Data Services Research Articles

Humanistic Therapy for Young People: Client-Perceived Helpful Aspects, Hindering Aspects, and Processes of Change

Abstract This qualitative study aimed to establish aspects of humanistic therapy that young people (13–16 years old) perceived as helpful and hindering, and to test a novel method for identifying perceived processes of change. A “medium q” thematic analysis was conducted followed by a coding-based “process of change analysis.” Participants were 50 young people in London schools who experienced moderate or severe emotional symptoms and had participated in up to 10 sessions of a school-based humanistic intervention. Participants were predominantly female and ethnically heterogeneous. Therapist qualities most often perceived as helpful were affiliative in nature. Unhelpful therapist activities were silences and a lack of input. Young people described feeling free to talk and open up. Helpful outcomes included feeling unburdened, gaining insight, and improving relationships. “Getting things off their chest,” “Advice and guidance,” “Modeling relationships,” and “insights to behavior change” were identified as specific processes of change in over 50% of young people. Approximately one-third felt hindered by a lack of therapist input, silences, or not feeling able to open up or trust. These findings indicate the potential value of an active, “process guiding” stance in humanistic therapy. Our process of change analysis has potential for identifying perceived change mechanisms in therapy. This work was supported by the Economic and Social Research Council [grant reference ES/M011933/1]. Anonymized qualitative interview transcripts are available on request to the First Author/Chief Investigator. Quantitative, participant-level data for the ETHOS study (with data dictionary), and related documents (e.g., parental consent form), are available via the ReShare UK Data Service (reshare.ukdataservice.ac.uk/853764/). Access requires ReShare registration.

Victim Verification with the Use of Deep Metric Learning in DVI System Supported by Mobile Application

This paper presents the design of a system to support the identification of victims of disasters and terrorist attacks. The system, called ID Victim (IDV), is a web application using a mobile app and data server. The DVI (Disaster Victim Identification) procedure, an international standard developed by Interpol, is used. The purpose of the IDV system is to facilitate and expedite the process of determining victims’ identities. A neural identification module was developed and trained on approximately 13,000 images from the LFW dataset and fine-tuned using 400 simulated PostMortem (PM) and AnteMortem (AM) images. Postmortem data include photographs of victims while antemortem data consist of pre-disaster photos of potential victims. The module generates a hypothesis, linking PM to AM, which is then verified. The module achieved test identification accuracy of up to 60% for 25 sample PM and AM sets. The system partially automates photo comparisons by DVI teams, improving efficiency, reducing identification time, and limiting the exposure of operators to graphic images. Implementing the system as a mobile application accelerates the process by enabling direct data entry during victim examinations on-site.

Risk Management of Information Security in Inaportnet Using ISO/IEC 27005:2018

This study aims to analyse information security risks in the Inaportnet system at the Port Authority Class II Tanjung Buton using the ISO/IEC 27005:2018 standard. The system is a digital innovation designed to expedite port services but faces significant challenges in information security. The first step involved identifying assets within the Inaportnet system, followed by recognizing potential threats and vulnerabilities associated with these assets. This process is crucial as it lays the groundwork for understanding where risks may arise. The research employs the Failure Mode and Effects Analysis (FMEA) method to identify, assess, and prioritise risks based on assets, threats, vulnerabilities, and existing controls. A total of 17 risks were identified, categorized from "very low" to "low" priority levels. The highest risk involves operational disruption due to sudden power outages, with an RPN score of 72. This study proposes risk mitigation recommendations, including Systems connected to the internet that are vulnerable to cyberattacks, such as hacking or malware, which can result in data theft or service disruptions. Therefore, it is essential to implement firewalls and intrusion detection systems to safeguard the network against external threats. The findings provide practical guidance for improving the information security and operational reliability of the Inaportnet system. By implementing these mitigations, the Port Authority is expected to enhance the reliability of port services and protect critical information.

Optimising data sharing whilst protecting participant privacy: a data note describing processed data from a qualitative study of healthcare professionals’ experiences of caring for women with false positive screening test results

