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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]

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]

Smart manufacturing is a popular concept for smarter decision-making and more efficient production. Although distributed methods for data management and processing in smart manufacturing have many advantages such as low cost of adaptation and convenience for local database, some methods are hard to manage variable data sources and discover proper range of data for smart decision-making. Therefore, Dataspace is considered in this article to be a feasible and effective method. From the relation-defined perspective of utilisation of industrial Big Data, the contribution is a novel industrial Dataspace design with static structure and working flow paths for smart manufacturing. In design, the industrial Dataspace platform has been proposed to accommodate smart manufacturing characteristics with the intelligence of pay-as-you-go, like harnessing distributed heterogenous data from industrial enterprises, understanding industrial data by ontology or knowledge, corelating the data with smart applications, and enabling related decisions. A further analytical case in Surface Mounting Technology manufacturing of welding procedure is provided to illustrate the execution of customisation, focused and related decision support, and system evolution within industrial Dataspace. [ABSTRACT FROM AUTHOR]

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

Physical and cadastral data management practices for underground facilities are insufficient to represent underground assets' Rights, Restrictions, and Responsibilities (RRRs) and their relationships with the cadastre. For this reason, developing a 3D integrated data (underground and above‐ground) model may be an approach to an effective land management system. This research aims to develop a creative data model by integrating Turkish National Geographic Information System (TUCBS‐AY in Turkish) with international standards (Land Administration Domain Model (LADM) and CityGML) as a holistic approach. The requirements determined for underground facilities using TUCBS‐AY were used. Since the data produced in Turkey were modeled according to the CityGML 2.0 version, the CityGML 2.0 UtilityNetwork version was used in the study to be compatible with the existing Turkish cadastral system. While some classes in the model are generalized, they are expanded using LADM's classes for legal rights and part information. In addition, the CityGML classes have been expanded within the model so that parcels, buildings, and independent sections are represented separately, thus providing the opportunity to represent the underground utilities associated with them, their RRRs, and ownership situations. Requirements for the proposed model were determined based on Turkey's cadastral system and underground data. The main purpose is to manage the legal and physical relations of underground and surface objects at the city scale, compatible with the current Turkish cadastral system, with a holistic approach. The proposed integrated conceptual data model shows how legal rights and ownership of underground structures can be logically represented with a 3D data model. [ABSTRACT FROM AUTHOR]

This report describes a comprehensive framework for applying artificial intelligence (AI) in veterinary medicine. Our framework draws on existing research on AI implementation in human medicine and addresses the challenges of limited technology expertise and the need for scalability. The critical components of this framework include assembling a diverse team of experts in AI, promoting a foundational understanding of AI among veterinary professionals, identifying relevant use cases and objectives, ensuring data quality and availability, creating an effective implementation plan, providing team training, fostering collaboration, considering ethical and legal obligations, integrating AI into existing workflows, monitoring and evaluating performance, managing change effectively, and staying up-to-date with technological advancements. Incorporating AI into veterinary medicine requires addressing unique ethical and legal considerations, including data privacy, owner consent, and the impact of AI outputs on decision-making. Effective change management principles aid in avoiding disruptions and building trust in AI technology. Furthermore, continuous evaluation of AI's relevance in veterinary practice ensures that the benefits of AI translate into meaningful improvements in patient care. [ABSTRACT FROM AUTHOR]

Nowadays, both process data management and numerical analysis are necessary for decision‐making in a tunnel project from its early design stage to the construction phase. Traditional generation of simulation models largely depends on tunnel design and reports, which are error prone due to the involvement of manual intervention. This article presents the application of a digital workflow to efficiently transfer automatically acquired data from tunnel projects to computational simulations. The application workflow is developed and executed through a web‐based environment. Project data from the Victory Boogie Woogie tunnel is retrieved from the process controlling software PROCON via web services to generate numerical simulations for settlement predictions at different tunnel sections. The simulated results are validated using real measured settlements in the project to prove the quality of the simulation model. Different scenarios of applying operational steering parameters are then investigated, utilising the simulation model, which can be used to support the on‐site engineers as an assistance system to drive the tunnel boring machines. The work has been carried out within the framework of the transfer project T2 of the Collaborative Research Center "Interaction Modeling in Mechanized Tunneling" (SFB 837), which aims to implement and test a digital twin for process control under practical conditions in the tunnelling industry. [ABSTRACT FROM AUTHOR]

