Data Collection Framework Research Articles

Beyond Macroethics: Developing an Ethical Framework for Interview Data Collection and Analysis to Acknowledge Participant Rights

AbstractParticipant rights and voices are protected through institutional ethical considerations in the social sciences and applied linguistics. Yet, several ethical concerns remain. In addition to adhering to institutional macroethics, researchers should develop microethics to address contextual issues within their research. The need for the development of microethics is particularly important in the qualitative research process where researchers engage in studies with participants rather than treating them merely as data sources. Drawing on key concepts in philosophical hermeneutics, the current study discusses an ethical framework for data collection and analysis that embraces participants’ rights and voices through reflexive questioning, reciprocal dialogues, unbiased listening, and rightful analysis of participants’ data with and from two multilingual families in Norway. Participatory diagramming was employed in a collaborative participatory approach to present the collected data and initial data analysis to the participants, allowing them to review and validate the data analysis conducted by the researchers. Using thematic analysis to analyze participants’ experiences and reflections, our findings indicate that the ethical framework of interview and analysis enhanced researchers’ understanding, empowered participants in the research process, facilitated member-checking, created beneficial collaboration opportunities for the participants, and facilitated reaffirmation of informed consent.

The Role of Information Technology in the Conservation of Intangible Cultural Heritage at Bagamoyo, Tanzania

This study assessed the role of information technology in the Conservation of intangible cultural heritage in Tanzania specifically at Bagamoyo. It assessed the role of information technology in the protection and inheritance of intangible cultural heritage and the development of information technology in the protection and inheritance of intangible cultural heritage. The study used a quantitative approach through a case study design as the framework for data collection. The study collected data from 40 respondents through questionnaire and observation guide. The data was analysed using SPSS, where frequencies and percentages as well as mean scores were computed after which tables were derived. The study found that Information technology in important in conservation of both intangible and tangible cultural heritage albeit with some challenges. Some of the important aspects of information technology in cultural heritage conservation were such as Digital documentation, analysis and restoration, enhancing accessibility, virtual and augmented reality, language and accessibility, Documentation and archiving, digitization of performances and art forms, oral history preservation, and enhancing accessibility were important aspects through which information technology is used for cultural heritage conservation. The study recommended for enhance digital documentation and archiving, increase funding and resource allocation, develop collaborative partnerships, implement community engagement programs, enhance accessibility and sharing of digital resources and leveraging mobile technology and social media.

Guidance for reporting 2024 laboratory data on avian influenza

Abstract This technical report aims at guiding the reporting of data on analytical test results, and related metadata, to EFSA in the context of the activities for the surveillance of avian influenza. The objective is to explain in detail the individual data elements that are included in the EFSA Standard Sample Description version 2 (SSD2) data model. The guidance is intended to support the reporting countries in data transmission using eXtensible Markup Language (XML) data file transfer through the Data Collection Framework (DCF) according to the protocol described in the EFSA Guidance on Data Exchange version 2 (GDE2). The data elements are explained, including information about data type, list of allowed terms and associated business rules. Instructions about how to report common sampling schemes are also provided to ensure harmonised reporting among countries.

The Regensburg Dental Trauma Registry: Methodical Framework for the Systematic Collection of Dentoalveolar Trauma Data.

Objectives: Traumatic dental injuries (TDIs) are common, particularly in children and adolescents, and require timely, well-documented treatment for optimal long-term functional and esthetic outcomes. Despite their prevalence, comprehensive data on TDI remain limited. The Regensburg Dental Trauma Registry (RDTR) was established to enable structured data collection, documentation and analysis of dentoalveolar trauma cases to improve both research and clinical practice. Methods: The RDTR was developed at the Centre for Dental Traumatology at the University Hospital Regensburg as part of a multi-stage implementation process, which involved creating clinical infrastructure, establishing treatment protocols, providing continuous clinician training, and designing a standardized documentation form to capture essential data, including patient demographics, accident details, clinical assessments, and initial treatment. Data are transferred into a REDCap electronic case report form (eCRF), which is hosted on secure university servers, ensuring efficient administration, controlled access and high data integrity. Quality assurance measures, including automated and manual data checks and regular treatment protocol updates, maintain high data accuracy and consistency. Results: This initial methodological report outlines the systematic approach of the RDTR and its potential to generate large datasets. These will enable in-depth analyses of injury patterns, treatment effectiveness, risk factors, and more. Future expansion includes collaboration with additional university hospitals to broaden the dataset and support multi-center approaches. Conclusions: The RDTR offers a framework for consistent data collection and quality control, laying the foundation for comprehensive analyses that contribute to the development of preventive strategies and treatment protocols.

