Government Data Research Articles

Interrogating artist-teacher identity transformation in adult community learning

ABSTRACT This research analyses how the experience of artist-teachers in ACL differs from that of artist-teachers in other educational sectors, and the impact of this difference on artist-teachers in ACL. This responds to the gap in the published literature on the role of artist-teachers working in FE generally and ACL more specifically. There is significant published research on artist-teachers in secondary and higher education within the UK and North America, where they are defined, and their role and experience delineated. However, the artist-teacher in Adult Community Learning (ACL), in the UK, has been overlooked. There is no data on the number or demographic of artist-teachers in this sector, with no acknowledgement of government data or published research. Indeed, there is no comprehensive list of ACL centres in the UK or extant data on how many ACL centres are operating and offering adult art courses.

Considerations for trustworthy cross-border interoperability of digital identity systems in developing countries

AbstractIn developing nations, the implementation of Foundational Identity Systems (FIDS) has optimised service delivery and inclusive economic growth. Cross-border e-government will gain traction as developing countries increasingly look to identity federation and trustworthy interoperability through FIDS for the identification and authentication of identity holders. Despite this potential, the interoperability of FIDS in the African identity ecosystem has not been well-studied. Among the difficulties in this situation are the intricate internal political dynamics that have led to weak institutions, suggesting that FIDS could be used for political purposes; additionally, citizens’ or identity holders’ habitual low trust in the government raises concerns about data security and privacy protection. Similarly, vendor lock-in, cross-system compatibility, and ambiguous legislative rules for data exchange are other concerns. Interoperability is fundamentally necessary as a precondition for e-government services and serves as the foundation for the best possible service delivery in the areas of social security, education, and finance, as well as gender equality as demonstrated by the European Union (EU). Moreover, the integration of cross-border FIDS and an ecosystem of effective data governance will be created by unified data sharing via an interoperable identity system. Thus, in this study, we point to the challenges, opportunities, and requirements for cross-border interoperability in an African setting. Furthermore, we investigated current interoperability solutions such as the EU’s eIDAS and Estonian X-Road and proposed an approach for scoping requirements to achieve a fully functional interoperable identity ecosystem in the African setting. Our findings show that interoperability in the African identity ecosystem is essential for expanding the scope of e-government throughout the continent and for bolstering the smooth authentication and verification of identity holders for inclusive economic growth.

Explainable Graph Neural Networks: An Application to Open Statistics Knowledge Graphs for Estimating House Prices

In the rapidly evolving field of real estate economics, the prediction of house prices continues to be a complex challenge, intricately tied to a multitude of socio-economic factors. Traditional predictive models often overlook spatial interdependencies that significantly influence housing prices. The objective of this study is to leverage Graph Neural Networks (GNNs) on open statistics knowledge graphs to model these spatial dependencies and predict house prices across Scotland’s 2011 data zones. The methodology involves retrieving integrated statistical indicators from the official Scottish Open Government Data portal and applying three representative GNN algorithms: ChebNet, GCN, and GraphSAGE. These GNNs are compared against traditional models, including the tabular-based XGBoost and a simple Multi-Layer Perceptron (MLP), demonstrating superior prediction accuracy. Innovative contributions of this study include the use of GNNs to model spatial dependencies in real estate economics and the application of local and global explainability techniques to enhance transparency and trust in the predictions. The global feature importance is determined by a logistic regression surrogate model while the local, region-level understanding of the GNN predictions is achieved through the use of GNNExplainer. Explainability results are compared with those from a previous work that applied the XGBoost machine learning algorithm and the SHapley Additive exPlanations (SHAP) explainability framework on the same dataset. Interestingly, both the global surrogate model and the SHAP approach underscored the comparative illness factor, a health indicator, and the ratio of detached dwellings as the most crucial features in the global explainability. In the case of local explanations, while both methods showed similar results, the GNN approach provided a richer, more comprehensive understanding of the predictions for two specific data zones.

An advanced Artificial Intelligence platform for a personalised treatment of Eating Disorders.