Simple Summary: Large datasets concerning childhood cancers are rare. Therefore, it is important to fully exploit all available data, which are distributed over several resources, including biomaterials, images, clinical trials, and registries. With privacy-preserving record linkage (PPRL), datasets can be merged, without disclosing the patients' identities. Although PPRL is already implemented or described in various settings, use case descriptions are fragmented and incomplete. The present paper gives an overview of current and future use cases of PPRL in childhood cancer. We screened the literature, projects, and trial protocols, analysed a hypothetical patient journey, and discussed use cases with experts. All the identified use cases were structured along six key dimensions. We conclude that PPRL is a key concept in childhood cancer. Therefore, PPRL strategies should already be considered when starting research projects, to avoid distributed data silos, to maximise the knowledge derived from collected data, and, ultimately, to improve outcomes for children with cancer. Large datasets in paediatric oncology are inherently rare. Therefore, it is paramount to fully exploit all available data, which are distributed over several resources, including biomaterials, images, clinical trials, and registries. With privacy-preserving record linkage (PPRL), personalised or pseudonymised datasets can be merged, without disclosing the patients' identities. Although PPRL is implemented in various settings, use case descriptions are currently fragmented and incomplete. The present paper provides a comprehensive overview of current and future use cases for PPRL in paediatric oncology. We analysed the literature, projects, and trial protocols, identified use cases along a hypothetical patient journey, and discussed use cases with paediatric oncology experts. To structure PPRL use cases, we defined six key dimensions: distributed personalised records, pseudonymisation, distributed pseudonymised records, record linkage, linked data, and data analysis. Selected use cases were described (a) per dimension and (b) on a multi-dimensional level. While focusing on paediatric oncology, most aspects are also applicable to other (particularly rare) diseases. We conclude that PPRL is a key concept in paediatric oncology. Therefore, PPRL strategies should already be considered when starting research projects, to avoid distributed data silos, to maximise the knowledge derived from collected data, and, ultimately, to improve outcomes for children with cancer. [ABSTRACT FROM AUTHOR]

Protein crystallography is an established method to study the atomic structures of macromolecules and their complexes. A prerequisite for successful structure determination is diffraction‐quality crystals, which may require extensive optimization of both the protein and the conditions, and hence projects can stretch over an extended period, with multiple users being involved. The workflow from crystallization and crystal treatment to deposition and publication is well defined, and therefore an electronic laboratory information management system (LIMS) is well suited to management of the data. Completion of the project requires key information on all the steps being available and this information should also be made available according to the FAIR principles. As crystallized samples are typically shipped between facilities, a key feature to be captured in the LIMS is the exchange of metadata between the crystallization facility of the home laboratory and, for example, synchrotron facilities. On completion, structures are deposited in the Protein Data Bank (PDB) and the LIMS can include the PDB code in its database, completing the chain of custody from crystallization to structure deposition and publication. A LIMS designed for macromolecular crystallography, IceBear, is available as a standalone installation and as a hosted service, and the implementation of key features for the capture of metadata in IceBear is discussed as an example. [ABSTRACT FROM AUTHOR]

Context-aware data management becomes the focus of several research efforts, which can be placed at the intersection between the Internet of Things (IoT) and Edge Computing (EC). Huge volumes of data captured by IoT devices are processed in EC environments. Even if edge nodes undertake the responsibility of data management tasks, they are characterized by limited storage and computational resources compared to Cloud. Apparently, this mobilises the introduction of intelligent data selection methods capable of deciding which of the collected data should be kept locally based on end users/applications requests. In this paper, we devise a mechanism where edge nodes learn their own data selection filters, and decide the distributed allocation of newly collected data to their peers and/or Cloud once these data are not conformed with the local data filters. Our mechanism intents to postpone final decisions on data transfer to Cloud (e.g., data centers) to pervasively keep relevant data as close and as long to end users/applications as possible. The proposed mechanism derives a data-selection map across edge nodes by learning specific data sub-spaces, which facilitate the placement of processing tasks (e.g., analytics queries). This is very critical when we target to support near real time decision making and would like to minimize all parts of the tasks allocation procedure. We evaluate and compare our approach against baselines and schemes found in the literature showcasing its applicability in pervasive edge computing environments. [ABSTRACT FROM AUTHOR]

In the current landscape of medical data management, processing data across diverse institutions and maximizing their value are paramount. However, traditional methods lack a secure and efficient mechanism for end-to-end traceability and supervision, posing challenges in distributed scenarios lacking mutual trust. Leveraging blockchain's decentralized, tamper-proof, and traceable features, this paper introduces a blockchain-based medical data management platform. This platform enables full-process management of raw data, operational behaviors, intermediate data, and final data, meeting the needs of trusted storage and supervision of data. We propose two methods, namely, naive method and DAG-based method, to realize forward tracking and backward tracing of medical data stored on the blockchain, respectively. We validated and analyzed the storage and query performance of the medical data management platform on real medical data, and we also conducted experimental analyses on the efficiency of the proposed traceability algorithm under different data scales and processing path lengths. The results demonstrate that our platform and traceability methods effectively meet the management needs of medical data distributed across institutions. [ABSTRACT FROM AUTHOR]

Copyright of HMD: Praxis der Wirtschaftsinformatik is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Pharmacists play a crucial role in medication safety and quality improvement in healthcare, yet face significant challenges due to limited data access, poor data quality, and insufficient data management skills. National pharmacy organizations emphasize the importance of pharmacists using data effectively to improve patient care. This paper suggests several strategies to overcome data challenges, including more widespread adoption of a Chief Pharmacy Informatics Officer (CPIO) role in healthcare institutions, the development and validation of medication safety metrics and dashboards, and the incorporation of data analysis and application skills into pharmacist education and training. The creation of the CPIO position is proposed to lead efforts in data management and use for medication safety. Additionally, establishing clear medication safety metrics and dashboards is recommended to monitor and improve safety practices. To support these roles and tools, enhancing pharmacist training in data analytics is deemed essential. Implementing these strategies aims to empower pharmacists to engage more effectively in proactive medication safety and quality improvement efforts. This approach is expected to address current gaps in data utilization and management, facilitating a more informed method for pharmacists to make healthcare safe for patients. [ABSTRACT FROM AUTHOR]