UAV-Enabled Diverse Data Collection via Integrated Sensing and Communication Functions Based on Deep Reinforcement Learning

Unmanned aerial vehicles (UAVs) and drones are considered to represent a flexible mobile aerial platform to collect data in various applications. However, the existing data collection methods mainly consider uplink communication. The burgeoning development of integrated sensing and communication (ISAC) provides a new paradigm for data collection. A diverse data collection framework is established where the uplink communication and sensing functions are both considered, which can also be referred to as the uplink ISAC system. An optimization is formulated to minimize the data freshness indicator for communication and the detection freshness indicator for sensing by optimizing the UAV paths, the transmitted power of IoT devices and UAVs, and the transmission allocation indicators. Three state-of-the-art deep reinforcement learning (DRL) algorithms are utilized to solve this optimization. Experiments are conducted in both single-UAV and multi-UAV scenarios, and the results demonstrate the effectiveness of the proposed algorithms. In addition, the proposed algorithms outperform the benchmark in terms of accuracy and efficiency. Moreover, the effectiveness of the data collection mode with only communication or sensing functions is also verified. Also, the numerical Pareto front between communication and sensing performance is obtained by adjusting the importance parameter.

Comparative assessment of ESG ratings methodology and results based on XBRL

This study provides a comparative analysis of Environmental, Social, and Governance (ESG) ratings methodologies and explores the potential of eXtensible Business Reporting Language (XBRL) to enhance transparency and comparability in ESG reporting. Evaluating ratings from different agencies, the research identifies significant methodological inconsistencies that lead to conflicting information for investors and stakeholders. Statistical tests and adjusted rating scales confirm substantial divergence in ESG scores, primarily due to differing data categories and indicators used by rating firms. Using a sample of 265 European companies, the study demonstrates that individual ESG agencies report markedly different ratings for the same firms, which can mislead stakeholders. It proposes that XBRL based reporting can mitigate these inconsistencies by providing a standardized framework for data collection and reporting. XBRL enables accurate and efficient data collection, reducing human error and enhancing the transparency of ESG reports. The findings advocate for integrating XBRL in ESG reporting to achieve higher levels of comparability and reliability. The study calls for greater regulatory oversight and the adoption of standardized taxonomies in ESG reporting to ensure consistent and comparable data across sectors and jurisdictions. Despite challenges like the lack of a standardized taxonomy and inconsistent adoption, the research contends that XBRL can significantly improve the reliability of ESG ratings. In conclusion, this study suggests that standardizing ESG data through XBRL could provide a viable solution to the unreliability of current ESG rating scales, supporting sustainable business practices and informed decision making by investors.

Integrating Sustainability and Cultural Sensitivity: Clustering Muslim Tourist Lifestyles in the Andaman Coastal Cluster, Thailand

This study explores the integration of sustainability and cultural sensitivity in clustering Muslim tourist lifestyles in the Andaman coastal region. The Muslim tourist demographic, representing a significant portion of the global population, prioritizes halal standards and Islamic attributes in their travel experiences. This research investigates the lifestyle patterns of Muslim tourists and their alignment with sustainable tourism principles, particularly in Thailand’s Andaman coastal area, known for its marine and beach tourism. A multi-method approach was employed, utilizing the AIO (Activities, Interests, and Opinions) framework for quantitative data collection through the surveys of 836 Muslim tourists, and qualitative insights from in-depth interviews and focus group discussions with local stakeholders. The study identified key lifestyle clusters: Mainstream Travelers, Experience Seekers, and Chillouts, each with specific preferences for halal services, prayer facilities, and eco-friendly accommodations. The findings underscore the importance of a tailored approach in promoting sustainable tourism that respects cultural values. Recommendations include enhancing halal-certified services, improving infrastructure for religious practices, and promoting eco-friendly tourism options. By aligning sustainability with cultural sensitivity, the Andaman coastal region can attract more Muslim tourists while preserving its environmental and cultural heritage. This research contributes to the broader discourse on sustainable tourism by emphasizing cultural sensitivity and provides practical guidelines for tourism stakeholders in the Andaman region to develop inclusive and sustainable tourism strategies.

