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Looking back at 50 years of Journal of Consumer Research methods and interviewing some of the field's most respected methodologists, this article seeks to craft a core set of best practices for scholars in consumer research. From perennial issues like conceptual validity to emerging issues like data integrity and replicability, the advice offered by our experts can help scholars improve the way they approach their research questions, provide empirical evidence that instills confidence, use new tools to make research more inclusive or descriptive of the "real world," and seek to become thought leaders. [ABSTRACT FROM AUTHOR]

BACKGROUND: Extreme Risk Protection Orders (ERPOs) are civil court orders that prohibit firearm purchase and possession when someone is behaving dangerously and is at risk of harming themselves and/or others. As of June 2024, ERPOs are available in 21 states and the District of Columbia to prevent firearm violence. This paper describes the design and protocol of a six-state study of ERPO use. METHODS: The six states included are California, Colorado, Connecticut, Florida, Maryland, and Washington. During the 3-year project period (2020-2023), ERPO case files were obtained through public records requests or through agreements with agencies with access to these data in each state. A team of over four dozen research assistants from seven institutions coded 6628 ERPO cases, abstracting 80 variables per case under domains related to respondent characteristics, events and behaviors leading to ERPO petitions, petitioner types, and court outcomes. Research assistants received didactic training through an online learning management system that included virtual training modules, quizzes, practice coding exercises, and two virtual synchronous sessions. A protocol for gaining strong interrater reliability was used. Research assistants also learned strategies for reducing the risk of experiencing secondary trauma through the coding process, identifying its occurrence, and obtaining help. DISCUSSION: Addressing firearm violence in the U.S. is a priority. Understanding ERPO use in these six states can inform implementation planning and ERPO uptake, including promising opportunities to enhance safety and prevent firearm-related injuries and deaths. By publishing this protocol, we offer detailed insight into the methods underlying the papers published from these data, and the process of managing data abstraction from ERPO case files across the multi-state and multi-institution teams involved. Such information may also inform future analyses of this data, and future replication efforts. REGISTRATION: This protocol is registered on Open Science Framework ( https://osf.io/kv4fc/ ).

In recent years, the way that maintenance is carried out has evolved due to the incorporation of digital tools and Industry 4.0 concepts. By connecting to and communicating with their production system, companies can now gather information about the current and future health of the equipment, enabling more efficient control through a process called predictive maintenance (PdM). The goal of PdM is to reduce unplanned downtimes and proactively address maintenance needs before failures occur. However, it can be challenging for industrial practitioners to implement an intelligent maintenance system that effectively manages data. This paper presents a methodology for developing and implementing a PdM system in the automotive industry, using open standards and scalable data management capabilities. The platform is validated through the presentation of two industry use cases. [ABSTRACT FROM AUTHOR]

In the last decades, clinical laboratories have significantly advanced their technological capabilities, through the use of interconnected systems and advanced software. Laboratory Information Systems (LIS), introduced in the 1970s, have transformed into sophisticated information technology (IT) components that integrate with various digital tools, enhancing data retrieval and exchange. However, the current capabilities of LIS are not sufficient to rapidly save the extensive data, generated during the total testing process (TTP), beyond just test results. This opinion paper discusses qualitative types of TTP data, proposing how to divide laboratory-generated information into two categories, namely metadata and peridata. Being both metadata and peridata information derived from the testing process, it is proposed that the first is useful to describe the characteristics of data, while the second is for interpretation of test results. Together with standardizing preanalytical coding, the subdivision of laboratory-generated information into metadata or peridata might enhance ML studies, also by facilitating the adherence of laboratory-derived data to the Findability, Accessibility, Interoperability, and Reusability (FAIR) principles. Finally, integrating metadata and peridata into LIS can improve data usability, support clinical utility, and advance AI model development in healthcare, emphasizing the need for standardized data management practices. [ABSTRACT FROM AUTHOR]

This scoping review maps research ethics and integrity challenges and best practices encountered by research actors in the DACH countries (Germany, Austria, and Switzerland), including researchers, funders, publishers, research ethics committees, and policymakers, during the COVID-19 pandemic. The COVID-19 pandemic brought research and, in turn, research ethics and integrity, into public focus. This review identified challenges related to changing research environments, diversity in research, publication and dissemination trends, scientific literacy and trust in science, recruitment, research redundancy and study termination, placebo and human challenge studies, data management, and informed consent. These challenges are linked to two crucial factors: first, actors in the DACH research ecosystem lacked a sound knowledge base to assess the risks and benefits of research during the COVID-19 pandemic. Second, researchers in the DACH region faced pressure from policymakers, funders, and the public to generate relevant, timely, and consistent findings to mitigate the impacts of the COVID-19 pandemic. In addition, this review highlights best practices to mitigate the effects of future crises on research ethics and integrity, including enhanced cooperation among actors, continuous ethics assessments, and support for public scientific literacy. [ABSTRACT FROM AUTHOR]

Access control is a fundamental component of any data management system, ensuring the prevention of unauthorized data access. Within the realm of data streams, it plays a crucial role in query processing by facilitating authorized access to them. This paper introduces the StreamFilter framework, which focuses on securely processing queries with range filters over streaming data. Leveraging the Role-Based Access Control model, the StreamFilter framework enables the specification of fine-grained access policies at various levels of granularity, such as tuples and attributes, through the utilization of a bit string structure. To enhance the search operation during data stream query processing, the framework employs a distributed indexing method, constructing a set of smaller B + Tree indices rather than a single large B + Tree index. Furthermore, it seamlessly integrates access authorization evaluation with query processing, efficiently filtering unauthorized parts from the query results. The experimental results demonstrate an approximately 50% increase in efficiency for processing queries with range filters compared to the post-filtering strategy. This improvement is observed across all types of data distribution, including uniform, skew, and hyper skew. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) connects objects that sense, communicate, and interact to achieve shared goals, and its integration with manufacturing has led to Industry 4.0. Security is crucial for user acceptance, involving data confidentiality, integrity, authentication, identity management, privacy, and trust among devices. Trust management is vital in IoT and IIoT for reliable services, accurate data collection, device authentication, and secure decision-making. This paper surveys blockchain-based trust mechanisms in IoT and IIoT, categorizing them into four types: basic trust frameworks, enhanced trust interaction, trust data management, and trust-based transaction validation. We analyze these mechanisms to identify their strengths, weaknesses, and current challenges. Blockchain shows promise in improving trust management by decentralizing trust, ensuring transparency, and enhancing security. However, challenges like scalability, energy consumption, and integration with existing IoT infrastructure remain. This survey aims to guide future research by highlighting these issues and proposing potential solutions, contributing to the development of blockchain-based trust management systems in IoT and IIoT. [ABSTRACT FROM AUTHOR]