Copyright of ABI Technik is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Teachers form expectations that can influence their students' performance, and there are a variety of ways these expectations can be communicated. In the current study, we tested a novel method for communicating expectations via examples of student work—examples that contain basic, entry-level work and communicate low, but manageable expectations or examples that contain complex, advanced-level work and communicate high and challenging expectations. Across three semesters, 91 college students in a data management course completed a class assignment that involved exploratory coding activities. Prior to the assignment, students were randomly assigned to view basic or advanced examples of student work. Students assigned to the advanced-examples condition reported higher perceptions of task difficulty and frustration, but they also exhibited higher levels of performance in terms of the complexity of their own work. Results suggest that setting challenging expectations can create a desirable difficulty that ultimately benefits students' performance in an authentic learning environment. [ABSTRACT FROM AUTHOR]

Good record keeping practice and research data management underlie responsible research conduct and promote reproducibility of research findings in the sciences. In many cases of research misconduct, inadequate research data management frequently appear as an accompanying finding. Findings of disorganized or otherwise poor data archival or loss of research data are, on their own, not usually considered as indicative of research misconduct. Focusing on the availability of raw/primary data and the replicability of research based on these, we posit that most, if not all, instances of research data mismanagement (RDMM) could be considered a questionable research practice (QRP). Furthermore, instances of RDMM at their worst could indeed be viewed as acts of research misconduct. Here, we analyze with postulated scenarios the contexts and circumstances under which RDMM could be viewed as a significant misrepresentation of research (ie. falsification), or data fabrication. We further explore how RDMM might potentially be adjudicated as research misconduct based on intent and consequences. Defining how RDMM could constitute QRP or research misconduct would aid the formulation of relevant institutional research integrity policies to mitigate undesirable events stemming from RDMM. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is emerging as a transformative technology in healthcare, including endodontics. A gap in knowledge exists in understanding AI's applications and limitations among endodontic experts. This comprehensive review aims to (A) elaborate on technical and ethical aspects of using data to implement AI models in endodontics; (B) elaborate on evaluation metrics; (C) review the current applications of AI in endodontics; and (D) review the limitations and barriers to real‐world implementation of AI in the field of endodontics and its future potentials/directions. The article shows that AI techniques have been applied in endodontics for critical tasks such as detection of radiolucent lesions, analysis of root canal morphology, prediction of treatment outcome and post‐operative pain and more. Deep learning models like convolutional neural networks demonstrate high accuracy in these applications. However, challenges remain regarding model interpretability, generalizability, and adoption into clinical practice. When thoughtfully implemented, AI has great potential to aid with diagnostics, treatment planning, clinical interventions, and education in the field of endodontics. However, concerted efforts are still needed to address limitations and to facilitate integration into clinical workflows. [ABSTRACT FROM AUTHOR]

Due to the increasing importance of exposome in environmental epidemiology, feasibility and usefulness of an Environmental Data Management System (EDMS) using Open Data was evaluated. The EDMS includes data from 10 European cities (Celje (Slovenia), Łódź (Poland), Manchester (UK), Palermo (Italy), Paris (France), Porto (Portugal), Regensburg (Germany), Reus (Spain), Rijeka (Croatia), Thessaloniki (Greece)) about external non-specific and specific exposome factors at the city or country level (2017–2020). Findings showed that the highest values of life expectancy were in Reus females (86 years) and Palermo males (81 years). UK had the highest obesity rate (28%), Croatia the highest prescribed drug consumption (62%), Greece and Portugal the highest smoking rates (37%, 42%) and daily alcohol consumption (21%), respectively. The most polluted cities were Thessaloniki for PM10 (38 µg/m3), Łódź for PM2.5 (25 µg/m3), Porto for NO2 (62 µg/m3) and Rijeka for O3 (92 µg/m3). Thessaloniki had the highest grey space (98%) and Łódź the highest cumulative amount of pollen (39,041 p/m3). The highest daily noise levels ≥ 55 dB was in Reus (81% to traffic) and Regensburg (21% to railway). In drinking water, arsenic had the highest value in Thessaloniki (6.4 µg/L), boron in Celje (24 mg/L) and lead in Paris (46.7 µg/L). Portugal and Greece showed the highest pesticide residues in food (7%). In conclusion, utilizing open-access databases enables the translation of research findings into actionable strategies for public health interventions. [ABSTRACT FROM AUTHOR]

In a dynamic business environment, the convergence of Digital Transformation and Sustainable Transformation has emerged as a comprehensive strategy for companies seeking to thrive in the modern era. For this transformation to successfully develop, the appropriate conditions must be present to carry out a process of digitisation and sustainability. Therefore, this research aimed to identify the readiness level of companies established in the city of Tijuana, Mexico, to adopt a dual transformation process. An analysis of the different digital and sustainable transformation models was carried out, and a self-diagnosis model was designed. Then, a quantitative instrument was applied to a sample of 148 small, medium and large companies in Tijuana between 2022 and mid-2023. The analysis consisted of a descriptive analysis of the data and a correlation analysis. It concludes that the elements that most explain the dual transformation are strategy, culture, organisational and technical capabilities. Likewise, the related factors are definition and communication, alignment, and scope of the objectives within the strategy; change management, knowledge and collaboration in the culture; within the organisation: leadership, processes and organisational structure; and finally in the technical capabilities data management, training and systems integration. The results of this research allowed us to identify and propose strategies to implement the dual transformation and increase the scalability and competitiveness of the adopting companies. [ABSTRACT FROM AUTHOR]