An agile city? Tactical urbanism and responses to protracted displacement in the City of Amman, Jordan

This study seeks to understand the working dynamics through which Greater Amman Municipality (GAM) responds to the challenges of protracted displacement in the city, focusing particularly on the concept of “tactical urbanism” (TU) as a tool and framework. This could be broadly useful to key actors in reconsidering response strategies, strengthening the role of local administrations. The research adopted a qualitative data collection and analysis framework via participatory forums and key informant interviews, steered by guiding research questions. Findings reveal the presence of interconnected multi-scale limitations, opportunities and roles that determine GAM’s response towards displacement and constrain its ability to act within a comprehensive planning rationale. Instead, GAM is driven towards TU as an alternative approach that mitigates these determinants and offers a means for more responsive, constructive action. It is argued that TU affords a more agile response towards displacement, enhancing community engagement, innovation, resilience and identity.

ScamGen: Unveiling psychological patterns in tele-scam through advanced template-augmented corpus generation

Telephone scams, with their profound psychological impact, often compel victims to make hasty and severe decisions. Studying these scams is challenging due to the scarcity of comprehensive datasets, a result of the private nature of telephone interactions. In this paper, we introduce ScamGen, a template-based data augmentation technique designed to enhance Chinese telephone scam data. ScamGen leverages psychological insights to generate diverse and realistic scam scenarios, focusing on the psychological dynamics between scammers and victims. This novel approach integrates psychological theory with data augmentation, diverging from traditional methods by emphasizing scammer–victim interactions. Our method begins with a multi-source data collection framework, compiling an initial seed dataset of tele-scam samples. Using sentence- and word-level perturbations, we expand this seed data to create a comprehensive and diverse dataset covering a wide range of scam scenarios. Rigorous evaluations demonstrate that ScamGen outperforms large language models in generating high-quality, varied datasets. Additionally, we develop five deep learning models for intent detection on this dataset, with BERT achieving the highest precision at 86.68%. The dataset, which will be made publicly available, marks a significant step toward understanding scammer tactics and improving tele-scam detection systems.

Exploring the Confluence of Animal Medicine and its Implications for Human Health: A Systematic Literature Review.

The abuse of veterinary drugs has emerged as a concerning trend, with global fatalities on the rise. Our understanding of this phenomenon remains limited. This study aims to identify the veterinary drugs being misused, the reasons behind their misuse, and how they are obtained. Utilising PubMed, Scopus, and Web of Science databases, along with related grey literature, we applied the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) framework for data collection. Screening and cross-referencing yielded 66 relevant articles, encompassing case reports, surveys, reports, and systemic literature reviews. The analysis identified 28 distinct veterinary drugs being misused in humans, primarily falling into categories, e.g., α-2- and β-2-adrenergic receptor agonists, GABAergic receptor modulators, opioid receptor agonists, nonsteroidal anti-inflammatory drugs (NSAIDs), and N-methyl-D-aspartate (NMDA) receptor antagonists. These drugs were used for various purposes, including recreational use, weight loss, bodybuilding, pain relief, and self-medication for stress-related symptoms. Routes of administration predominantly included parenteral, oral, and inhalation methods. Veterinary workers/assistants and individuals connected to animals were identified as contributors to the misuse of these medications. Motivations for their utilisation ranged from affordability and accessibility to the ease of obtaining multiple prescriptions from various veterinary sources, often in conjunction with other illicit substances. Dependence and addiction were common outcomes of the misuse of veterinary medicines by humans. Overall, this systematic review underscores the increasing popularity of veterinary prescription drug misuse despite being under-reported with limited available data. Healthcare professionals are urged to remain vigilant to potential overdose events involving these medications.

Mapping the complexity of task demands during constant observation of patients with complex health behaviors.

Unlicensed patient care assistants (PCAs) are often tasked with providingconstant observation (CO)of patients withcomplex health behaviors. The unique demands of CO are poorly understood, making it challenging to design effective training.Four-componentinstructional design theory suggests the tasks associated with CO featurenon-recurrentchallenges, such that effective, simulation-based deliberate practice must reflect real-life variability.This study aimed toelucidate the variety of challenges PCAsencounter during CO of patientswithcomplex health behaviors. We used a qualitativedescriptivedesignand developed apreliminary framework to code interviews.The data collection framework wasdesigned to support creating numerous realistic scenariosto support generalized and transferable learning.From our interviews with 16 participants, we identified 1,066 statements associated with variations in CO tasks. We grouped our findings by two domains, "patient factors" and "environmental contexts." Our results revealed many recurring and non-recurring challengesinherent in CO, requiring arange of skills to maintain patient and staff safety.Instructional design elements may include scenarios incorporatingenvironment and resource assessment,cognitive feedback for non-recurrent tasks, novel methods of self-harm initiated by the patient, and incorporating interdisciplinary staff in which the learner must navigate a complex conversation. This studyclarifiesthetask demands of CO andisuseful as a task analysis to guide scenario development for simulation-based experiences. A less systematic approachwould risk underrepresenting the difficulties inherent in the task, reinforcing a limited set of behaviors that may not generalize well to the non-recurrent challenges in CO.

