have frequently not progressed beyond the pilot level, a condition hindering their ability to generate long-term societal benefits and scale their impact. Governance models play an important role in ensuring DCs’ stability over time; however, existing models do not address this issue. Our research investigates DCs’ governance settings to determine governance dimensions’ design settings enhancing DCs’ long-term stability. The research identifies through the literature on collaborative governance and DCs seven key governance dimensions for the long-term stability of DCs. Then, through the analysis of 16 heterogeneous case studies, it outlines the optimal design configurations for each dimension. Findings make a significant contribution to academic discourse by shedding light on the governance aspects that bolster the long-term stability of DCs. Additionally, this research offers practical insights and evidence-based guidelines for practitioners, aiding in the creation and maintenance of enduring DCs.

A number of data governance policies have recently been introduced or revised by the Indian Government with the stated goal of unlocking the developmental and economic potential of data. The policies seek to implement standardized frameworks for public data management and establish platforms for data exchange. However, India has a longstanding history of record-keeping and information transparency practices, which are crucial in the context of data management. These connections have not been explicitly addressed in recent policies like the Draft National Data Governance Framework, 2022. To understand if record management has a role to play in modern public data governance, we analyze the key new data governance framework and the associated Indian Urban Data Exchange platform as a case study. The study examines the exchange where public records serve as a potential source of data. It evaluates the coverage and the actors involved in the creation of this data to understand the impact of records management on government departments’ ability to publish datasets. We conclude that while India recognizes the importance of data as a public good, it needs to integrate digital records management practices more effectively into its policies to ensure accurate, up-to-date, and accessible data for public benefit.

This article analyzes the assemblages of humans and other-than-humans that animated the sacred landscape of Cerro de la Virgen, a hilltop site occupied during the Formative period (1800 BC–AD 250) in the lower Río Verde Valley of coastal Oaxaca, Mexico. Commensalism in the region increased markedly in scope and complexity throughout the Formative period, culminating in the region's first polity at AD 100. Feasting practices became relatively standardized, but the placement of objects and bodies in public architecture—a set of collective practices associated with the vital forces that animated the cosmos—varied considerably from site to site during the late Terminal Formative period (150 BC–AD 250). Lower Verde scholars have argued that these idiosyncrasies reflect the myriad collective identities of the region's hinterland communities, a pattern rooted in local affiliations that may have conflicted with an expanding regional identity centered at the urban center of Río Viejo. I augment this discussion by highlighting the role that the materiality of the landscape, present before humans even occupied the region, played in the construction of collective identity. I develop an interpretive approach that pays special attention to Indigenous concepts of ontology, particularly those related to animacy and its transference, and uses the semiosis of American philosopher Charles Peirce to elucidate meaning from deposits of cached objects. The animate qualities assembled through fired clay and chiseled stone at Cerro de la Virgen afforded a ritual pattern that was unique in coastal Oaxaca at the end of the Formative period.

This article proposes five ideas that the design of data governance policies for the trustworthy use of artificial intelligence (AI) in Africa should consider. The first is for African states to assess their domestic strategic priorities, strengths, and weaknesses. The second is a human-centric approach to data governance, which involves data processing practices that protect the security of personal data and the privacy of data subjects; ensure that personal data are processed in a fair, lawful, and accountable manner; minimize the harmful effect of personal data misuse or abuse on data subjects and other victims; and promote a beneficial, trusted use of personal data. The third is for the data policy to be in alignment with supranational rights-respecting AI standards like the African Charter on Human and Peoples Rights, the AU Convention on Cybersecurity, and Personal Data Protection. The fourth is for states to be critical about the extent to which AI systems can be relied on in certain public sectors or departments. The fifth and final proposition is for the need to prioritize the use of representative and interoperable data and ensure a transparent procurement process for AI systems from abroad where no local options exist.

This paper focuses on people's attitudes towards democracy and authoritarian regimes in Myanmar and whether the extent to which they prefer democracy is moderated by the severity of the coronavirus disease-2019 (COVID-19) pandemic. If people view the authoritarian regime's capacity to take swift action favourably, their opposition to it may be lower. We explored this hypothesis by conducting a survey of 756 individuals in Myanmar in June 2022 that incorporated a vignette experiment. A hypothetical scenario of Myanmar society in 2023 was presented with a two-by-two design – the conditions of the government (election is restored or not) and the pandemic situation (good or bad) were randomly varied, and the respondents were asked to report their favourability of the hypothetical scenario. The results reveal: (1) regardless of the pandemic condition, respondents prefer democracy to authoritarian regimes by a wide margin; and (2) the extent to which democracy is preferred is lower when the COVID-19 condition is more severe. Similar results were obtained from supplementary analyses using a conjoint experiment.

