Government Data Research Articles

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.

Navigating GDPR Compliance: The Intersection of Data Governance, Accountability, and Organizational Culture

This Abstract highlights the GDPR's important role in shaping data protection practices within the EU and its broader implications for data governance. The GDPR harmonizes data protection laws across EU, establishing comprehensive guidelines for the collection, processing, storage, and transfer of personal data. The GDPR directs significant penalties for non-compliance, thereby reinforcing the importance of adhering to data protection standards. Additionally the GDPR also validates the principles of lawfulness, fairness, transparency, and accountability in the processing of personal data.

Cognitive machine learning techniques for predictive maintenance in industrial systems: A data-driven analysis

This paper delves into the intricate relationship between machine learning (ML) and data analysis, spotlighting the recent advancements, prevailing challenges, and emerging opportunities that underscore their integration. By conducting an extensive review of scholarly literature and real-world case studies, this article uncovers the synergistic potential of ML and data analysis, emphasizing their combined influence across diverse industries and domains. The exploration is framed around pivotal themes including algorithmic innovations, which are at the heart of ML's ability to transform vast and complex datasets into actionable insights. Moreover, the discussion extends to predictive modeling techniques, a cornerstone of data analysis that leverages historical data to forecast future trends, behaviors, and outcomes. Practical applications are scrutinized to demonstrate how the confluence of ML and data analysis is pioneering solutions in fields as varied as healthcare, where predictive analytics can save lives, to finance, where it is used to navigate market uncertainties. This paper also addresses the barriers to effective integration, such as data privacy concerns and the need for robust data governance frameworks. Through this comprehensive examination, the article sheds light on the rapidly evolving landscape of ML-driven data analysis, offering insights into how these technological advancements are reshaping research methodologies, industry practices, and societal norms.

Integrating agile methodologies into data governance: Achieving flexibility and control simultaneously

This review paper explores integrating agile methodologies into data governance to achieve flexibility and control. Agile methodologies, known for their iterative development, collaboration, and responsiveness to change, can enhance the adaptability of data governance frameworks. This paper analyzes the alignment of agile principles with data governance, discusses strategies for integration, such as adaptive data governance and incremental implementation, and examines how to balance flexibility with the necessary controls for effective data governance. Key practices, challenges, and potential solutions focus on fostering a collaborative culture and leveraging appropriate tools and technologies. The paper also suggests future research directions, emphasizing the role of emerging technologies and evolving regulatory requirements. Ultimately, this integration enhances decision-making, operational efficiency, and organizational competitive advantage.

Ensuring compliance with regulatory and legal requirements through robust data governance structures

This review paper examines the critical role of robust data governance in ensuring compliance with regulatory and legal requirements in today's data-driven environment. Through an exploration of key concepts such as data governance definition, components, alignment with regulatory requirements, and essential elements of a robust framework, the paper highlights the significance of effective data governance practices in mitigating risks, maintaining data integrity, and facilitating regulatory compliance. Practical implications for organizations seeking to enhance their data governance structures and future research directions in the field are discussed.

Strategies for engaging stakeholders in data governance: Building effective communication and collaboration

This paper explores strategies for engaging stakeholders, enhancing communication, and fostering collaboration in data governance. It examines the importance of stakeholder engagement and communication channels, such as meetings, digital platforms, and reports, in facilitating collaboration. Lessons learned from successful data governance initiatives underscore the significance of aligning efforts with business objectives and securing executive sponsorship. Practical recommendations include defining clear roles, tailoring communication strategies, and promoting stakeholder participation. Key success factors include leadership support and alignment with organizational culture, while potential pitfalls include resistance to change and inadequate resources. Future research directions include exploring innovative technologies for stakeholder engagement and evaluating the effectiveness of data governance initiatives.

Design and Development of Cybersecurity Suite

Protecting personal, corporate and government data in the digital age requires cybersecurity. Traditional cybersecurity methods sometimes lack comprehensive and dynamic protection mechanisms as cyber-attacks change and become more sophisticated. A new cybersecurity suite is proposed in this study. It integrates various advanced security technologies into a single user-friendly platform interaction. This paper specifically combines techniques for real-time phishing detection, strong password management, safe encryption and decryption services and an interactive module to promote user awareness and behaviours to improve digital security for individuals and organization. Rapid prototyping is used in iterative and incremental development phases to adapt to changing security needs and user input. This method allows the platform to be constantly improved to satisfy the current cybersecurity standards and react to new threats. The prototyped developed provides proactive digital defence, a complete cybersecurity platform that equips users to defend their digital presence. With the intuitive design and advanced feature set, the prototype aspires to democratize cybersecurity. Empirical testing of the prototype revealed that makes an effective protection of a wider audience without the complexities of security software. This paper contributes in the advances cybersecurity technology and shaping the digital protection measures.