IoV block secure: blockchain based secure data collection and validation framework for internet of vehicles network

IoV block secure: blockchain based secure data collection and validation framework for internet of vehicles network

Integrative Approaches to Abiotic Stress Management in Crops: Combining Bioinformatics Educational Tools and Artificial Intelligence Applications

Abiotic stresses, including drought, salinity, extreme temperatures and nutrient deficiencies, pose significant challenges to crop production and global food security. To combat these challenges, the integration of bioinformatics educational tools and AI applications provide a synergistic approach to identify and analyze stress-responsive genes, regulatory networks and molecular markers associated with stress tolerance. Bioinformatics educational tools offer a robust framework for data collection, storage and initial analysis, while AI applications enhance pattern recognition, predictive modeling and real-time data processing capabilities. This review uniquely integrates bioinformatics educational tools and AI applications, highlighting their combined role in managing abiotic stress in plants and crops. The novelty is demonstrated by the integration of multiomics data with AI algorithms, providing deeper insights into stress response pathways, biomarker discovery and pattern recognition. Key AI applications include predictive modeling of stress resistance genes, gene regulatory network inference, omics data integration and real-time plant monitoring through the fusion of remote sensing and AI-assisted phenomics. Challenges such as handling big omics data, model interpretability, overfitting and experimental validation remain there, but future prospects involve developing user-friendly bioinformatics educational platforms, establishing common data standards, interdisciplinary collaboration and harnessing AI for real-time stress mitigation strategies in plants and crops. Educational initiatives, interdisciplinary collaborations and trainings are essential to equip the next generation of researchers with the required skills to utilize these advanced tools effectively. The convergence of bioinformatics and AI holds vast prospects for accelerating the development of stress-resilient plants and crops, optimizing agricultural practices and ensuring global food security under increasing environmental pressures. Moreover, this integrated approach is crucial for advancing sustainable agriculture and ensuring global food security amidst growing environmental challenges.

Design of real-time & historical database for photovoltaic power plant

Abstract The real-time & historical database of photovoltaic power plant monitoring system is used to store and manage the real-time and historical data, including power plant parameters, environmental data, and photovoltaic unit data and so on. Analyzing of this data is helpful for monitoring the status of the plant equipment, performing maintenance, and enhancing the operational efficiency of the power plant. The data models and database function of the database is designed. The data models include tags, data processing, and data storage models. Database function include the modules of the collection, storage, compression, and index query. The data collection module utilizes dynamic link libraries to access database data and configures parameters through a comprehensive data collection framework. The storage strategies involve in a combination of in-memory databases and disk-based historical databases, incorporating secondary caching technology and cloud server storage techniques. The data compression module is achieved through secondary compression methods. The index query module encompass time index files and the secondary index files to improve query speed and real-time system performance. The designed elements collectively establish an efficient monitoring system database.

A cross-institutional database of operational risk external loss events in Chinese banking sector 1986–2023

Nowadays the collection of operational risk data worldwide highly relies on human labor, leading to slow updates, data inconsistency, and limited quantity. There remains a substantial shortage of publicly accessible operational risk databases for risk analysis. This study proposes a new data collection framework by aggregating text mining methods to replace the exhausting manual collection process. The news about operational risk can be automatically collected from the web page, then its content is analyzed and the key information is extracted. Finally, the Public-Chinese Operational Loss Data (P-COLD) database for financial institutions is constructed and expanded. Each record contains 12 key information, such as occurrence time, loss amount, and business lines, offering a more thorough description of operational risk events. With 3,723 data records from 1986 to 2023, the P-COLD database has become one of the largest and most comprehensive external operational risk databases in China. We anticipate the P-COLD database will contribute to advancements in operational risk capital calculations, dependence analysis, and institutional internal controls.

Exploring Symptom Management Experiences Among College Students With Type 1 Diabetes Mellitus Using a Theoretical Framework: A Qualitative Study.