Eating Disorders (EDs) affect individuals globally and are associated with significant physical and mental health challenges. However, access to adequate treatment is often hindered by societal stigma, limited awareness, and resource constraints. The project aims to utilize the power of Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), to improve EDs diagnosis and treatment. The Master Data Plan (MDP) will collect and analyze data from diverse sources, utilize AI algorithms for risk factor identificat io n, treatment planning, and relapse prediction, and provide a patient-facing chatbot for information and support. This platform will integrate patient data, support healthcare professionals, and empower patients, thereby enhancing care accessibility, personalizing treatment plans, and optimizing care pathways. Robust data governance measures will ensure ethical and secure data management. Anticipated outcomes include enhanced care accessibility and efficiency, personalized treatment plans leading to improved patient outcomes, reduced waiting lists, heightened patient engagement, and increased awareness of EDs with improved resource allocation. This project signifies a pivotal shift towards data-driven, patient-centered ED care in Italy. By integrat ing AI and promoting collaboration, it seeks to redefine mental healthcare standards and foster better well- being among individuals with EDs.

Assessing the integration of refugee health data into national health information systems in Jordan, Lebanon, and Uganda

BackgroundWith the increasing number of protracted refugee crises globally, it is essential to ensure strong national health information systems (HIS) in displacement settings that include refugee-sensitive data and disaggregation by refugee status. This multi-country study aims to assess the degree of integration of refugee health data into national HIS in Jordan, Lebanon, and Uganda and identify the strengths and weaknesses of their national HIS in terms of collecting and reporting on refugee-related health indicators.MethodsThe study employs a comparative country analysis approach using a three-phase framework. The first phase involved reviewing 4120 indicators compiled from global health organizations, followed by a multi-stage refinement process, resulting in 45 indicators distributed across five themes. The second phase consisted of selecting relevant criteria from the literature, including data sources, annual reporting, disaggregation by refugee status, refugee population adjustments, accuracy, and consistency. The third phase involved assessing data availability and quality of the selected indicators against these criteria.ResultsOur analysis uncovered significant challenges in assessing the health status of refugees in Jordan, Lebanon, and Uganda, primarily stemming from limitations in the available health data and indicators. Specifically, we identified significant issues including incomplete local data collection with reliance on international data sources, fragmented data collection from various entities leading to discrepancies, and a lack of distinction between refugees and host populations in most indicators. These limitations hinder accurate comparisons and analyses. In light of these findings, a set of actionable recommendations was proposed to guide policymakers in the three countries to improve the integration of refugee health data into their national HIS ultimately enhancing refugees’ well-being and access to healthcare services.ConclusionThe current status of refugee-related health data in Jordan, Lebanon, and Uganda indicates the need for improved data collection and reporting practices, disaggregation by refugee status and better integration of refugee health data into national HIS to capture the health status and needs of refugees in host countries. Key improvement strategies include establishing a centralized authority for consistent and efficient data management, fostering transparent and inclusive data governance, and strengthening workforce capacity to manage refugee health data effectively.

Phased post-disaster recovery challenges: 2016–2017 floods and landslides in Colombo and Kalutara, Sri Lanka

This paper aims to understand the challenges arising in individual post-disaster recovery (PDR) phases and how they contribute to overall recovery progress. It further investigates the transition of recovery from one phase to the subsequent phases of PDR. The 2016 and 2017 floods and landslides in Sri Lanka were used as the case study focusing on the two affected districts, Colombo and Kalutara. An in-depth study was conducted to explore the PDR phases from response to long-term reconstruction and development following the disaster events.A mixed methods approach was adopted where, under a qualitative approach, twenty-two semi-structured interviews with disaster management stakeholders were conducted. Moreover, on quantitative analysis, a survey questionnaire was conducted. The case study field data was supplemented with an extensive document review.The study found several factors in different PDR phases hindering long-term recovery. They include: a lack of communication and coordination mechanisms among key stakeholders in the response phase, and limited capacity and resources of local authorities in the rehabilitation phase. The challenges in recovery and long-term reconstruction and development phases were to disaster governance, collection and management of data and information, and low level of inter-agency collaboration. Some factors were recurring and continually hindered the PDR process and had a larger impact in Kalutara than in the Colombo district. Regarding the transition of recovery, there was only one transition from response and rehabilitation to the recovery phase occurred. No evidence was found for the second recovery transition from recovery to the long-term reconstruction and development phase.These findings will assist in planning future post-disaster recoveries in the region to ensure issues are anticipated, and proper planning is done ahead of time. The goal is to tackle the issues impeding long-term recovery in each PDR phase to allow a smooth and successful transition into the long-term phase.The findings of this research make an original contribution to the limited scholarship on the implications of individual PDR phases on long-term recovery.