In oil and gas industry, conductor casing design and installation provide structural integrity to the well system, withstanding loads from equipment and offshore environment during all construction and production phases. In this context, decision-making process of a suitable soil-casing coupling must be supported by high-quality soil data. As such, evaluation of the soil spatial variability demands several assessments of the soil properties in different locations and, in ultradeep waters, execution of a reasonable number of experimental measurements is cumbersome and expensive. Regarding the aspect of parametric modeling using limited information, the current work presents an approach for managing CPTu data to bridge geotechnical and top hole design knowledge using geostatistical methods and recommended practices for oil and gas industry. This approach integrates kriging-based techniques and DNV formulation in an integrated model to analyze geotechnical parameters depth- and geographically-wise. All evaluated information is used to complement experimental analysis, providing indicators of geomechanical behavior within highly correlated area. The developed methodology is made available in a prototype which allows handling CPTu parameters in a user-friendly automated way. [ABSTRACT FROM AUTHOR]

Background: A skilful data management is the heart of any cohort study as results of such studies have far reaching impact on development national policies which will improve public health. Methods: DERVAN cohort is a longitudinal study set up in KONKAN region of India. The study is expected to last at least 20 years. It plans to investigate impact of adolescent growth, diet and cognition on the risk of development of non-communicable diseases in the adulthood with diabetes as the main focus. The study will also investigate parents and investigate their contribution to the risk. We plan to recruit 1520 adolescent girls. MS Access was used to design data management system. Each family was given a unique identity number. At base line we have created 21 tables, 10 queries for adolescent data and 5 tables, 2 queries for parental data. The data is analysed using statistical software SPSS. Conclusion: Though our system may not be designed using high end data base systems, it still caters to our needs in the initial stage of the project but its skilful design is expected to make a smooth adaptation to new database environment in the subsequent stages. [ABSTRACT FROM AUTHOR]

Biorefineries offer a new solution for the sustainable production of chemicals, materials, fuels, heat, and power while reducing dependence on non-renewable resources and helping to mitigate climate change. To ensure a broad implementation of biorefineries, efficiency is vital in the planning phase and must be monitored during operation. However, optimizing biorefinery processes can be challenging due to the inherent complexity of biomass feedstock, its variability, and the several technology combinations available. Digital mapping or modeling through approaches such as Digital Twins can lead to a better understanding of the processes involved. A Digital Twin fed by continuous process data can contribute to optimizing biorefinery processes, improving efficiency and helping to minimize adverse impacts. However, acquiring all relevant process parameters can be challenging due to the need to implement sensors and the volume and quality of the generated data. Here, we present the concept of Lean Digital Twin for biorefinery optimization to achieve efficient operation and monitoring while keeping overall data acquisition, processing, and storage as simple as possible. We show that implementing data-driven optimization strategies can help ensure the sustainability of biorefineries. [ABSTRACT FROM AUTHOR]

To solve the problems of unstable chain and poor supply chain (SC) function in the China’s agriculture industry, the relevant study is carried out on the function evaluation of the sustainable agricultural SC by structural equation modelling (SEM) under the background of digital technology. Primarily, the analysis is implemented on the research status of the sustainable SC and SC function. Then, starting with the relationship among big data (BD) application, information sharing, and sustainable SC function of agricultural production, a function evaluation model is implemented for the sustainable SC of SEMbased agricultural production. Next, a questionnaire is designed for the research on the impact of BD applications on the function of the sustainable agricultural SC. Ultimately, the statistical analysis of the obtained data reveals the correlation between the interaction and influence among the BD application, information sharing, and sustainable SC function of agricultural production. The results show that the BD application dimension, information sharing dimension, and all sub-dimensions have is beneficial to the function of the agricultural sustainable SC. The application of multidimensional cloud technology in BD has no obvious relationship with the dimensionality and level of information sharing, and the relationship between other corresponding dimensions shows a significant positive impact. A referenceable method is provided for the practice of sustainable SC management of agricultural production. [ABSTRACT FROM AUTHOR]

Question Answering (QA) systems provide accurate answers to questions; however, they lack the ability to consolidate data from multiple sources, making it difficult to manage complex questions that could be answered with additional data retrieved and integrated on the fly. This integration is inherent to Situational Data Integration (SDI) approaches that deal with dynamic requirements of ad hoc queries that neither traditional database management systems, nor search engines are effective in providing an answer. Thus, if QA systems include SDI characteristics, they could be able to return validated and immediate information for supporting users decisions. For this reason, we surveyed QA-based systems, assessing their capabilities to support SDI features, i.e., Ad hoc Data Retrieval, Data Management, and Timely Decision Support. We also identified patterns concerning these features in the surveyed studies, highlighting them in a timeline that shows the SDI evolution in the QA domain. To the best of your knowledge, this study is precursor in the joint analysis of SDI and QA, showing a combination that can favor the way systems support users. Our analyses show that most of SDI features are rarely addressed in QA systems, and based on that, we discuss directions for further research. [ABSTRACT FROM AUTHOR]