The Effect of ESG Data of Companies on Financial Performance: A Panel Data Analysis on The BIST Sustainability Index

The relationship between companies' environmental, social, and governance (ESG) data and their financial performance is of significant interest. The primary aim of this study was to investigate whether the ESG scores of companies in the BIST Sustainability Index impact their financial performance. By analysing the relationship between ESG scores and financial performance indicators of 26 companies, whose ESG scores were calculated by S&P Global for the period 2018-2022, using the panel data analysis method, we found compelling results. ESG factors were found to have a significant and positive effect on return on assets (ROA), return on equity (ROE), net profit margin (NPM), and asset growth (AGR) but a negative effect on the market-to-book ratio (MBR).

Secure environmental, social, and governance (ESG) data management for construction projects using blockchain

Environmental, social, and governance (ESG) considerations are increasingly becoming imperative and obligatory across various industries. The ESG performance within the architecture, engineering, and construction (AEC) industry is under heightened market scrutiny. However, current ESG management in construction is still in its infancy due to two limitations: (1) a deficiency in ESG knowledge, such as indicators pertinent to construction activities, and (2) a lack of data security in ESG management, culminating in inefficient and unreliable environmental management practices. Therefore, this paper employs the Design Science Research Method (DSRM) to introduce a Blockchain-ESG Integrated (BESGI) framework, facilitating traceable ESG data management within construction projects. This framework presents three significant contributions. First, it identifies ten AEC-ESG indicators by analyzing ESG methods. Second, it proposes a mapping approach for AEC-ESG indicators to construction projects for key ESG information access and data source identification. Third, it develops a blockchain-based data management mechanism for traceable ESG data management in the BESGI framework. It validates and evaluates the framework in a construction project in Hong Kong. The results show that the framework is usable and can save labor costs by 20.15 % compared to traditional ESG management. This study offers a secure data management solution for ESG analysis of construction projects.

Transit Board Diversity and Pandemic Service Cuts in Vulnerable Communities

Despite its primary role in shaping policy and service characteristics, board governance is an understudied topic in the field of urban transit. Existing research on board management and representative bureaucracy theory suggests that the race and gender diversity of boards has a significant impact on organizational activity but that these relationships are highly dependent on the cultural context and industry analyzed. In this paper, we evaluate how the diversity of transit boards (with respect to race, gender, and disability) in the U.S.A. correlates with service changes authorized by these boards during the COVID-19 pandemic. Utilizing a database on board governance and general transit feed specification data for 36 agencies, we find a positive relationship between the presence of women on transit boards and vertically equitable service cuts, defined as increasing or maintaining transit service in more vulnerable neighborhoods. Overall, transit agencies with more female board members had more equitable service cuts, on average, during the COVID-19 pandemic.

Management strategies for the potential and value of mangrove forest ecosystem services in the Panango area of south Bolaang Mongondow regency

The Panango area is an area that has mangrove forests in its coastal areas that are used for ecotourism, fisheries, and environmental conservation. Mangrove forests in the area have the potential and value of important benefits to the surrounding community which has been using them for a long time, but data and information are not yet available, so it is necessary to identify and assess the ecosystem services in the area. The purpose of this study was to determine the potential and value of the benefits of mangrove ecosystem services in the Panango area in terms of provisioning, regulating, cultural, and supporting functions, and to determine strategies for managing the mangrove forest in the area. This research uses primary data obtained through field observations, namely observation of research objects, completion of questionnaires, and interviews with respondents. Secondary data was obtained from local government data and other data sources relevant to the research. The research data analysis technique was carried out by calculating the total economic value of each mangrove ecosystem service and SWOT analysis to determine mangrove area management strategies. This study concluded that the economic value of the potential and benefits of mangrove forest ecosystem services in the Panango area in total Rp. 26,259,380,092/year consisting of the value of service providers of Rp. 684,300,000/year, the potential for regulatory services as a wave breaker with a benefit value of Rp. 9,705,300,000/10 years, the potential value as a cultural service of Rp. 15,846,000,000/year, and the potential value as a supporting service of Rp. 23,780,092/year. Strategies that can be used in sustainable management of the potential and value of the benefits of mangrove forest ecosystem services in Panango area are through conservation efforts to protect and manage the area by involving joint participation between local governments and communities and other stakeholders.