“Open data means business”: Infrastructural and economic implications of opening up data in smart London

Around 2009–2010, opening up public data was a governmental strategy in the UK, as part of the implementation of the Transparency Agenda, based on the assumption that unfettered access to government data would inexorably lead to transparency, accountability and participation. Following this conjecture, and in an attempt to create an alternative to the corporate-driven smart city discourse, the Greater London Authority prioritised open data in its initial smart city plans to facilitate a ‘citizen-centred’ smart city. However, subsumed within the digital economy, open data was eventually promoted and implemented primarily for its lucrative potential to create new businesses, rather than prioritising the aspirations of transparency and participation. In this article, I explore the implications of this shift and the nature of open data- driven smart city making by focusing on a transport app, Citymapper that is built on open data released by Transport for London (TfL). By closely studying the app's product development process to generate profit together with its struggle to raise funding through venture capital and crowdfunding, I arrive at two main arguments. First, I argue that open data driven smart city making in London was a form of experimentation instead of a formal and rigid planning as it saw open data as an end in itself without much understanding of what this process would lead to in practice. As such, the case of Citymapper shows that the contingent process of opening up public transport data meant opening up public infrastructure in the city. Second, by examining the app's unsustainable business models and detailing its struggle to maintain revenue and profitability, I outline how open data-driven products are ultimately subsumed within the logic of platform capitalism rather than creating an alternative digital economy.

Veterinary Services'use of the Global Burden of Animal Diseases to prioritise interventions, monitor impact and develop critical competencies.

Attracting and sustaining investment in Veterinary Services and animal health programmes from national government budgets, development aid and grants, and philanthropic donors requires economic rationale using relevant, reliable and validated analytical approaches. The complex interwoven relationships between animal health, livestock husbandry systems, national food security, global health security and environmental sustainability emphasise the importance of improving data governance and stewardship and applying economic analysis to understand animal disease burdens. These efforts should enable prioritised investment of limited resources and effective monitoring of the impact of programmes over time. Data governance and stewardship capacities are fundamental to development, implementation and performance monitoring of evidence-based policies in animal health. There are challenges in data availability for national and subnational livestock populations in different sectors, for disease incidence and prevalence, and for animal health expenditure in support of optimised allocation of scarce resources, be they finance, land, labour, or management attention and policy focus. Animal health data systems governance and stewardship and economic analysis are core skills for Veterinary Services in developing and applying evidence-based policy, but capability probably varies among World Organisation for Animal Health (WOAH) Members. The WOAH Performance of Veterinary Services programme has several critical competencies that are relevant to economics of animal health and to data governance and stewardship, but these have not yet been targeted for coordinated capacity development. Implementation of publicâ€"private partnership approaches for animal health programmes creates increasing expectations of robust data and methods for prioritisation, options analysis, and assessing impacts and costs. Experience and examples from national systems in New Zealand, Australia, Ethiopia and Indonesia illustrate current challenges associated with prioritisation of animal health programmes using economic analysis. The Global Burden of Animal Diseases programme intends to support WOAH Members and partners to develop capacities for and standardise approaches to economic analysis and prioritisation in animal health programmes.

Government Data and Agricultural Productivity:Evidence from the National Agriculture Imagery Program

Government Data and Agricultural Productivity:Evidence from the National Agriculture Imagery Program

Effect of COVID-19 lockdowns on quality-of-life and health services access by socio-economic status in Australia.

This study examined changes in physical and mental health quality-of-life and health services access before and after the onset of the COVID-19 pandemic among individuals of lower and higher socio-economic status (SES) in Australia. Difference-in-differences and logistic regression models were undertaken using data from the Household, Income and Labour Dynamics in Australia (HILDA) survey and government data on COVID-19 lockdowns between January 2020 and February 2021. Individuals from higher SES reported larger decreases in mental health quality-of-life scores than those from lower SES after the onset of the pandemic. Those from lower SES reported less disruption with any health services (24.2% vs 30.4%; OR = 0.68; p < 0.001), specifically dental services (8.2% vs 15.4%; OR = 0.51; p < 0.001) and allied health services (5.9% vs 8.5%; OR = 0.60; p < 0.001), compared with those from higher SES. Additional days under lockdown were associated with reduced access to all health services (OR = 1.19). Furthermore, long-term health conditions (higher SES: OR = 1.54) and scores indicative of poorer physical (lower SES: OR = 1.17; higher SES: OR = 1.07) and mental health (lower SES: OR = 1.16; higher SES: OR = 1.12) were associated with increased health services disruption. While individuals from higher SES were more likely than those from lower SES to experience greater relative declines in mental health and increased disruption with health services access, individuals with a greater apparent need for health services, regardless of SES, may have faced inequalities in accessing these services during the COVID-19 pandemic.