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Background: Covid-19 pandemic highlighted the need for implementing Personal Electronic Health Records (PEHR) for patients' data management. Furthermore, this pandemic underscored the relevance for integrated and interoperable Electronic Health Records (EHR) to support disease surveillance, hospital capacity planning and resource management (Peek N, Sujan M, Scott P (2020) Digital health and care in pandemic times: impact of COVID-19. BMJ Health Care Inf 27(1):e100166. https://doi.org/10.1136/bmjhci-2020-100166). Due to the lack of comprehensive patients' record in plastic, reconstructive and aesthetic surgery, Axon's myHealth app offers a break-through patient-centric design allowing patients to be in control of their records and updating them in real-time for their plastic and aesthetic care providers to have a clearer understanding of patients' history and progress from pre-op to post-op. Methods: The Axon Dublin survey took place during Covid-19 pandemic in two phases: Phase 1 aimed to assess the feasibility of patients integrating the Axon myHealth application into their clinical visits. Testing occurred in a clinical environment, where patients were encouraged to download and use the Axon system with a health practitioner (HP) present. Phase 2 focused on home testing, evaluating patients' willingness to manage their health remotely with HP assistance. This phase included self-testing activities such as performing rapid Covid-19 antigen tests, recording medical history, and measuring blood pressure at home. Results: The Axon Dublin Study aimed to assess patient engagement, clinical impact, and cost-effectiveness of the Axon myHealth application. Over 85% of patients showed interest in owning a Personal Electronic Health Record. Notably, 36% continuously monitored chronic conditions. Clinical decisions, informed by patient data, saw 61.9% compliance. Noteworthy, 23% of hypertensive participants required immediate medication changes. Patient self-capture of data reduced consultation time. Public health implications were significant, with 39% vaccinated and 31% reporting complications. High user satisfaction (97%) demonstrated the app's effectiveness in infection control and chronic care. Conclusions: Offering patients the ability to update and control their data is a growing interest, with a clear need in plastic and aesthetic surgery to have a better understanding of a patient's medical past and progress throughout the surgical process and period. This platform, which is time and cost efficient, can only facilitate personalised care and improve outcomes while maintaining patient's confidentiality. Level of evidence: Not gradable. [ABSTRACT FROM AUTHOR]

Collaborative neuroscience requires systematic data management and analysis. How this is best done in practice remains unclear. Based on a survey across collaborative neuroscience projects, we document the current state of the art focusing on data integration, sharing, and researcher training. We propose best practices and list actions and policies to attain these goals. Collaborative neuroscience requires systematic data management and analysis. How this is best done in practice remains unclear. Based on a survey across collaborative neuroscience projects, Schottdorf et al. document the current state of the art focusing on data integration, sharing, and researcher training. Schottdorf et al. propose best practices and list actions and policies to attain these goals. [ABSTRACT FROM AUTHOR]

Emerging technologies of artificial intelligence (AI) and automated decision‐making (ADM) promise to advance many industries. Healthcare is a key locus for new developments, where operational improvements are magnified by the bigger‐picture promise of improved care and outcomes for patients. Forming the zeitgeist of contemporary sociotechnical innovation in healthcare, media portrayals of these technologies can shape how they are implemented, experienced and understood across healthcare systems. This article identifies current applications of AI and ADM within Australian healthcare contexts and analyses how these technologies are being portrayed within news and industry media. It offers a categorisation of leading applications of AI and ADM: monitoring and tracking, data management and analysis, cloud computing, and robotics. Discussing how AI and ADM are depicted in relation to health and care practices, it examines the sense of promise that is enlivened in these representations. The article concludes by considering the implications of promissory discourses for how technologies are understood and integrated into practices and sites of healthcare. [ABSTRACT FROM AUTHOR]

Neuroscience research has evolved to generate increasingly large and complex experimental data sets, and advanced data science tools are taking on central roles in neuroscience research. Neurodata Without Borders (NWB), a standard language for neurophysiology data, has recently emerged as a powerful solution for data management, analysis, and sharing. We here discuss our labs’ efforts to implement NWB data science pipelines. We describe general principles and specific use cases that illustrate successes, challenges, and non-trivial decisions in software engineering. We hope that our experience can provide guidance for the neuroscience community and help bridge the gap between experimental neuroscience and data science. Key takeaways from this article are that (1) standardization with NWB requires non-trivial design choices; (2) the general practice of standardization in the lab promotes data awareness and literacy, and improves transparency, rigor, and reproducibility in our science; (3) we offer several feature suggestions to ease the extensibility, publishing/sharing, and usability for NWB standard and users of NWB data [ABSTRACT FROM AUTHOR]

AbstractSeveral advanced reactor designs are now under active development in the United States and elsewhere, promising sustainable solutions to the growing world energy needs. The designs currently being considered are quite diverse and different from the more established light water reactor technology that has dominated the operating commercial nuclear landscape. While advanced reactor concepts were first explored in the dawn of the nuclear age, they are now being reconsidered under the light of modern needs, and specifically, for their flexible operating conditions and inherent safety characteristics.In response, the U.S. Nuclear Regulatory Commission staff is moving forward with development of 10 CFR Part 53 rulemaking, which is a more risk-informed, technology-agnostic framework for licensing and regulating such new designs. The nuclear industry response to this regulatory initiative resulted in the technical report by the Nuclear Energy Institute, NEI 18-04 Revision 1, which provides an implementation roadmap of the risk-informed approach when defining the safety case for a new plant design. The implementation of this safety case may be a nontrivial exercise for an actual reactor design.This paper provides a demonstration of performing such an analysis for a representative advanced reactor. Public information from the General Atomics high-temperature gas reactor design was considered in this demonstration. The analysis workflow was facilitated with the FPoliSolutions’ proprietary Risk-Informed System Engineering (RISE) digital platform, a product that was presented in previous publications. RISE is one application of FPoli’s enterprise digital platform, which was created to facilitate orchestration of complex workflows leveraging recent technologies developed at national laboratories, such as Idaho National Laboratory’s RAVEN and EMRALD frameworks. The analysis described in the paper includes the selection and classifications of events, the integration of probabilistic risk analysis artifacts, and event modeling simulations for consequence evaluations.The results are then used for system, structures, and components safety classification and a synthesis of the safety case for the design in line with the frequency-consequence targets presented in NEI 18-04. The purpose of the analysis, as framed in RISE, is to readily produce outputs and views that can aid users and regulators in making risk-informed decisions to demonstrate their plant safety case. [ABSTRACT FROM AUTHOR]