Although the copyright protection schemes supported by blockchain have significantly changed traditional copyright data management, there are still some data security challenges that cannot be ignored, especially the secure access and controllable management of copyright data. Quantum computing attacks also pose a threat to its security. Targeting these issues, we design and propose a blockchain copyright protection scheme based on attribute-based encryption (ABE). In this scheme, the security advantages of blockchain technology are utilized to ensure the authenticity and integrity of copyright data. Based on lattice cryptography and the decision ring learning with errors (R-LWE) problem, a new ABE algorithm that supports searchable ciphertext and policy updates is designed. Then, we introduce it into the blockchain copyright protection scheme, which enables secure access to copyright data and fine-grained control. In addition, the lattice cryptography can strengthen this scheme against quantum attacks. Through security analysis, our scheme can prove to be secure against adaptive chosen keyword attacks, selective chosen plaintext attacks, and adaptive chosen policy attacks in the random oracle model. More importantly, the comparison analysis and experimental results show that our proposed approach has lower computation costs and storage costs. Therefore, our scheme has better security and performance in copyright protection. [ABSTRACT FROM AUTHOR]

As a typical information system, a cloud file system enables the storage, retrieval, and management of data on remote servers or server clusters. The reliable design of such systems is critical to ensure the security of data and availability of services. However, designing correct-by-construction systems is challenging due to the complexity of and concurrency inherent in cloud file systems. Further, existing works on cloud file system analysis often focus on specific systems or lack formal modeling and verification, leading to potential design flaws and security vulnerabilities. To address these issues, we propose MSCFS-RP, which is a formal analysis model based on colored Petri nets. Leveraging the strengths of colored Petri nets in representing diverse information types with colored tokens and defining explicit rules for concurrent interactions, our model captures the writing and reading processes of clients, meta servers, and clusters. With strong formalism and support for verification using CPN Tools, we rigorously evaluate key properties such as replication consistency under various scenarios. The results demonstrate that MSCFS-RP satisfies these properties, validating its effectiveness and trustworthiness in managing information within cloud storage systems. [ABSTRACT FROM AUTHOR]

Informed, wise, land management has been a challenge since a long time, but has gained extra importance through the need for responding to climate change effects. The European Green Deal is one example of a concertation effort in a world where data availability, sharing options, and just‐in‐time exploitation are not entirely solved. The LandSupport service responds to this challenge through a combination of a Spatial Decision Support System on top of a datacube management and analytics engine. In this paper, we specifically focus on this datacube engine and how it can contribute to a better decision making through an ordered multidimensional data management paired with strictly standards‐compliant interfaces. Further, the approach described shows a remedy against the current fragmentation of services: through a location‐transparent federation a single common pool of information is established, including distributed fusion allowing an unlimited mix‐and‐match of data regardless of their location. [ABSTRACT FROM AUTHOR]

The German initiative "National Research Data Infrastructure for Personal Health Data" (NFDI4Health) focuses on research data management in health research. It aims to foster and develop harmonized informatics standards for public health, epidemiological studies, and clinical trials, facilitating access to relevant data and metadata standards. This publication lists syntactic and semantic data standards of potential use for NFDI4Health and beyond, based on interdisciplinary meetings and workshops, mappings of study questionnaires and the NFDI4Health metadata schema, and literature search. Included are 7 syntactic, 32 semantic and 9 combined syntactic and semantic standards. In addition, 101 ISO Standards from ISO/TC 215 Health Informatics and ISO/TC 276 Biotechnology could be identified as being potentially relevant. The work emphasizes the utilization of standards for epidemiological and health research data ensuring interoperability as well as the compatibility to NFDI4Health, its use cases, and to (inter-)national efforts within these sectors. The goal is to foster collaborative and inter-sectoral work in health research and initiate a debate around the potential of using common standards. [ABSTRACT FROM AUTHOR]

The article focuses on Norway's ongoing efforts to digitally transform its public sector, as outlined in its Digital Agenda and forthcoming strategy. Topics include enhancing digital governance, optimizing government investments in digital technologies, and fostering GovTech initiatives like AI to support sustainability and innovation. The review underscores the need for improved digital infrastructure, data management, and talent development to achieve effective digital government model.

Objectives: Field Epidemiology Training Programs (FETPs) are competencybased training programs that play a critical role in strengthening global health security and enhancing the epidemiological capabilities of public health professionals. This scoping review examined available published literature on the evaluations of FETPs globally. Methods: A literature review was conducted to evaluate FETPs globally. Keywords specific to the evaluation of FETPs were utilized to search the PubMed, Scopus, and Web Science databases. After applying the inclusion and exclusion criteria, 12 relevant studies from an initial pool of 60 were included in this study. Data extraction included key details, and a qualitative synthesis organized diverse findings using a narrative approach to draw appropriate conclusions and generate recommendations. Results: The review covered findings from 12 studies covering all three FETP modalities and spanning countries in various regions. Evaluations explored gained skills, engagement in FETP activities, and improvements in field epidemiological functions. Gained skills and knowledge, engagement in FETP activities, and improvements in field epidemiological functions were evident, with specific expectations for each FETP tier. Positive outcomes were consistent across studies, revealing improvements in surveillance activities, outbreak response, data management, and other system functions. Conclusion: This review confirmed the positive impact of FETPs on trainees and graduates, which emphasized competency enhancements across different modalities. Various strategies are recommended to improve the evaluation of FETPs. For effective evaluation, it is necessary to develop robust evaluation tools and establish standardized metrics to compare FETPs across regions or countries. [ABSTRACT FROM AUTHOR]