ABSTRACT Introduction The present article describes the processed data generated in a qualitative interview study and template analysis. Many women find the experience of being recalled and receiving a false-positive breast screening test result to be distressing. The interview study aimed to understand breast screening healthcare professionals’ (HCPs) experiences of providing care during the recall process and when receiving false-positive screening test results, including their communication with women around false-positive screening test results. Methods Twelve HCPs from a single screening unit in the English National Health Service Breast Screening Programme participated in semi-structured interviews in 2020. All participants were female. A range of HCPs roles were recruited, including advanced radiographer practitioners, breast radiographers, breast radiologists, clinical nurse specialists, and radiology healthcare assistants. The data were analysed thematically using template analysis from a limited realist perspective. Results A total of 20 data files are described, reflecting the iterative nature of template analysis. The files report various versions of codes, subthemes, themes, and every template produced during analysis. The files are publicly available on the Open Science Framework and UK Data Service (ReShare). Discussion This data note outlines our approach to conducting a template analysis of qualitative data while protecting highly identifiable data, which is stored in a non-public archive and only available to the study team. It offers a practical, worked example of the template analysis process, thereby providing a detailed illustration beyond the concise summaries typically found in published reports, and complementing methodological papers of template analysis.

Blockchain Based Cryptographic Algorithm for Data Protection in Electronic Voting System

The development of digital world created advancement of technology, by which the data services are electronically transacted. Electronic voting is used to improve the election system in several ways. But technical issues associated with protection of data, is considered as a major concern leading to illegal activities and threats. Some of the conventional methods used in securing e-votes are matching finger prints of individual, control unit accessibility, which provides decreased capability of attaining proper authenticity and security. Even several cryptographic methods are used for data security, but produced limited accuracy. So, to improve the reliability and accuracy of data protection in e-voting, combination of AES (Advanced Encryption System) and RSA (Rivest, Shamir, Adleman) combined with blockchain technology is implemented. AES uses larger key size which makes the data privacy robust. But encryption and decryption time produces decreases computational speed. So, AES is combined with RSA, as it uses shorter key length and easy to implement with block-chain algorithm, it is deployed to decrease encryption and decryption time, thus producing highly sensitive data security. The performance of the system is validated by calculating the encryption and decryption time, block size and verification time of original data with output data.

Understanding the adoption intention of financial data retrieval services: An empirical analysis of my data.

This study empirically analyzed the variables affecting the adoption intention of a financial data retrieval service (i.e. My Data) through the convergence of the stimulus-organism-response (S-O-R) model, the value-based adoption model (VAM), and the unified theory of acceptance and use of technology (UTAUT1). This approach examined the causal relationships where the characteristics of My Data services (stimulus) influence users' perceived benefit and sacrifice (proposed by VAM), as well as performance expectancy, effort expectancy, social influence, and facilitating conditions (proposed by UTAUT1) (organism), which in turn impact adoption intention (response). The results indicate that the three sub-factors of My Data service characteristics-system quality, information quality, and service quality have significant effects on perceived benefit, perceived sacrifice, performance expectancy, effort expectancy, social influence, facilitating conditions, and adoption intention. These findings provide both theoretical and practical insights for the development, diffusion, and activation strategies of My Data, while simultaneously validating the convergence model's effectiveness.

THE IMPACT OF 5TH INDUSTRIAL REVOLUTION TECHNOLOGIES ON ACADEMIC LIBRARIES: IMPROVING DIVERSITY AND INCLUSIVE SERVICES IN OPEN AND DISTANCE LEARNING

This study investigates the impact of Fifth Industrial Revolution (5th IR) technologies on enhancing diversity and inclusive services in Open and Distance Learning libraries. Using a descriptive design, the research focused on librarians at the National Open University of Nigeria, with data collected through a Google Form questionnaire. Analysis was conducted using SPSS and linear regression to test hypotheses. Findings revealed that librarians believe 5th IR technology improves data extraction, processing, and service efficiency (mean score 3.44, SD = 0.586). The study identifies key implementation strategies, including awareness-raising, curriculum updates, training, collaboration, and skill enhancement. It emphasizes the importance of funding, reliable power supply, and collaboration for effective 5th IR adoption. The study underscores the potential for human-machine partnerships to automate tasks and foster connectivity, suggesting an optimistic outlook for the 5th IR’s role in promoting inclusive, customized library services in academic and distance learning environments.

Scenario-oriented data interoperability: maximising the connection between data and users in collaboration environments

ABSTRACT In large-scale collaborative environments, data interoperability faces challenges due to varying data standards, differences in time and space, and rising demands for data services. Traditional methods focus on integrating resources but often miss the need for interoperability between users and data. This research introduces a new approach to data interoperability, which emphasises scenario-based strategies. We use topic analysis and context fusion to handle industry-specific terminology, making it easier for users to understand cross-speciality data. Our approach includes a concept-extended metamodel to address business logic differences and connect data with user scenarios. We tested our model in a railway company and found it effective and useful based on survey feedback.