The recent global outbreaks of infectious diseases such as COVID-19, yellow fever, and Ebola have highlighted the critical need for robust health data management systems that can rapidly adapt to and mitigate public health emergencies. In contrast to traditional systems, this study introduces an innovative blockchain-based Electronic Health Record (EHR) access control mechanism that effectively safeguards patient data integrity and privacy. The proposed approach uniquely integrates granular data access control mechanism within a blockchain framework, ensuring that patient data is only accessible to explicitly authorized users and thereby enhancing patient consent and privacy. This system addresses key challenges in healthcare data management, including preventing unauthorized access and overcoming the inefficiencies inherent in traditional access mechanisms. Since the latency is a sensitive factor in healthcare data management, the simulations of the proposed model reveal substantial improvements over existing benchmarks in terms of reduced computing overhead, increased throughput, minimized latency, and strengthened overall security. By demonstrating these advantages, the study contributes significantly to the evolution of health data management, offering a scalable, secure solution that prioritizes patient autonomy and privacy in an increasingly digital healthcare landscape. [ABSTRACT FROM AUTHOR]

Neuroscience studies entail the generation of massive collections of heterogeneous data (e.g. demographics, clinical records, medical images). Integration and analysis of such data in research centers is pivotal for elucidating disease mechanisms and improving clinical outcomes. However, data collection in clinics often relies on non-standardized methods, such as paper-based documentation. Moreover, diverse data types are collected in different departments hindering efficient data organization, secure sharing and compliance to the FAIR (Findable, Accessible, Interoperable, Reusable) principles. Henceforth, in this manuscript we present a specialized data management system designed to enhance research workflows in Deep Brain Stimulation (DBS), a state-of-the-art neurosurgical procedure employed to treat symptoms of movement and psychiatric disorders. The system leverages REDCap to promote accurate data capture in hospital settings and secure sharing with research institutes, Brain Imaging Data Structure (BIDS) as image storing standard and a DBS-specific SQLite database as comprehensive data store and unified interface to all data types. A self-developed Python tool automates the data flow between these three components, ensuring their full interoperability. The proposed framework has already been successfully employed for capturing and analyzing data of 107 patients from 2 medical institutions. It effectively addresses the challenges of managing, sharing and retrieving diverse data types, fostering advancements in data quality, organization, analysis, and collaboration among medical and research institutions. [ABSTRACT FROM AUTHOR]

The Military Internet of Things (MIoT) has emerged as a new research area in military intelligence. The MIoT frequently has to constitute a federation-capable IoT environment when the military needs to interact with other institutions and organizations or carry out joint missions as part of a coalition such as in NATO. One of the main challenges of deploying the MIoT in such an environment is to acquire, analyze, and merge vast amounts of data from many different IoT devices and disseminate them in a secure, reliable, and context-dependent manner. This challenge is one of the main challenges in a federated environment and forms the basis for establishing trusting relationships and secure communication between IoT devices belonging to different partners. In this work, we focus on the problem of fulfillment of the data-centric security paradigm, i.e., ensuring the secure management of data along the path from its origin to the recipients and implementing fine-grained access control mechanisms. This problem can be solved using innovative solutions such as applying attribute-based encryption (ABE). In this work, we present a comprehensive solution for secure data dissemination in a federated MIoT environment, enabling the use of distributed registry technology (Hyperledger Fabric), a message broker (Apache Kafka), and data processing microservices implemented using the Kafka Streams API library. We designed and implemented ABE cryptography data access control methods using a combination of pairings-based elliptic curve cryptography and lightweight cryptography and confirmed their suitability for the federations of military networks. Experimental studies indicate that the proposed cryptographic scheme is viable for the number of attributes typically assumed to be used in battlefield networks, offering a good trade-off between security and performance for modern cryptographic applications. [ABSTRACT FROM AUTHOR]

Based on the lining structure of an operational tunnel, the AHP and Fuzzy mathematical models were used to determine the weight of the evaluation index and solve the membership matrix. The weighted-average Fuzzy comprehensive function was used to combine the two, and the Fuzzy–AHP evaluation model was built and programmed. According to the self-developed Fuzzy–AHP evaluation-programmed model, a visualized structure safety evaluation system for operational tunnels was developed by using MATLAB. The system's functional design, program development, and computational visualized interface were implemented, and key codes were provided. The system can be divided into four modules: data management, fuzzy computation, predictive analysis and key disease indexes to focus on. In addition, the system can easily edit and modify the evaluation function, which includes not only the Fuzzy evaluation but also other types of evaluation functions applicable to other practical engineering projects, improving the applicability of the system. After that, the system was applied to the structure safety evaluation of a mountain tunnel, which provided the evaluation results and key indexes to focus on in the tunnel. Finally, the rationality of the system design was verified by constructing the corresponding BP–RBF combined neural network. This study provides a reference for the establishment of an intelligent structure safety warning system for operational tunnels. [ABSTRACT FROM AUTHOR]

Digitalization has been increasingly recognized for its role in addressing numerous societal and environmental challenges. However, the rapid surge in data production and the widespread adoption of cloud computing has resulted in an explosion of redundant, obsolete, and trivial (ROT) data within organizations' data estates. This issue adversely affects energy consumption and carbon footprint, leading to inefficiencies and a higher environmental impact. Thus, this opinion paper aims to discuss the challenges and potential solutions related to the environmental impact of file storage on the cloud, aiming to address the research gap in "digital sustainability" and the Green IT literature. The key findings reveal that technological issues dominate cloud computing and sustainability research. Key challenges in achieving sustainable practices include the widespread lack of awareness about the environmental impacts of digital activities, the complexity of implementing accurate carbon accounting systems compliant with existing regulatory frameworks, and the role of public–private partnerships in developing novel solutions in emerging areas such as 6G technology. [ABSTRACT FROM AUTHOR]