When admitting patients to hospital and treating them, psychiatrists and other health professionals may need to deprive them of their liberty. Where this occurs, professionals will need to work within a statutory framework to practice legally and protect their patients’ right to liberty under Article 5 of the European Convention on Human Rights. Within England and Wales, some clinical scenarios will require a choice to be made between the Mental Health Act 1983 (MHA) and the Mental Capacity Act 2005 (MCA) and its Deprivation of Liberty Safeguards (DoLS). This choice can be complex, is often overlooked and frequently misunderstood in clinical practice. Deciding between the two frameworks must be done on a case-specific basis. With the use of code of practice guidelines, case law and an unfolding clinical scenario we aim in this article to support clinicians in taking a clear-sighted approach to the dilemma and the factors to consider when deciding between the two regimes.

In 2022, the world experienced the deadliest year of armed conflict since the 1994 Rwandan genocide. Much of the intensity and frequency of recent conflicts has drawn more attention to failures in forecasting—that is, a failure to anticipate conflicts. Such capabilities have the potential to greatly reduce the time, motivation, and opportunities peacemakers have to intervene through mediation or peacekeeping operations. In recent years, the growth in the volume of open-source data coupled with the wide-scale advancements in machine learning suggests that it may be possible for computational methods to help the international community forecast intrastate conflict more accurately, and in doing so reduce the rise of conflict. In this commentary, we argue for the promise of conflict forecasting under several technical and policy conditions. From a technical perspective, the success of this work depends on improvements in the quality of conflict-related data and an increased focus on model interpretability. In terms of policy implementation, we suggest that this technology should be used primarily to aid policy analysis heuristically and help identify unexpected conflicts.

How do leaders select their top-level foreign policy appointees? Through a formal model of the domestic and intragovernmental politics surrounding an international crisis, I investigate the trade-offs shaping leaders’ appointment strategies. In the model, a leader selects a foreign policy appointee, anticipating how the appointment will affect the advice he receives in the crisis, the electorate's evaluation of his performance, and ultimately the policies that he and his foreign counterparts pursue as a consequence. The analysis uncovers a fundamental tension in the leader's ability to use appointments to advance his core political and policy objectives of deterring foreign aggression, obtaining accurate advice, and maximizing domestic approval: any appointment that advances one of these objectives invariably comes at the cost of another, and the leader's appointment strategy must balance across these trade-offs. Analyzing cross-national appointment patterns to the offices of ministers of defense and foreign affairs, I find descriptive evidence consistent with the model's predictions: leaders from dovish parties are more than twice as likely as leaders from hawkish parties to select cross-partisan and politically independent appointees, and such appointments are less likely for leaders of either party as they approach re-election.

In the burgeoning landscape of African smart cities, education stands as a cornerstone for sustainable development and unlocking future potential. Accurate student performance prediction holds immense social importance, enabling early intervention, improved learning outcomes, and equitable access to quality education, aligning with sustainable development goals. Traditional models often falter in Africa due to imbalanced datasets and irrelevant features. This research leverages machine learning in Nigerian classrooms to predict underperforming students. Techniques like synthetic minority oversampling, edited nearest neighbors, and the Boruta algorithm for feature selection, alongside genetic algorithms for efficiency, enhance model performance. The ensemble models achieve AUCs of 90–99.7%, effectively separating low-performing and high-performing students. Implemented via Streamlit and Heroku, these models support real-time, data-driven decisions, enhancing early intervention, personalized learning, and informing policy and public service design. This ensures equitable education and a brighter future across Africa. By leveraging ML, this research empowers universities to support struggling students, optimize educational costs, and promote inclusive development, fostering data-driven decision-making and resource allocation optimization. Ultimately, this research paves the way for a future where data empowers education within African smart cities, unlocking the full potential of data-driven solutions and ensuring equitable educational opportunities across the continent.

Studies on court administration in India have so far focused their attention largely on caseload management and judge strength of the higher judiciary. In-depth investigations of the performance of India’s lower courts, the primary loci of a citizen’s contact with the judiciary, are rarer, largely due to the lack of available data at scale. We conduct a quantitative analysis of a large dataset of more than 1700 Indian district courts between 2010 and 2018, to assess court performance through the measure of timeliness of case disposal. We use median days to decision—the median number of days it takes for a district court in India to decide a case. We aim to understand the impact of well-established factors—working strength and tenure of judges, case administration, age distribution of cases, and category or case type—against district courts’ performance. We find that court type and nature of cases are important predictors of a district court’s performance, and that the total number of judge working days and average bench strength are not good indicators of courts’ performance—the workload per judge being actually lower in low-performance district courts, compared to high-performing courts. Our study also reveals the strengths and weaknesses of the available judicial data platforms and points toward reforms in judicial administration to address these concerns.