Research on the Current Status and Development Trends of AI in Customer Service Systems

The application of artificial intelligence technology in customer service systems is rapidly developing, involving various aspects such as natural language processing and machine learning, greatly improving service efficiency and user satisfaction. However, with the widespread application of these technologies, issues such as data protection, service quality, and humanization have gradually surfaced. This article aims to analyze the current application status of artificial intelligence in the field of customer service, deeply study the various challenges it faces, and propose a series of improvement measures and future development directions to promote the efficient application of artificial intelligence technology in customer service, and provide more excellent service experience for enterprises and consumers.

EdgeGuard: Decentralized Medical Resource Orchestration via Blockchain-Secured Federated Learning in IoMT Networks

The development of medical data and resources has become essential for enhancing patient outcomes and operational efficiency in an age when digital innovation in healthcare is becoming more important. The rapid growth of the Internet of Medical Things (IoMT) is changing healthcare data management, but it also brings serious issues like data privacy, malicious attacks, and service quality. In this study, we present EdgeGuard, a novel decentralized architecture that combines blockchain technology, federated learning, and edge computing to address those challenges and coordinate medical resources across IoMT networks. EdgeGuard uses a privacy-preserving federated learning approach to keep sensitive medical data local and to promote collaborative model training, solving essential issues. To prevent data modification and unauthorized access, it uses a blockchain-based access control and integrity verification system. EdgeGuard uses edge computing to improve system scalability and efficiency by offloading computational tasks from IoMT devices with limited resources. We have made several technological advances, including a lightweight blockchain consensus mechanism designed for IoMT networks, an adaptive edge resource allocation method based on reinforcement learning, and a federated learning algorithm optimized for medical data with differential privacy. We also create an access control system based on smart contracts and a secure multi-party computing protocol for model updates. EdgeGuard outperforms existing solutions in terms of computational performance, data value, and privacy protection across a wide range of real-world medical datasets. This work enhances safe, effective, and privacy-preserving medical data management in IoMT ecosystems while maintaining outstanding standards for data security and resource efficiency, enabling large-scale collaborative learning in healthcare.

Research on Service-Oriented Sharing and Computing Framework of Geographic Data for Geographic Modeling and Simulation

Geographic data are the foundation of geographic model construction, and any stage of their acquisition, processing, and analysis may have an impact on the efficiency and quality of geographic modeling and simulation. With the advent of the era of big data, a large number of data resources are generated in the field of geographic information. However, due to the heterogeneity of geographic data and the security of data usage, massive geographic data resources are difficult to fully explore and utilize, resulting in the formation of data islands. This paper proposes a service-oriented geographic data-sharing and computing framework, which provides users with a complete set of geographic data access and application processes (such as data acquisition, processing, configuration, etc.), so as to reduce the difficulty of using data and improve the efficiency of data sharing. The framework mainly consists of three core components: (1) the “Data service container” can publish data resources as data services to provide a consistent data access interface; (2) the “Workspace” provides a series of methods and tools for users to develop data-computing solutions; and (3) the “Data-computing engine” is responsible for performing computing tasks such as data processing and configuration. Finally, a case of runoff simulation using the SWAT model is designed, in which the whole process of data sharing, acquisition, calculation, and application is realized, so as to verify the validity of the proposed framework.

A Review on Cloud Security: Challenges, Solutions, and Innovations

Cloud computing has become one of the fastest growing and most advance technological evolutions providing companies and individuals the means to scale resources, as well as being flexible and saving cost. While these advantages come with high security risks such as data breaches, unauthorized access, and service disruptions respectively. We review and compare five recent studies that cover aspects of cloud security from encryption, to threat detection, to multi-cloud challenges, to quantum resistant solutions. The papers offer useful information about the emerging security techniques and frameworks. The methodologies, results, limitations and areas for future discussion are presented in a comparative analysis

Using Aotearoa New Zealand’s Open Data with QGIS and Blender to create two map styles

Abstract. This article explores the use of open data, QGIS, and Blender in the creation of two distinct map styles. The New Zealand government’s commitment to open data is demonstrated by the availability of extensive geographical data through the LINZ Data Service and other free providers. The first map style, Natural Basemap, uses land cover and elevation data to produce a pseudo-realistic map. Terrain shading and texture application are used to enhance visual realism. The second style, Perspective Linework, uses Blender’s Freestyle non-photorealistic rendering engine to depict topographical features such as ridgelines and creases from 3D terrain models. Both styles offer innovative approaches to visualising New Zealand’s topography, using open data and software to create visually compelling maps.