Modern bioimaging core facilities at research institutions are essential for managing and maintaining high‐end instruments, providing training and support for researchers in experimental design, image acquisition and data analysis. An important task for these facilities is the professional management of complex multidimensional bioimaging data, which are often produced in large quantity and very different file formats. This article details the process that led to successfully implementing the OME Remote Objects system (OMERO) for bioimage‐specific research data management (RDM) at the Core Facility Cellular Imaging (CFCI) at the Technische Universität Dresden (TU Dresden). Ensuring compliance with the FAIR (findable, accessible, interoperable, reusable) principles, we outline here the challenges that we faced in adapting data handling and storage to a new RDM system. These challenges included the introduction of a standardised group‐specific naming convention, metadata curation with tagging and Key–Value pairs, and integration of existing image processing workflows. By sharing our experiences, this article aims to provide insights and recommendations for both individual researchers and educational institutions intending to implement OMERO as a management system for bioimaging data. We showcase how tailored decisions and structured approaches lead to successful outcomes in RDM practices. [ABSTRACT FROM AUTHOR]

Data analytics (DA) use has been frequently considered a transformational practice in the public sector, particularly in terms of its potential for data-driven decision making by local governments. Despite growing interest from academics and practitioners, empirical research on what cities are actually doing regarding DA and how is still relatively scarce, particularly in terms of a focus on specific activities and processes. Based on a socio-technical view, this paper examines a local government’s experience with DA use and how DA can be seen as a process and transformational practice. Results reveal that DA practices go beyond the use of specific technologies and involve collaborative efforts to formulate meaningful problems and prepare data for specific uses. Indeed, problem formulation is an essential step where collective discussions and assessment by technical and nontechnical DA practitioners occurs. Our findings suggest that (1) data analytics viewed as a socio-technical process involves data management and analysis, but also collaborative processes between multiple city agencies, (2) collaborative efforts go beyond data analytics and include data collection, data representation, and problem formulation; and (3) formulating the problem in a collaborative manner could be considered an important first step when using data analytics for decision-making in local governments. Furthermore, collaborative problem formulation in DA seems to be affected by specific variables related to the collaborative effort such as leadership, governance, and technology. [ABSTRACT FROM AUTHOR]

Objective Methods Results Conclusions Causal analysis including causal inference and causal mediation is pivotal to inform effective interventions. In modern epidemilogy, causal analysis involves four key steps: formulating causal questions, employing directed acyclic graphs (DAGs), conducting data management and selecting statistical strategies. Our objective was to conduct a scoping review to assess how longitudinal observational studies (LOSs) in dental field have integrated these four steps to contribute leverage evidence that inform oral public health interventions.LOSs focusing on determinants of dental caries published from 2012 to 2024 were systematically retrieved from five major databases. The Joanna Briggs Institute‐scoping review guidance and the Covidence application were employed to identify eligible LOSs for being reviewed.Out of the 85 eligible LOSs, none formulated causal hypothesis by applying ‘what if’ question or investigated mediation across three levels of the determinants of oral health. A minority (18 studies, ~21.2%) employed DAGs to visualise relationships among study variables, while only one third (33 studies, ~39%) clearly defined confounders. The majority (64 studies, ~75%) incorporated a time‐varying feature of their data, yet only a few (11 studies) fully leveraged this advanced aspect. Among these studies that fully utilised time‐varying data, more than half encountered challenges in employing robust statistics to address confounders arising from such data dynamics.Dental LOSs have, to date, mostly focused on investigating associations over causality, often neglecting the four‐step causal analysis and not fully utilising time‐varying data. Researchers necessitate to shift their focus to causal inference and prioritise building capacity in causal analysis with a consistent four‐step approach to advance the field. Studies exploring mechanisms linking determinants of dental caries across levels and leveraging time‐varying data are strongly encouraged. [ABSTRACT FROM AUTHOR]

This study examines consortia academic research data repositories, and their curators, across five global regions. Using an ad-hoc sample, we explore the roles of curators in research data repositories and their perceptions of skills and training, Research Data Management (RDM) practices, and the limitations, strengths, and opportunities they encounter in doing their work. Furthermore, our study analyses the roles of academic research repositories and the degree to which the FAIR principles are embedded in their strategic approaches. Our findings indicate that although numerous repositories are managed through collaborations among higher education bodies and research organisations, in certain nations, the development of research data repositories is in its early stage, often led by a single entity and having limited representation. Additionally, curators face challenges when dealing with the submission of datasets by researchers, who often provide insufficiently clear information for data reuse. We argue that despite the efforts to align data repositories with FAIR principles, substantial progress is required in educating researchers on their effective utilisation. [ABSTRACT FROM AUTHOR]

In light of current risks and environmental impacts, HBIM (historic building information modeling) offers a highly efficient and interactive method for managing historical data and representing the current states of ancient clay structures. In this study, traditional geodetic techniques were employed to digitally locate a structure without compromising its topographic information to create an accurate model. Tools such as total stations, GNSS receivers, and UAVs were utilized to generate detailed topography of the study site and its surroundings. An ontology-based data management structure was also developed to store historical data and site intervention projects, adhering to the ISO 12006-2 standard. This was achieved through automated scripts in Dynamo softwarev.2.18.1. A comparison between the point cloud (279 images) and total station data (600 points) revealed a georeferencing accuracy difference of +/−0.003 m. Consequently, the developed methods can effectively represent similar structures digitally. The proposed ontological structure facilitates automated storage of internal and external information. [ABSTRACT FROM AUTHOR]

Biobanking plays a pivotal role in biomedical research by providing standardized processing, precise storing, and management of biological sample collections along with the associated data. Effective data management is a prerequisite to ensure the integrity, quality, and accessibility of these resources. This review provides a current landscape of data management in biobanking, discussing key challenges, existing strategies, and potential future directions. We explore multiple aspects of data management, including data collection, storage, curation, sharing, and ethical considerations. By examining the evolving technologies and methodologies in biobanking, we aim to provide insights into addressing the complexities and maximizing the utility of biobank data for research and clinical applications. [ABSTRACT FROM AUTHOR]