Predictive analytics for financial compliance: Machine learning concepts for fraudulent transaction identification

Predictive analytics has emerged as a pivotal tool in financial compliance, offering sophisticated methods for identifying fraudulent transactions through the application of machine learning (ML) concepts. As financial institutions grapple with increasingly complex fraud schemes and stringent regulatory requirements, the integration of predictive analytics with ML provides a proactive approach to fraud detection and prevention. Machine learning algorithms excel in analyzing vast datasets, identifying hidden patterns, and making real-time predictions. In the realm of financial compliance, supervised learning models such as logistic regression, decision trees, and random forests are commonly used to classify transactions as legitimate or fraudulent. These models are trained on historical transaction data, learning to recognize the subtle indicators of fraud by identifying correlations between various features and fraudulent outcomes. This allows for high-accuracy predictions on new, unseen data. Unsupervised learning techniques, such as clustering and anomaly detection, are equally critical in predictive analytics for financial compliance. These methods do not require labeled data and are adept at uncovering novel fraud patterns by detecting outliers and irregularities that deviate from normal transactional behavior. Anomaly detection algorithms, including k-means clustering and isolation forests, can identify transactions that exhibit unusual characteristics, flagging them for further investigation. The integration of predictive analytics with real-time data processing capabilities enhances the agility of fraud detection systems. Streaming analytics and real-time scoring enable the continuous monitoring of transactions, ensuring that suspicious activities are identified and addressed promptly. This real-time aspect is crucial for minimizing the impact of fraudulent transactions and ensuring compliance with regulatory standards. Despite the advancements, implementing predictive analytics for financial compliance involves challenges such as ensuring data quality, addressing privacy concerns, and maintaining model transparency. Financial institutions must navigate these challenges by employing robust data governance practices, leveraging secure data processing techniques, and adopting explainable AI models that provide insights into their decision-making processes. In conclusion, predictive analytics, powered by machine learning concepts, offers a robust framework for identifying fraudulent transactions and enhancing financial compliance. By leveraging advanced ML algorithms and real-time data processing, financial institutions can proactively detect and prevent fraud, thereby safeguarding their operations and ensuring adherence to regulatory mandates. This approach not only mitigates financial losses but also strengthens the overall integrity and trustworthiness of the financial system.

Transformative Business Outcomes of AI-Driven Master Data Management (MDM) and Data Governance Programs

Master Data management (MDM) has transitioned from a deferred task to a critical business imperative in the modern competitive landscape. Companies must swiftly identify and leverage their most valuable master data across the enterprise, at scale and in near real-time, to maintain a competitive edge. Master data serves as the foundational core that drives systems and business processes, powering the entire organization. Integrating AI into MDM accelerates value realization and goal attainment, enhances business outcomes, and maximizes return on investment (ROI). A meticulously implemented AI-driven MDM system, coupled with a Data Governance program, is essential for unlocking this potential. This paper highlights the pivotal role of AI-driven MDM and Data Governance programs in promoting operational excellence and sustainable growth through a comprehensive analysis of their strategic importance and diverse benefits and use cases.

‘This is NOT human services’: Counter-mapping automated decision-making in social services in Australia

This paper offers a counter-map of automation in social services decision-making in Australia. It aims to amplify alternative discourses that are often obscured by power inequalities and disadvantage. Redden (2005) has used counter-mapping to frame an analysis of big data in government in Canada, contrasting with ‘dominant outward facing government discourses about big data applications’ to focus on how data practices are both socially shaped and shaping. This paper reports on a counter-mapping project undertaken in Australia using a mixed methods approach incorporating document analysis, interviews and web scraping to amplify divergent discourses about automated decision-making. It demonstrates that when the focus of analysis moves beyond dominant discourses of neoliberal efficiency, cost cutting, accuracy and industriousness, alternative discourses of service users’ experiences of automated decision-making as oppressive, harmful, punitive and inhuman(e) can be located.

Roles and Responsibilities of the Global Specialist Digital Health Workforce: Analysis of Global Census Data.