The Citation Economy as a Site of Extraction for Surveillance Publishing

This paper links the ideas of surveillance capitalism and what Jeff Pooley has described as surveillance publishing with that of the citation economy. A few companies with dominance over academic publishing have been able to capture and use surplus value created through the publishing lifecycle. This extraction—of academic labour, of data, of information—is reinvested into their proprietary data analytics products. This is both literally, as the data collected by the publishing side can be incorporated into data analytics algorithms, and financially, as the profit margins of these academic publishing arms are astonishingly high. Crucially, these profits have been used to expand these companies’ portfolios of extractive data services across industries as academic publishers transition from information vendors to technology-driven data brokers. By providing their labour directly (as editors, reviewers, etc.) or indirectly (as authors) to these companies, scholars are complicit in data collection and analysis used for everything from advertising to law enforcement. This data is sold back to universities who use it to evaluate and surveil the publishing practices of their employees, using proprietary metrics and methods that do not align with principles of academic freedom. This paper provides an overview of this landscape, concluding with implications and recommendations for the scholars and librarians ensnared in it. It also includes a mini-zine we plan to distribute to help contextualize academics’ roles in the citation economy and the ethical implications for their work.

When Demand Outpaces Capacity: The Evolution of Data Services and Support at an Academic Library

This article explores the changing landscape of data services at Yale University's academic library and examines the evolving roles of data librarians and subject librarians in response to the increasing demand for data-related support in research. The authors offer insights into the shifts and challenges within the academic library environment, highlighting the increasingly multidisciplinary nature of research and the significant contributions of both data and subject librarians in supporting researchers and meeting their data-related needs.

Data Governance and Distribution of Biobank: A Case from a Chinese Cancer Hospital.

Objectives: To facilitate the regionalization, specialization, and digitization of biobanks, three issues regarding data collection and application must be addressed (1) integration and distribution of data governance, (2) efficiency and efficacy of data governance, and (3) sustainability of data governance. Methods: We collaborated with stakeholders to identify priorities and assess infrastructure needs through the continuous evaluation and analysis of projects. We developed data management solutions, catalogs, and data models to optimize and support data collection, distribution, and application. Furthermore, ontologies were used to facilitate data integration from multiple sources, and Minimum Information About BIobank Data Sharing (MIABIS) was defined as accessible to all patients. To enhance data integrity, we conducted retrospective and prospective follow-up studies. Results: We completed infrastructure upgrades to match technical solutions and research demands. An information management software with six primary functional divisions was developed for data governance. We optimized the database structure and changed the biospecimen accumulation model from biospecimen-based to patient-centered and service-oriented. Subsequently, we specified 85 attributes of MIABIS to describe the biobank contents. A dual-pillar approach was adopted to expand the biobank's data in collaboration with other institutions, and MIABIS served as a bridge for both vertical and horizontal networks. From 2003 to 2021, we collected a total of 156,997 patient biospecimens/data from 20 cancer types, matching 53,113 cases from follow-up surveys. In addition, we supplied more than 40,000 biospecimens/data points for above 300 scientific research projects. Conclusions: An appropriate information platform for a biobank is fundamental to data collection, distribution, and application, particularly in the context of data-intensive research. We implemented a standardized scientific data structure to fulfill the research requirements. The sustainable development of a biobank depends on a scientific, standardized, and service-oriented data governance approach, along with the efficient utilization of emerging technologies.

ДЕРЕКТЕРДІ КӨРІНЕТІН ЖАРЫҚ АРҚЫЛЫ БЕРУГЕ БОЛАТЫН МОДУЛЯЦИЯ ӘДІСТЕРІН ЗЕРТТЕУ

The emergence of new directions based on the optical range in the field of communications is explained by existing problems in organizing modern wireless infrastructure. Currently, the radio frequency band widely used for data transmission is overloaded and cannot meet the rapidly growing traffic and demand for wireless data services. Additional implementation of wireless systems based on the use of optical range can help solve existing problems. Visible light communication, as an integral part of optical wireless communication, can realize the requirements for data transmission speed in the network, and reduce the load on the bandwidth of existing wireless networks. The article discusses modulation methods in a wireless optical communication network. Modulation characteristics that are important for system performance are studied. For types of pulse modulation, the power spectral density is calculated and studied to obtain time waveforms with a given signal-to-noise ratio. A model of an ideal transmission and reception system for OOC-NRZ in the presence of additive white Gaussian noise is constructed, which is suitable for indoor visible light data transmission systems. Signal shapes and the influence of instrumental and external noise were studied. Calculated values of the bit error probability based on experimental data are given. The proposed simulation model can be used in the design of real transmitting and receiving devices for a visible light communication system.