The burgeoning field of fog computing introduces a transformative computing paradigm with extensive applications across diverse sectors. At the heart of this paradigm lies the pivotal role of edge servers, which are entrusted with critical computing and storage functions. The optimization of these servers' storage capacities emerges as a crucial factor in augmenting the efficacy of fog computing infrastructures. This paper presents a novel storage optimization algorithm, dubbed LIRU (Low Interference Recently Used), which synthesizes the strengths of the LIRS (Low Interference Recency Set) and LRU (Least Recently Used) replacement algorithms. Set against the backdrop of constrained storage resources, this research endeavours to formulate an algorithm that optimizes storage space utilization, elevates data access efficiency, and diminishes access latencies. The investigation initiates a comprehensive analysis of the storage resources available on edge servers, pinpointing the essential considerations for optimization algorithms: storage resource utilization and data access frequency. The study then constructs an optimization model that harmonizes data frequency with cache capacity, employing optimization theory to discern the optimal solution for storage maximization. Subsequent experimental validations of the LIRU algorithm underscore its superiority over conventional replacement algorithms, showcasing significant improvements in storage utilization, data access efficiency, and reduced access delays. Notably, the LIRU algorithm registers a 5% increment in one-hop hit ratio relative to the LFU algorithm, a 66% enhancement over the LRU algorithm, and a 14% elevation in system hit ratio against the LRU algorithm. Moreover, it curtails the average system response time by 2.4% and 16.5% compared to the LRU and LFU algorithms, respectively, particularly in scenarios involving large cache sizes. This research not only sheds light on the intricacies of edge server storage optimization but also significantly propels the performance and efficiency of the broader fog computing ecosystem. Through these insights, the study contributes a valuable framework for enhancing data management strategies within fog computing architectures, marking a noteworthy advancement in the field. [ABSTRACT FROM AUTHOR]

Scientific data are essential to advancing scientific knowledge and are increasingly valued as scholarly output. Understanding what drives dataset downloads is crucial for their effective dissemination and reuse. Our study, analysing 55,473 datasets from 69 data repositories, identifies key factors driving dataset downloads, focusing on interpretability, reliability, and accessibility. We find that while lengthy descriptive texts can deter users due to complexity and time requirements, readability boosts a dataset's appeal. Reliability, evidenced by factors like institutional reputation and citation counts of related papers, also significantly increases a dataset's attractiveness and usage. Additionally, our research shows that open access to datasets increases their downloads and amplifies the importance of interpretability and reliability. This indicates that easy access enhances the overall attractiveness and usage of datasets in the scholarly community. By emphasizing interpretability, reliability, and accessibility, this study offers a comprehensive framework for future research and guides data management practices toward ensuring clarity, credibility, and open access to maximize the impact of scientific datasets. [ABSTRACT FROM AUTHOR]

Researcher fraud is often easy and enticing in academic research, with little risk of detection. Cases of extensive fraud continue to occur. The amount of fraud that goes undetected is unknown and may be substantial. Three strategies for addressing researcher fraud are (a) retrospective investigations after allegations of fraud have been made, (b) sting operations that provide conclusive evidence of fraud as it occurs, and (c) data management practices that prevent the occurrence of fraud. Institutional and regulatory efforts to address researcher fraud have focused almost exclusively on the retrospective strategy. The retrospective approach is subject to controversy due to the limitations of posthoc evidence in science, the difficulty in establishing who actually committed the fraud in some cases, the application of a legal standard of evidence that is much lower than the usual standards of evidence in science, and the lack of legal expertise by scientists investigating fraud. The retrospective strategymay be reliably effective primarily in cases of extensive, careless fraud. Sting operations can overcome these limitations and controversies, but are not feasible in many situations. Data management practices that are effective at preventing researcher fraud and unintentional errors are well-established in clinical trials regulated by government agencies, but appear to be largely unknown or unimplemented in most academic research. Established data management practices include: archiving secure copies of the raw data, audit trails, restricted access to the data and data collection processes, software validation, quality control checks, blinding, preregistration of data processing and analysis programs, and research audits that directly address fraud. Current discussions about data management in academic research focus on sharing data with little attention to practices that prevent intentional and unintentional errors. A designation or badge such as error-controlled data management could be established to indicate research that was conducted with data management practices that effectively address intentional and unintentional errors. [ABSTRACT FROM AUTHOR]