The purpose of this study was to explore symptom management experiences among college students with type 1 diabetes mellitus (T1DM). Limited qualitative data using a theoretical framework exist that explore the self-care behavior processes for symptom management. A qualitative approach was used for this study. The middle-range theory of self care of chronic illness served as a framework for data collection and analysis procedures. Data collection included distributing a survey to collect participants' demographic and sociodemographic data and utilizing a semi-structured interview guide to conduct one-on-one interviews with 31 participants. Interviews occurred via Zoom (n = 28) and in person (n = 3). Interview transcripts were uploaded in NVivo for data management. The research team created a codebook using theoretical constructs to assist with thematic analysis. Data are representative of a sample whose characteristics include undergraduate students ages 18 to 23 living with T1DM for 2 years or more who attended large, public, 4-year universities located in the southeastern United States. Three main themes were created using theoretical constructs: symptom detection experiences, symptom interpretation experiences, and symptom response experiences. Two subthemes were identified for each theme. Participants engaged in symptom management for blood glucose regulation through detecting changes in their blood glucose physiologically and via technology. Additionally, symptom interpretation involved analyzing blood glucose trends and determining common causes of blood glucose changes. Symptom response included immediately addressing hypoglycemia but delayed responses addressing hyperglycemia. Challenges were present responding to hypoglycemia; therefore, additional research is warranted to improve symptom response skills.

Recent Trends in the Study of Springs in Nepal: A Review

Springs, a component of groundwater systems, are a vital source of fresh water for fulfilling people's demand for drinking water, household uses, and irrigation, especially in the Middle Hill region of Nepal. Springs provide water for base flows and lifelines for many rivers originating from the Middle Hill regions. The present study reviews a recent trend of spring studies and investigations in Nepal through a systematic search of published and unpublished works related to springs, which are freely available. The results show 47 publications, out of 30 are published, and 17 are unpublished. The origin of published work is mainly related to project-related works, whereas unpublished works come from the academic sector for fulfilling academic criteria for thesis research. According to the physiographical division of Nepal, the study area falls in the Middle Hills of Nepal, with the maximum area located in Bagmati Province. Most of the studies that qualitative rather than quantitative information of springs. Studies are not linked with spring source and their seasonal dynamics. However, clearly available data, attributes and information about springs from 47 reviewed documents are noted. Systematic data generation and a standard framework for data collection are also missing. Nevertheless, out of 47 studies, including 11 published and 4 unpublished, the total number of springs per sq. km. in the Middle Hill region of Nepal is estimated as 2.57, which can be integrated after more research on future springs–related work.

Algorithm for detecting abnormal electricity usage by customers based on electricity big data and deep learning

Abstract In order to optimize the effect of individuals’ aberrant power utilization detection and reinforce the recognition rate and accuracy of abnormal power consumption detection of electricity use, an aberrant power utilization identification algorithm based on electric power big data and deep learning is proposed. Taking the two-way dialogue between power users and the power company as the basic perspective, real-time monitoring and collection of power consumption dynamics of users is carried out, and the power big data collection framework is designed. The retrieval of aberrant power utilization detection indexes is completed. Based on the deep learning algorithm, the artificial neural network base operation medium is combined to quantitatively solve the aberrant power utilization indexes, completing the design of a user aberrant power utilization detection algorithm. Experimental results show that the accuracy of the designed algorithm is 0.963, the F1 value is 0.985, and the AUC value is 0.942, which can effectively identify the aberrant power utilization and has a better performance of abnormal power consumption identification and detection.

System Design, Automatic Data Collection Framework and Embedded Software Development of Internet of Things (IoT) for Air Pollution Monitoring of Nagpur Metropolis

System Design, Automatic Data Collection Framework and Embedded Software Development of Internet of Things (IoT) for Air Pollution Monitoring of Nagpur Metropolis

Data collection framework for enhanced carbon intensity indicator (CII) in the oil tankers

AbstractThe International Maritime Organization (IMO) aims to reduce greenhouse gas (GHG) emissions by 40% by 2030 compared with 2008. The carbon intensity indicator (CII) calculates the annual reduction factor required to continuously improve a ship's operational carbon intensity at a specific rating level. Verification and documentation of the achieved annual operational CII against the prescribed target are necessary to establish the operational carbon intensity rating. This study focuses on the intricate process of data collection for CII within the oil shipping industry, targeting engineering departments and shipboard management teams. Against the backdrop of the industry's substantial carbon dioxide emissions, the IMO has mandated the calculation of CII values for ships exceeding 5000 gross tons to promote sustainability and reduce environmental impact. We have collected emission data of 20 oil tankers over a period of 2 years using our ship maintenance and operating system (SMOS) and analyzed the data to compare the CII ratings. Our results indicate that a staggering ~63% of the vessels had the lowest CII rating of category E. It is therefore crucial to properly collect, organize, and evaluate data for CII calculation and take necessary measures to improve rating. This paper provides a deeper insight into the evolving CII calculation methodology, emphasizing the incorporation of correction factors and exclusions, and delineates the essential data collection practices needed to facilitate accurate CII calculations.