Tracing the Path: Data Lineage and Its Impact on Data Governance

Tracing the Path: Data Lineage and Its Impact on Data Governance

Digital age: The path choice of government-citizen value co-creation

This study explores the relationship between government and citizen in the digital age and proposes a specific path to realize the value co-creation of government and citizen in the digital age. Quantitative analysis of 40 policy texts on “digital government construction” of 30 provincial and municipal governments in China before November 2022 was conducted using NVivo12. The case of Minsheng Cloud revealed that provincial and municipal government departments have not paid sufficient attention to citizen participation. Furthermore, citizens' participation ability and enthusiasm are not high. The study found that in the process of strengthening the construction of digital government, “government optimisation”, “citizen participation”, “governance guarantee”, “data governance” and “government digitization” are the key elements. We discuss the feasibility, necessity and coupling of the value co-creation theory to establish the benign interaction between government and citizens and realize co-creation. We find that the “DART” model can be co-created based on value, and that the continuous Improving the four aspects of “dialogue”, “access”, “transparency” and “risk assessment” will undoubtedly help realize the value co-creation of government and citizens in the digital era, and form a governance pattern of co-construction and sharing. It is therefore of great significance to promote the government's digital construction and digital governance capability to accelerate the construction of digital China and promote the modernisation of the national governance system and governance capability.

Socio-technical factors influencing big data analytics adoption in healthcare

The purpose of this study is to determine the key socio-technical factors influencing big data analytics adoption in healthcare services. A systematic literature review was conducted using peer-reviewed scholarly publications spanning from 2013 to 2023 to illuminate the influencing factors. Twelve papers focused on the factors influencing big data analytics (BDA) adoption in healthcare services were included for review. The factors were divided into four major groups namely i) person, ii) technology, iii) organization, and iv) environment. Analytical skills define a person, whereas technology is characterized by system quality and information quality. Organization support, organization resources, training, data governance, and evidence-based decision-making are all associated with the organization. Finally, government regulations are allocated to the environment. This review presents evidence of the socio-technical factors that influence big data analytics adoption in healthcare services. The findings from this review recommend future big data analytics adoption in healthcare services to carefully evaluate the factors identified in this study.

"I Just Assumed This Was Already Being Done": Canadian Patient Preferences for Enhanced Data Sharing for Precision Oncology.

In Canada, health data are siloed, slowing bioinnovation and evidence generation for personalized cancer care. Secured data-sharing platforms (SDSPs) can enable data analysis across silos through rapid concatenation across trial and real-world settings and timely researcher access. To motivate patient participation and trust in research, it is critical to ensure that SDSP design and oversight align with patients' values and address their concerns. We sought to qualitatively characterize patient preferences for the design of a pan-Canadian SDSP. Between January 2022 and July 2023, we conducted pan-Canadian virtual focus groups with individuals who had a personal history of cancer. Following each focus group, participants were invited to provide feedback on early-phase analysis results via a member-checking survey. Three trained qualitative researchers analyzed data using thematic analysis. Twenty-eight individuals participated across five focus groups. Four focus groups were conducted in English and one in French. Thematic analysis generated two major and five minor themes. Analytic themes spanned personal and population implications of data sharing and willingness to manage perceived risks. Participants were supportive of increasing access to health data for precision oncology research, while voicing concerns about unintended data use, reidentification, and inequitable access to costly therapeutics. To mitigate perceived risks, participants highlighted the value of data access oversight and governance and informational transparency. Strategies for secured data sharing should anticipate and mitigate the risks that patients perceive. Participants supported enhancing timely research capability while ensuring safeguards to protect patient autonomy and privacy. Our study informs the development of data-governance and data-sharing frameworks that integrate real-world and trial data, informed by evidence from direct patient input.