Nowadays, huge devices are connected to the internet to deal with many cloud environments for storing data efficiently. Also, it is used to store big data determined in business activities. However, the data storage in the cloud avoids duplicate copies of files and reduces complexity issues. However, this platform is utilized by many users for storing the file in cloud and this creates a data redundancy problem. Also, fingerprint indexing is a major issue in data storage in the cloud. So in order to overcome this issue a novel Enhanced Arithmetic Optimization Algorithm with Mixed-Kernel-based Extreme Learning Machine-based Random Forest (EAOA-MKELM-RF) method is proposed that overcome the deduplication issues. In spite, the scalability issues are solved, and the limitations determined in fingerprint indexing. Further, the pipelining and parallelizing hash function values are utilized to address the scalability issue in the deduplication process. The data storage container organizes the programs and files into data blocks, metadata files and nonduplicate blocks. However, the proposed methods determine the similarity issues and accelerate the fingerprint queries for better scalability. The datasets employed to evaluate the proposed approach are CorrAL-100 dataset, Madelon dataset, XOR-100 dataset, Infocom’05 dataset and Linux kernel dataset. The experimentation results demonstrated that the proposed method attained a superior accuracy by 96.8% in terms of restoration accuracy, robustness and delivery ratio. Additionally, the proposed approach reduces buffer time, improving overall system performance. This validation optimized the storage resources, diminished storage costs and improved overall data management. [ABSTRACT FROM AUTHOR]

To explore complex biological questions, it is often necessary to access various data types from public data repositories. As the volume and complexity of biological sequence data grow, public repositories face significant challenges in ensuring that the data is easily discoverable and usable by the biological research community. To address these challenges, the National Center for Biotechnology Information (NCBI) has created NCBI Datasets. This resource provides straightforward, comprehensive, and scalable access to biological sequences, annotations, and metadata for a wide range of taxa. Following the FAIR (Findable, Accessible, Interoperable, and Reusable) data management principles, NCBI Datasets offers user-friendly web interfaces, command-line tools, and documented APIs, empowering researchers to access NCBI data seamlessly. The data is delivered as packages of sequences and metadata, thus facilitating improved data retrieval, sharing, and usability in research. Moreover, this data delivery method fosters effective data attribution and promotes its further reuse. This paper outlines the current scope of data accessible through NCBI Datasets and explains various options for exploring and downloading the data. [ABSTRACT FROM AUTHOR]

With the development of measurement techniques and numerical simulation technologies, data-driven turbulence research has become a new approach in this field. In China, several wind tunnel laboratories and supercomputing centers have been established for turbulence simulations, resulting in a substantial collection of turbulence data. However, there is currently no centralized turbulence data management platform in China, which makes it difficult to achieve the exchange and share of the expensive experimental and simulation data. Turbulence data is characterized by its large volume, high dimensionality, precision and heterogeneity, which present problems in terms of storage, access and management efficiency. A turbulence big data distributed storage system called TDFS was designed, specifically targeting typical flow problems in aviation, aerospace, and marine applications. Considering the access characteristics of turbulence big data, the novel metadata management methods and data access interfaces were designed in TDFS. Experimental results demonstrate that TDFS achieves interface response speed improvements of 54.38% and 57.7% compared with HDFS and GlusterFS, respectively. Additionally, to reduce the storage overhead of turbulence big data, a lazy replication compression mechanism based on HDF5 was designed, resulting in 34% reduction in storage space, compared to the original replication storage approach. [ABSTRACT FROM AUTHOR]

In the field of distributed medical data storage, relayed data is usually kept centrally on the central node, which may lead to an increased risk of a single point of failure and security challenges from malicious tampering. While introducing backup nodes can partially alleviate these issues, the efficiency of synchronization and switching between nodes remains constrained. In addition, nodes storing relayed data can reach a consensus to modify the forwarded data, which lacks trustworthiness. Given the problems faced by traditional distributed storage in the field of medical data, combined with the characteristics of blockchain technology, we propose a distributed medical data storage model based on blockchain technology, named DDS (Decentralized Distributed Storage), which is intended to be applied in medical data management. In the DDS model, we embed relay data into blocks for storage and ensure the integrity and security of relay data for medical data by introducing redundant blockchain storage and collaborative verification mechanisms. The model is divided into two key phases: the storage phase of relay data of medical data and the verification phase. In the storage phase, the doctor's signature and the associated copy location data are sent to multiple validation nodes, after which the relay data block of medical data is generated and written to the blockchain. In the validation phase, the node first verifies whether the local relay data blockchain state is consistent with the global state. If it is not, a state synchronization operation is performed. Next, the validation node retrieves the local relay data blockchain to verify the integrity of the medical data relay data. From the theoretical analysis and experimental results, the DDS model can ensure the traceability and integrity of relay medical data and has good concurrent processing performance and less impact on the efficiency of medical data storage. [ABSTRACT FROM AUTHOR]

Connected devices in IoT continuously generate monitoring and measurement data to be delivered to application servers or end-users. Transmitting IoT data through networks would lead to congestion and long delays. NDN is an emerging network paradigm based on name-identified data known to be an appropriate architecture for supporting IoT networks. In-network caching is one of the main advantages of NDN, a major issue discussed in many studies. One of the significant challenges for some IoT data is the transiency nature, making the data caching mechanism different. IoT data such as ambient monitoring in urban areas and tracking current traffic conditions are often transient, which means these data have a limited lifetime and then expire. In the proposed approach, data placement is decided upon based on the data lifetime and node position. Data lifetime is an essential property that must be involved in caching methods; consequently, the data are classified based on the data lifetime, and specific nodes are selected for caching according to defined classes and nodes' positions in topology. Based on the proposed scheme, the nodes with the highest outgoing interface count or the edge nodes are selected for data caching. By considering both data lifetime and node location, we determine the suitable caching location for each data class separately. In addition, we remove data that has a short lifetime and is not suitable for caching from the caching mechanism of NDN nodes. By considering both the cache and data placements for transient data, a more comprehensive view is grasped in improving the caching performance. This issue, which has not been addressed in the available studies run on IoT data caching, can lead to the appropriate use of available storage and reduce redundancy. Eventually, the simulation results performed by the ndnSIM simulator show the proposed method could improve the cache mechanism efficiency in terms of both delay and hit ratio. Comparison results of the proposed method with CE2 and Btw indicate that this method can provide a reduction in average delay and an increase in cache hit ratio. [ABSTRACT FROM AUTHOR]