The high-energy-density (HED) physics community is moving toward a new paradigm of high-repetition-rate (HRR) operation. To fully leverage the scientific power of HRR HED facilities, all of the components of each subsystem (laser, targetry, and performance diagnostics) must be connected and synchronized in a reliable and robust manner while the data acquired are tagged and archived in real time. To this end, GA has begun developing a generalized NoSQL-database framework, the MongoDB repository for information and archiving. An organizational strategy has been developed that shifts HED data organization from a shot-based to a diagnostic-based approach in order to increase archival and retrieval efficiency that lends itself to optimization applications. This work is a first step in pushing HRR HED science toward data management solutions that emphasize machine actionability and aim to stimulate community engagement to define data standards in HED science. [ABSTRACT FROM AUTHOR]

Ontologies are essential for the management and integration of heterogeneous datasets. This paper presents OntoBuilder, an advanced tool that leverages the structural capabilities of semantic labeled property graphs (SLPGs) in strict alignment with semantic web standards to create a sophisticated framework for data management. We detail OntoBuilder's architecture, core functionalities, and application scenarios, demonstrating its proficiency and adaptability in addressing complex ontological challenges. Our empirical assessment highlights OntoBuilder's strengths in enabling seamless visualization, automated ontology generation, and robust semantic integration, thereby significantly enhancing user workflows and data management capabilities. The performance of the linked data tools across multiple metrics further underscores the effectiveness of OntoBuilder. [ABSTRACT FROM AUTHOR]

U-Pb geochronology by isotope dilution– thermal ionization mass spectrometry (ID-TIMS) has the potential to be the most precise and accurate of the deep time chronometers, especially when applied to high-U minerals such as zircon. Continued analytical improvements have made this technique capable of regularly achieving better than 0.1% precision and accuracy of dates from commonly occurring high-U minerals across a wide range of geological ages and settings. To help maximize the long-term utility of published results, we present and discuss some recommendations for reporting ID-TIMS U-Pb geochronological data and associated metadata in accordance with accepted principles of data management. Further, given that the accuracy of reported ages typically depends on the interpretation applied to a set of individual dates, we discuss strategies for data interpretation. We anticipate that this paper will serve as an instructive guide for geologists who are publishing ID-TIMS U-Pb data, for laboratories generating the data, the wider geoscience community who use such data, and also editors of journals who wish to be informed about community standards. Combined, our recommendations should increase the utility, veracity, versatility, and “half-life” of ID-TIMS U-Pb geochronological data. [ABSTRACT FROM AUTHOR]

The Alaska Volcano Observatory (AVO) uses multidisciplinary data to monitor and study dozens of active and potentially active volcanoes. Here, we provide an overview of internally and externally generated data types, tools and resources used in their management, and challenges faced. Data sources include the following: (1) a multiparameter (seismic, infrasound, GNSS, web cameras) ground-based monitoring network that spans 3000 km and transmits data in real time; (2) a variety of satellite-borne sensors that provide information about surface change and volcanic emissions; (3) geologic and gas field campaigns; and (4) other external data products that provide situation awareness. Each data type requires distinct acquisition, processing, storage, visualization, and archiving approaches. AVO uses a variety of externally and internally developed tools to handle individual data types as well as multidisciplinary volcanological data. A primary tool is the Geologic Database of Information on Volcanoes in Alaska (GeoDIVA), which stores detailed, searchable information on more than 140 volcanoes and over 1000 eruptions and unrest events, including images, eruption descriptions, and geologic station and sample data, metadata, and analyses. It interacts with other internal tools that store monitoring reports and other operational records. Additional data management resources used by AVO assist with alarms and alerts, state-of-health monitoring, and multiparameter visualization. Requirements for 24/7 accessibility, the ever-expanding portfolio of data, and transitioning new tools from development to operations are all challenges faced by AVO and other volcano observatories. AVO strives to meet FAIR data practices and ensure that data are available to national and international community efforts using external repositories as well as those hosted by AVO and its parent institutions. [ABSTRACT FROM AUTHOR]

For populating Scientific Knowledge Graphs (SciKGs), research publications pose a central information source. However, typical forms of research publications like traditional papers do not provide means of integrating contributions into SciKGs. Furthermore, they do not support making direct use of the rich information SciKGs provide. To tackle this, the present paper proposes RDFtex, a framework enabling (1) the import of contributions represented in SciKGs to facilitate the preparation of -based research publications and (2) the export of original contributions from papers to facilitate their integration into SciKGs. The framework's functionality is demonstrated using the present paper itself since it was prepared with our proof-of-concept implementation of RDFtex. The runtime of the implementation's preprocessor was evaluated based on three projects with different numbers of imports and exports. A small user study ( N = 10 ) was conducted to obtain initial user feedback. The concept and the process of preparing a -based research publication using RDFtex are discussed thoroughly. RDFtex's import functionality takes considerably more time than its export functionality. Nevertheless, the entire preprocessing takes only a fraction of the time required to compile the PDF. The users were able to solve all predefined tasks but preferred the import functionality over the export functionality because of its general simplicity. RDFtex is a promising approach to facilitate the move toward knowledge graph augmented research since it only introduces minor differences compared to the preparation of traditional -based publications while narrowing the gap between papers and SciKGs. [ABSTRACT FROM AUTHOR]

Background: This article reports on a study that investigated data professionals in health care. The topic is interesting and relevant because of the ongoing trend towards digitisation of the healthcare domain and efforts for it to become data driven, which entail a wide variety of work with data. Objective: Despite an interest in data science and more broadly in data work, we know surprisingly little about the people who work with data in healthcare. Therefore, we investigated data work at a large national healthcare data organisation in Denmark. Method: An explorative mixed method approach combining a non-probability technique for design of an open survey with a target population of 300+ and 11 semi-structured interviews, was applied. Results: We report findings relevant to educational background, work identity, work tasks, and how staff acquired competences and knowledge, as well as what these attributes comprised. We found recurring themes of healthcare knowledge, data analytical skills, and information technology, reflected in education, competences and knowledge. However, there was considerable variation within and beyond those themes, and indeed most competences were learned "on the job" rather than as part of formal education. Conclusion: Becoming a professional working with data in health care can be the result of different career paths. The most recurring work identity was that of "data analyst"; however, a wide variety of responses indicated that a stable data worker identity has not yet developed. Implications: The findings present implications for educational policy makers and healthcare managers. [ABSTRACT FROM AUTHOR]