The Global Specialist Digital Health Workforce Census is the largest workforce survey of the specialist roles that support the development, use, management, and governance of health data, health information, health knowledge, and health technology. This paper aims to present an analysis of the roles and functions reported by respondents in the 2023 census. The 2023 census was deployed using Qualtrics and was open from July 1 to August 13, 2023. A broad definition was provided to guide respondents about who is in the specialist digital health workforce. Anyone who self-identifies as being part of this workforce could undertake the survey. The data was analyzed using descriptive statistical analysis and thematic analysis of the functions respondents reported in their roles. A total of 1103 respondents completed the census, with data reported about their demographic information and their roles. The majority of respondents lived in Australia (n=870, 78.9%) or New Zealand (n=130, 11.8%), with most (n=620, 56.3%) aged 35-54 years and identifying as female (n=720, 65.3%). The top four occupational specialties were health informatics (n=179, 20.2%), health information management (n=175, 19.8%), health information technology (n=128, 14.4%), and health librarianship (n=104, 11.7%). Nearly all (n=797, 90%) participants identified as a manager or professional. Less than half (430/1019, 42.2%) had a formal qualification in a specialist digital health area, and only one-quarter (244/938, 26%) held a credential in a digital health area. While two-thirds (502/763, 65.7%) reported undertaking professional development in the last year, most were self-directed activities, such as seeking information or consuming online content. Work undertaken by specialist digital health workers could be classified as either leadership, functional, occupational, or technological. Future specialist digital health workforce capability frameworks should include the aspects of leadership, function, occupation, and technology. This largely unqualified workforce is undertaking little formal professional development to upskill them to continue to support the safe delivery and management of health and care through the use of digital data and technology.

Proposing a 6R framework promoting circular strategies for platform organizations

Purpose This study aims to investigate the connection between the circular economy and sustainable operations management to identify the challenges and opportunities in platform organizations. The study looks at how the stated circular economy strategies (Reduce, Reuse, Repair, Refurbish, Repurpose and Recycle) are integrated across different industries, emphasizing how they align with the e-business model. The research evaluates their contribution to achieving Sustainable Development Goal 12, which focuses on responsible consumption and production. Design/methodology/approach A literature review has analyzed CE frameworks, business models and the role of sustainable operations management practices. This study utilized secondary data analysis of platform organizations and insights from case studies to identify patterns, strategies and outcomes. The study also involved practical examinations within organizations, specifically focusing on innovative start-ups. Findings The analysis uses the 6R framework (Reduce, Reuse, Repair, Refurbish, Repurpose and Recycle) to uncover valuable insights into organizational practices and highlight the role of platform organizations in promoting and achieving circular economy objectives. The research findings focus on the central importance of data regulation and governance while showcasing sustainable business practices through platform organizations. Originality/value This research is significant as it connects circular economy with platform organizations’ business models, emphasizing data regulation, resource efficiency, waste reduction and aligning business practices with Sustainable Development Goal 12.

Frameworks for effective data governance: best practices, challenges, and implementation strategies across industries

Frameworks for effective data governance: best practices, challenges, and implementation strategies across industries

Artificial intelligence in chronic kidney diseases: methodology and potential applications.

Chronic kidney disease (CKD) represents a significant global health challenge, characterized by kidney damage and decreased function. Its prevalence has steadily increased, necessitating a comprehensive understanding of its epidemiology, risk factors, and management strategies. While traditional prognostic markers such as estimated glomerular filtration rate (eGFR) and albuminuria provide valuable insights, they may not fully capture the complexity of CKD progression and associated cardiovascular (CV) risks.This paper reviews the current state of renal and CV risk prediction in CKD, highlighting the limitations of traditional models and the potential for integrating artificial intelligence (AI) techniques. AI, particularly machine learning (ML) and deep learning (DL), offers a promising avenue for enhancing risk prediction by analyzing vast and diverse patient data, including genetic markers, biomarkers, and imaging. By identifying intricate patterns and relationships within datasets, AI algorithms can generate more comprehensive risk profiles, enabling personalized and nuanced risk assessments.Despite its potential, the integration of AI into clinical practice faces challenges such as the opacity of some algorithms and concerns regarding data quality, privacy, and bias. Efforts towards explainable AI (XAI) and rigorous data governance are essential to ensure transparency, interpretability, and trustworthiness in AI-driven predictions.