An immense volume of data is generated in smart health environments which can be managed through fog computing. Fog computing provides computing and storage services closer to the end user, making it an essential application for Healthcare Internet of Things (HIoT) devices. In HIoT, tasks such as Cardiovascular Health Monitoring (CHM) which are highly sensitive to delays and failures and data are scheduled and managed by fog nodes. Timely detection and intervention in CHM are crucial during emergencies, such as suspected heart attacks, where rapid processing of physiological data and prompt triggering of alarms or notifications can save lives. A microservices-based approach is proposed in this study combining fog and cloud services. The proposed Two-Phase Fault Tolerant (TPFT) strategy for HIoT data management schedules and manages HIoT tasks on fog nodes in the first phase, while in the second phase, it implements a bidirectional fault-tolerant mechanism, covering task-aware fault tolerance and node-aware fault tolerance. Comparing the performance of TPFT with recent benchmarks, simulation results demonstrate that the proposed approach outperforms in terms latency, probability of failure rate, recovery time after failure, and extra resource utilization. [ABSTRACT FROM AUTHOR]

As the information age progresses and oilfield production deepens, information technology plays an increasingly pivotal role in oilfield production, scientific research, and management. Data quality and integrity have become key issues in both research and production. This study analyzes the architecture of data quality control methods based on the technical system of the OSDU (Open Subsurface Data Universe) sharing center. A virtual data layer (VDB) is introduced, and the overall architecture of the exploration and development data exchange management platform is constructed through collaborative technology. Employing a systematic design concept, all applications within the data exchange management architecture are organically combined, forming an integrated and cohesive design and development strategy. This research presents a novel approach where the system application is distinctly separated from the database management. This strategy effectively resolves the challenges associated with managing metadata in oilfield exploration. By designing the database and coding specifications, the development and deployment of the system are completed efficiently. The implemented system offers comprehensive functionalities to all oilfield levels, including data storage, auditing, querying, statistical analysis, and data exchange management. It facilitates the seamless exchange of diverse exploration and development data, ensuring scientific data management and establishing a foundation for shared scientific data. This enhances service quality and the level of exploration and development in the field. This paper focuses on the design and development of a data exchange management platform for oilfield exploration and development, using the oil and gas industry as a case study. The analysis of industry demands and technology supply within this context is crucial for advancing the exploration and development capabilities in oilfields. [ABSTRACT FROM AUTHOR]

The rapid evolution of GPS devices, and therefore, collection of GPS data can be used to investigate a wide variety of topics in wildlife research. The combination of remotely collected GPS data with on‐the‐ground field investigations is a powerful tool for exploring behavioral ecology. "GPS cluster studies" are aimed at pinpointing and investigating identified clusters in the field. Activity clusters can be based on various parameters (e.g., distance between GPS locations and the number of locations needed to establish a cluster), which are closely related to the set research questions. Variation in methods across years within the same study may result in data collection biases. Therefore, a streamlined method to parametrize, generate interactive maps, and extract activity cluster data using a predefined approach will limit biases, and make field work and data management straightforward for field technicians. We developed the "ClusterApp" Shiny application in the R software to facilitate a step‐by‐step guide to execute cluster analyses and data management of cluster studies on any species using GPS data. We illustrate the use of the "ClusterApp" with two location datasets constructed by data collected on brown bears (Ursus arctos) and gray wolves (Canis lupus). [ABSTRACT FROM AUTHOR]

This study explores how combining virtual reality (VR) and augmented reality (AR) with geographic information systems (GIS) revolutionizes data visualization. It traces the historical development of these technologies and highlights key milestones that paved the way for this study's objectives. While existing platforms like Esri's software and Google Earth VR show promise, they lack complete integration for immersive GIS visualization. This gap has led to the need for a dedicated workflow to integrate selected GIS data into a game engine for visualization purposes. This study primarily utilizes QGIS for data preparation and Unreal Engine for immersive visualization. QGIS handles data management, while Unreal Engine offers advanced rendering and interactivity for immersive experiences. To tackle the challenge of handling extensive GIS datasets, this study proposes a workflow involving tiling, digital elevation model generation, and transforming GeoTIFF data into 3D objects. Leveraging QGIS and Three.js streamlines the conversion process for integration into Unreal Engine. The resultant virtual reality application features distinct stations, enabling users to navigate, visualize, compare, and animate GIS data effectively. Each station caters to specific functionalities, ensuring a seamless and informative experience within the VR environment. This study also delves into augmented reality applications, adapting methodologies to address hardware limitations for smoother user experiences. By optimizing textures and implementing augmented reality functionalities through modules Swift, RealityKit, and ARKit, this study extends the immersive GIS experience to iOS devices. In conclusion, this research demonstrates the potential of integrating virtual reality, augmented reality, and GIS, pushing data visualization into new realms. The innovative workflows and applications developed serve as a testament to the evolving landscape of spatial data interpretation and engagement. [ABSTRACT FROM AUTHOR]