Decarbonizing the mobility and heating sector involves increasing connected components in low-voltage grids. The simulation of distribution grids and the incorporation of an energy system are relevant instruments for evaluating the effects of these developments. However, grids are highly diversified, and with over 900,000 low-voltage grids in Germany, the simulation would require significant data management and computing capacity. A solution already applied in the literature is the simulation of representative grids. Here, we show the compatibility of clusters and representatives for grid topologies from the literature and further extend and validate them by applying accurate grid data. Our analysis indicates that clusters from the literature unify well across three key parameters but also reveals that the clusters still exclude a relevant amount of grids. Extension, reclassification, and validation using about 1200 real grids establish meta-clusters covering the spectrum of grids from rural to urban regions, focusing on residential to commercial supply tasks. We anticipate our assay to be a further relevant step toward typifying low-voltage distribution grids in Germany. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) and machine learning (ML) have transformed the landscape of data management in higher education institutions, necessitating a critical evaluation of existing data privacy policies and practices. This research delves into the inadequacies of current frameworks in adapting to the swift evolution of Big Data. Student, faculty, and staff perspectives on data privacy are examined in terms of how their viewpoints influence university policies. Data privacy incidents at several universities are discussed to identify patterns and extract lessons learned. Recommendations and best practices for enhancing data privacy in the context of AI/ML implementation are discussed, with an emphasis on the need for policy reform and improved protocols to safeguard student privacy effectively. The integration of AI/ML in higher education must align with robust data privacy standards, enabling institutions to optimize teaching, learning, and administrative processes while safeguarding sensitive student information. Practical Takeaways: AI/ML advancements necessitate a critical review of data privacy policies in higher education to adapt to Big Data evolution.The perspectives of students, faculty, and staff are crucial in shaping effective data privacy policies.Analysis of data privacy incidents offers insights for policy reform and implementation of best practices.Recommendations emphasize the importance of policy reform and robust protocols to protect student privacy amidst AI/ML integration, ensuring effective teaching, learning, and administrative processes. [ABSTRACT FROM AUTHOR]

The process of locating and identifying missing persons presents a complex challenge that hinges on the collection and comparison of diverse data sets. This commentary offers an overview of some of the difficulties and considerations associated with data management in the context of large‐scale missing person identifications. Such complexities include the uniqueness of each disaster event, the response time to the event, the variable quality and quantity of data, and the involvement of numerous stakeholders, all of which contribute to the intricacies of data management. In addition, the paramount considerations of privacy and ethical standards further compound these challenges, especially when dealing with sensitive information such as genetic data. This commentary describes the integrated Data Management System (iDMS) developed by the International Commission on Missing Persons (ICMP) as one example of a comprehensive, freely available solution for data collection, storage, protection, and analysis in missing person cases. The various advantages of the system are discussed, including the system's interoperability among the diverse array of stakeholders involved. While the iDMS streamlines data management processes and therefore represents a significant advancement in the field of missing person identification, it is concluded that the pending issue extends beyond the software tools to encompass the lack of political will among stakeholders to collaborate there remains a pressing need for all stakeholders involved in the identification process to commit to a mechanism that facilitates compatibility and interoperability if different tools are used in disaster victim identification (DVI) scenarios. [ABSTRACT FROM AUTHOR]

StreamSAXS is a Python‐based small‐ and wide‐angle X‐ray scattering (SAXS/WAXS) data analysis workflow platform with graphical user interface (GUI). It aims to provide an interactive and user‐friendly tool for analysis of both batch data files and real‐time data streams. Users can easily create customizable workflows through the GUI to meet their specific needs. One characteristic of StreamSAXS is its plug‐in framework, which enables developers to extend the built‐in workflow tasks. Another feature is the support for both already acquired and real‐time data sources, allowing StreamSAXS to function as an offline analysis platform or be integrated into large‐scale acquisition systems for end‐to‐end data management. This paper presents the core design of StreamSAXS and provides user cases demonstrating its utilization for SAXS/WAXS data analysis in offline and online scenarios. [ABSTRACT FROM AUTHOR]

Blockchain technology ensures the integrity and transparency of data access control by keeping an unchangeable record of all transactions. Fog computing increases efficiency by dispersing cloud computing skills throughout the network's superiority and reducing latency in data access and authentication processes. The objective of this study is to develop a blockchain and fog computing model that implements secure data access control mechanisms for distributed data storage and authentication using a hybrid encryption algorithm. The model aims to provide secure and reliable data storage by leveraging the decentralized nature of blockchain and the proximity of fog computing to the data source. Additionally, the Hybrid Encryption Algorithm (HEA) enhances data security by combining multiple encryption techniques for robust protection against unauthorized access. This study proposes a novel blockchain and also the Fog Computing Model (FCM) that ensures secure data access control, distributed data storage, and authentication. The model utilizes a HEA and is based on the Deep Adaptive Power Probabilistic Clustering Algorithm for optimal cluster head selection. Probability clustering algorithms are employed to determine the prospect of attracting a cluster head. Elliptic Curve Diffie-Hellman (ECDH) and Secure Hashing Algorithm 512 (SHA-512) are utilized for secure key exchange and hashing, respectively. Additional features include bit addition, long message redemption value, initialization of hash value, and the integration of Ethereum Smart Contracts (SC) for trusted distributed cross-domain operations. The study investigates the performance of the proposal for cloud-fog computing networks via simulation analysis using Matlab software. This model aims to provide a comprehensive and robust framework for secure and efficient data management in distributed environments. In this context, reliability pertains to the level of assurance and trustworthiness of the data access control mechanisms and authentication processes integrated into the model. The proposed method demonstrates a high-reliability score of 95%, indicating its capability to provide secure and dependable data access and authentication procedures. This model offers a safeguarding distributed data storage, controlling access, and ensuring authentication using a combination of blockchain, fog computing, and HEAs respectively. [ABSTRACT FROM AUTHOR]