Sustainability using Artificial Intelligence: Transformative Solutions in the IT Sector

Sustainability has become a critical priority for businesses and governments as we address environmental changes such as climate change, resource depletion, waste management, and water level increases. This paper investigates the transformative role of artificial intelligence (AI) in sustainable product development, highlighting how advancements in AI technologies are reshaping traditional practices. Traditional product development processes often relied on resource-intensive methods that generated significant waste and offered limited adaptability to sustainability goals. As a cornerstone of the digital economy, the IT sector faces increasing pressure to address its environmental impact, particularly in areas like server management, network optimization, and data transmission. This paper explores the transformative role of Artificial Intelligence (AI) in driving sustainable practices within IT operations. AI-powered solutions, such as dynamic workload management, predictive maintenance, and traffic optimization, enable energy-efficient server and network operations, significantly reducing power consumption. Advanced AI-driven algorithms facilitate data compression, prioritize transmission, and optimize content delivery, minimizing carbon emissions from data-intensive activities. Additionally, AI supports renewable energy integration, tracks carbon footprints, and provides actionable insights for developing sustainable software and IT infrastructure. These innovative applications of AI enhance operational efficiency and contribute to global sustainability goals by reducing the environmental footprint of IT systems. This research highlights the potential of AI to create a greener IT landscape while maintaining the sector’s growth and performance imperatives.

Buyer–supplier TMT background misalignment and green innovation performance: the moderating roles of TMT tenure and incentives

PurposeWhile top management teams (TMTs) play an important role in shaping firms’ strategic choices and performance outcomes, studies on green innovation have rarely considered the influence of the TMT demographics of firms and their suppliers. Drawing upon upper echelon theory, this study investigates the impact of buyer–supplier TMT misalignment on green innovation performance, along with potential moderators of this effect.Design/methodology/approachThe empirical setting is Chinese-listed manufacturing firms that are present in both the Chinese Research Data Services Platform (CNRDS) database and the China Stock Market and Accounting Research (CSMAR) database. The study employs panel data regression methods on a dyadic dataset of 530 paired buyer–supplier firm-year observations over the period 2008–2019.FindingsBuyer–supplier TMT misalignment in terms of functional background and educational level is negatively associated with buyer green innovation performance. This negative effect can, however, be mitigated by TMT tenure and long-term incentives in buyer firms.Originality/valueBy introducing the notion of TMT background misalignment to the supply chain, this study advances the understanding of relational TMT demographics in predicting organizational performance and extends upper echelon theory.

Assessing the quality of data for selected reproductive health indicators in designated public health facilities in Bangladesh.

An effective health management information system plays a pivotal role in evidence-based decision-making and strengthening health service delivery in a country. The Directorate General of Health Services and the Directorate General of Family Planning of Bangladesh have adopted digital health management information system platforms named district health information system and management information system, respectively. Despite its significance, health management information system data has numerous issues, such as missing values, inaccuracies, lack of internal consistency, and the presence of outliers. This study aims to assess the data quality of reproductive health indicators in the health management information system of the Directorate General of Health Services and the Directorate General of Family Planning. The study examined two aspects of data quality: a) completeness of data, subdivided into completeness of facility reporting (report submission rate) and completeness of indicator data (presence of missing values); b) internal consistency of reported data, subdivided into presence of outliers, inter-indicator consistency, and consistency between reported data and original records (accuracy rate). The study utilised retrospective monthly data gathered from July 2021 to June 2022, covering 21 reproductive health indicators. Multi-stage cluster sampling was employed to select 112 health facilities for data collection, including 48 facilities from Directorate General of Health Services and 64 from Directorate General of Family Planning, representing various administrative levels across the country. The report submission rate for Directorate General of Health Services facilities was 98%, while for the Directorate General of Family Planning facilities, it was 86%. However, 35% of data points were missing in the district health information system server of Directorate General of Health Services, whereas no missing values were observed in the management information system server of Directorate General of Family Planning. Less than 3% of outliers were detected in the server data of both directorates. Inter-indicator consistency was maintained at a high rate of 98% in health facilities under both directorates. The accuracy of reported data varied across indicators and facility types: Directorate General of Health Services facilities showed accuracy rates ranging between 75 and 92%, with an aggregated rate of 86%. Different tiers of the Directorate General of Family Planning facilities had accuracy rates ranging from 92 to 96%. This research emphasises the significance of rectifying missing values, ensuring consistency, and improving reporting systems, with a particular focus on lower-tier health facilities, to enhance the validity and reliability of reproductive health data in Bangladesh.