Network relations among open government data stakeholders: a structural social capital and ERGM analysis

Network relations among open government data stakeholders: a structural social capital and ERGM analysis

Institutional Dimensions in Open Government Data: A Deep Dive Into Indonesia’s Satu Data Initiative and Its Implications for Developing Countries

Institutional factors such as data standardization, data interoperability, and the ability to include other stakeholders (e.g., civil society) in ecosystems are now understood as crucial considerations in Open Government Data (OGD) implementation. However, most of the current understanding of institutional factors linked to OGD growth has evolved around highly digitalized and developed countries. This study aims to address this gap by investigating open data implementation in Indonesia, a developing country that has been an early advocate of OGD in the "Asia Pacific". In 2014, Indonesia implemented Satu Data Indonesia (One Data Indonesia) as a national initiative to promote OGD objectives. We employ institutional dimensions as the central theoretical lens to unpack the successes and impediments to Satu Data Indonesia’s development. This research dataset is built on 16 expert interviews from government (n = 14) and private (n = 2) organizations that directly contribute to the growth of Satu Data Indonesia, qualitatively triangulated with documentary analysis of key policy and regulatory documents. These new data provide important insights into the complexity and challenges of nationwide OGD implementation and data-sharing. Our findings show that despite its being an early advocate, Indonesia’s OGD initiative is still in the early stages of development, impeded by several policy and administrative bottlenecks.

Turning universities into data-driven organisations: seven dimensions of change

AbstractUniversities are striving to become data-driven organisations, benefitting from data collection, analysis, and various data products, such as business intelligence, learning analytics, personalised recommendations, behavioural nudging, and automation. However, datafication of universities is not an easy process. We empirically explore the struggles and challenges of UK universities in making digital and personal data useful and valuable. We structure our analysis along seven dimensions: the aspirational dimension explores university datafication aims and the challenges of achieving them; the technological dimension explores struggles with digital infrastructure supporting datafication and data quality; the legal dimension includes data privacy, security, vendor management, and new legal complexities that datafication brings; the commercial dimension tackles proprietary data products developed using university data and relations between universities and EdTech companies; the organisational dimension discusses data governance and institutional management relevant to datafication; the ideological dimension explores ideas about data value and the paradoxes that emerge between these ideas and university practices; and the existential dimension considers how datafication changes the core functioning of universities as social institutions.

Research status and application of artificial intelligence large models in the oil and gas industry

This article elucidates the concept of large model technology, summarizes the research status of large model technology both domestically and internationally, provides an overview of the application status of large models in vertical industries, outlines the challenges and issues confronted in applying large models in the oil and gas sector, and offers prospects for the application of large models in the oil and gas industry. The existing large models can be briefly divided into three categories: large language models, visual large models, and multimodal large models. The application of large models in the oil and gas industry is still in its infancy. Based on open-source large language models, some oil and gas enterprises have released large language model products using methods like fine-tuning and retrieval augmented generation. Scholars have attempted to develop scenario-specific models for oil and gas operations by using visual/multimodal foundation models. A few researchers have constructed pre-trained foundation models for seismic data processing and interpretation, as well as core analysis. The application of large models in the oil and gas industry faces challenges such as current data quantity and quality being difficult to support the training of large models, high research and development costs, and poor algorithm autonomy and control. The application of large models should be guided by the needs of oil and gas business, taking the application of large models as an opportunity to improve data lifecycle management, enhance data governance capabilities, promote the construction of computing power, strengthen the construction of “artificial intelligence + energy” composite teams, and boost the autonomy and control of large model technology.

Transition from rehabilitation hospital to the National Disability Insurance Scheme (NDIS) for people with brain injury and spinal cord injury: a data linkage protocol

IntroductionTraumatic brain injury (TBI) and spinal cord injury (SCI) are both major contributors to permanent disability globally, with an estimated 27 million new cases of TBI and 0.93 million new...

Historical Performance Gap, Readiness of Government, and Open Data Quality: Transnational Empirical Analysis Based on Performance Feedback Theory

Open government data has become an important movement for government administrations in numerous countries. Previous studies have paid less attention to the complex relationship among the historical performance gap, the readiness of government (including policy and action dimensions), and open data quality. Therefore, on the basis of performance feedback theory, a theoretical model has been developed and tested to analyze the impact of the historical performance gap on the readiness of government and open data quality. The model is empirically tested by the partial least squares method using sample data from 115 countries. The results show that the historical performance gap could indirectly contribute to a country's open data quality by promoting the readiness of government. In addition, the impact of this gap on the readiness of government and open data quality shows a U-shaped curve. In other words, a greater change in the effect of open data utilization in a country in the previous year entails a more significant promotion of readiness of government and open data quality in the next year. These findings drive governments to pay more attention to performance evaluation in open data and improve the readiness of government to improve their respective country's open data quality.