The construction project information for FM team control and maintenance is vital. However, current practices indicate significant data drops during the handover stage, leading to rework during FM systems integration of inaccurate, delivered information. Although challenges in BIM implementation are well studied, there is a current lack of best practices from FM industry experts highlighting challenges with a specific focus on the areas of the data drop. Therefore, the aim is to develop a framework that can reduce data loss and improve data management. The objectives include robust literature review on challenges of information loss during handover phase, management and frameworks; Adopting case study and workshops to identify information loss challenges, framework development and validation with industry experts. This paper adopted a grounded theory approach to explore literature and conducted case studies, including a series of semistructured interviews and a workshop with FM industry experts. The findings show the areas and causes of information loss and how to manage them through the developed information loss management framework (ILMF). Practical Applications: With the start of any type of project, planning and establishing rules from the beginning facilitates the smooth transition between different phases. Similarly, in construction projects, this research provides guidelines on how facility managers can manage information loss when exchanged through projects' stages. Furthermore, with the continuing advancements in the construction world, the facility management should be equipped with management and leadership guidelines as presented in the proposed framework to reduce data loss during handover. However, there are cost and contractual issues that prohibit any involvement of the facility manager in early project stages. The results of this study provide an identification of professional practices do's and do not's that facilitate the management and development of projects with the support of the latest technologies in the market. Ultimately, developing a guide that adapts international standards. The study's contribution to knowledge includes the development of a new information loss management framework that adopts BIM and ISO standards after exploring the existing data management frameworks in the literature that lack data loss management as a focus. The framework works as a guide for various stakeholders such as designers, contractors with a focus on FM to facilitates data coordination and management to reduce information loss, its creation and validation were conducted in collaboration with industry experts. Furthermore, its novelty lies in integrating FM role, information management, information loss, address project life cycle, and adopts the ISO 19650 series. [ABSTRACT FROM AUTHOR]

Learning from prior incidents is crucial for improving safety, particularly in the construction industry where fatalities and injuries are frequent. High-precision classification of construction accident narratives is a laborious, time-consuming process that requires substantial domain expertise. However, automatic text classification had fallen short of expectations due to a lack of high-quality data sets, inadequate semantic interpretation, and primitive model architecture. To address these issues, this study developed a state-of-the-art text classification (TC) model to extract construction knowledge and classify construction accident narratives into predefined categories. The architecture of the TC deep-learning model was built based on the pretrained instruction-based omnifarious representations (INSTRUCTOR). A class-imbalance treatment (CIT) technique incorporating focal loss and weighted random sampling was embedded to make the model concentrate on hard samples and minority classes. The retrained and fine-tuned INSTRUCTOR-CIT model achieved an F1 score of 82.22% for the benchmark data set containing 1,000 accident narratives from the Occupational Health and Safety Administration (OSHA). Impressively, on a larger benchmark data set of 4,770 OSHA accident narratives labeled by another official system, the model achieved an F1 score of 94.84%, highlighting its generality. Furthermore, the experimental results demonstrated that our model was superior to existing methods with less preprocessing and higher accuracy. Finally, the contribution to construction project management was discussed to enhance unstructured data management in the construction industry. The findings of this study contribute to effective management practices and assist construction professionals focus on value-added tasks such as decision making and corrective action planning. [ABSTRACT FROM AUTHOR]

Data sharing is increasingly an expectation in health research as part of a general move toward more open sciences. In the United States, in particular, the implementation of the 2023 National Institutes of Health Data Management and Sharing Policy has made it clear that qualitative studies are not exempt from this data sharing requirement. Recognizing this trend, the Palliative Care Research Cooperative Group (PCRC) realized the value of creating a de-identified qualitative data repository to complement its existing de-identified quantitative data repository. The PCRC Data Informatics and Statistics Core leadership partnered with the Qualitative Data Repository (QDR) to establish the first serious illness and palliative care qualitative data repository in the U.S. We describe the processes used to develop this repository, called the PCRC-QDR, as well as our outreach and education among the palliative care researcher community, which led to the first ten projects to share the data in the new repository. Specifically, we discuss how we co-designed the PCRC-QDR and created tailored guidelines for depositing and sharing qualitative data depending on the original research context, establishing uniform expectations for key components of relevant documentation, and the use of suitable access controls for sensitive data. We also describe how PCRC was able to leverage its existing community to recruit and guide early depositors and outline lessons learned in evaluating the experience. This work advances the establishment of best practices in qualitative data sharing. [ABSTRACT FROM AUTHOR]

The hepatitis C virus (HCV) continues to pose a significant public health challenge in Iran, mirroring a worldwide concern. This situation calls for a cohesive strategy that aligns with the World Health Organization's (WHO) goals for HCV elimination by 2030. Central to this strategy is targeting high‐risk groups, notably people who inject drugs and prisoners, with prevention, screening and treatment. The deployment of point‐of‐care testing and treatments in prisons and harm reduction facilities is vital. The adoption of cost‐effective generic direct‐acting antivirals represents a major step forward. Furthermore, innovative educational initiatives for healthcare providers and awareness campaigns for the public are critical. Additionally, tackling stigma, ensuring treatment affordability and upholding strict surveillance and data management, coupled with ongoing policy reviews, are vital components. This comprehensive and integrated approach is designed to drive Iran towards eliminating HCV and can serve as a blueprint for other countries with similar challenges. [ABSTRACT FROM AUTHOR]

This study aimed to find out how public sector organizations in Tanzania, through the Ministry of Lands, Housing, and Human Settlements Development, can manage data using the characteristics of enterprise resource planning systems, such as perceived ease of use, relative advantage, compatibility, observability, and trialability. The study discovered that only two of the five ERP characteristics are applicable through using structural equation modeling. These are relative advantages and observability. To better achieve its goals, the ministry should make sure that methodological approaches are thoroughly examined before implementation. [ABSTRACT FROM AUTHOR]

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