Transit crashes are a serious social problem for any country, with a significant loss of human lives and economic consequences that are difficult to quantify. This article proposes a characterization of the transit crash rate for urban road safety using time series. A quantitative descriptive study was conducted, characterizing the variables of each crash extracted from the National Traffic Agency of Ecuador (NTA); the data were processed at a descriptive and predictive level for the city of Guayaquil. The first step was an exploration of the scientific interest of the topic with the processing of bibliographic data taken from Scopus and Web of Science articles. Among the results obtained, there is a growing trend of research related to the evaluation of traffic crash through applied statistics. Every day, approximately 155 people die as a result of a traffic crash. In addition, traffic crashes are analyzed based on three indicators: number of crashes, injuries and onsite fatalities. Finally, an adequate performance is found, with very few differences in the forecast of incidents using three times series models, autoregressive integrated moving average (ARIMA). It is expected that this study will be valuable for data analysts and decision makers at the security level to reduce human losses related to these events in urban cities with similar characteristics to the analyzed cases. [ABSTRACT FROM AUTHOR]

Effective data management is crucial for scientific integrity and reproducibility, a cornerstone of scientific progress. Well-organized and well-documented data enable validation and building on results. Data management encompasses activities including organization, documentation, storage, sharing, and preservation. Robust data management establishes credibility, fostering trust within the scientific community and benefiting researchers' careers. In experimental biomedicine, comprehensive data management is vital due to the typically intricate protocols, extensive metadata, and large datasets. Low-throughput experiments, in particular, require careful management to address variations and errors in protocols and raw data quality. Transparent and accountable research practices rely on accurate documentation of procedures, data collection, and analysis methods. Proper data management ensures long-term preservation and accessibility of valuable datasets. Well-managed data can be revisited, contributing to cumulative knowledge and potential new discoveries. Publicly funded research has an added responsibility for transparency, resource allocation, and avoiding redundancy. Meeting funding agency expectations increasingly requires rigorous methodologies, adherence to standards, comprehensive documentation, and widespread sharing of data, code, and other auxiliary resources. This review provides critical insights into raw and processed data, metadata, high-throughput versus low-throughput datasets, a common language for documentation, experimental and reporting guidelines, efficient data management systems, sharing practices, and relevant repositories. We systematically present available resources and optimal practices for wide use by experimental biomedical researchers. [ABSTRACT FROM AUTHOR]

Sun, Y.; Zhang, S.; Yang, S.; Zhou, W.; Cui, X., and Wu, Y., 2024. Review and analysis of electronic monitoring (EM) minimum standards for pelagic main fishing gear. Journal of Coastal Research, 40(4), 816–825. Charlotte (North Carolina), ISSN 0749-0208. Observer systems for monitoring and collecting catch information from pelagic fisheries are essential in ensuring the sustainable harvesting of pelagic fisheries resources. The emergence in recent years of electronic observers that rely on video surveillance is gradually becoming a complementary or alternative measure to human observers. Electronic observers' security and ease of use make full electronic monitoring (EM) of ocean-going fishing ships possible while ensuring complete data availability. The development and improvement of its standards are the keys to ensuring the large-scale application of EM. This paper summarizes and justifies the minimum standards for EM for pelagic fishing by analyzing the standards and current status of human observers, documenting the functions and applications of EM systems, comparing the monitoring capabilities of human observers and EM, and analyzing the characteristics of the three main fishing methods: purse seine, trawl, and longline, respectively, in terms of equipment platform technology, data collection, and data management. The development of EM standards can clarify the cost of the entire monitoring process, ensure the compliance and economics of EM, and achieve effective long-term monitoring of pelagic fishing. [ABSTRACT FROM AUTHOR]

The blockchain is an emerging and essential technology in offering various services to several applications including the financial industry and information technology. Consequently, recent information methodologies cannot satisfy the constraints. The data management issue may rise complexities including tracking operations with information security and unsatisfactory traceability data quality due to the high increase in inventory management data. To address the challenge, this paper aims to focus on developing new hyperledger blockchain-enabled secure inventory management with an adaptive key management process. Here, the inventory management data processed by different distributors is encrypted using the adaptive key generation by Fitness-derived Predator Presence Probability-based Squirrel Search Algorithm (FPPP-SSA) that ensures data control. The optimal key management in encryption solves the multi-objective function based on the "degree of modification information preservation rate and hiding ratio (Euclidean distance)." Once the data are enciphered, they are saved in the hyperledger blockchain, where the smart contract is the chain code application. While restoring or decrypting data, the authorized distributor should use the same adaptive key for maintaining secured inventory management. The performance is evaluated under diverse test cases, and the results are estimated under certain performance metrics. [ABSTRACT FROM AUTHOR]

Using constrained factor mixture models (FMM) for careless response identification is still in its infancy. Existing models have overly restrictive statistical assumptions that do not identify all types of careless respondents. The current paper presents a novel constrained FMM model with more reasonable assumptions that capture both longstring and random careless respondents. We provide a comprehensive comparison of the statistical assumptions between the proposed model and two previous constrained models. The proposed model was evaluated using both real data (N = 1,455) and statistical simulation. The results showed that the model had a superior fit, stronger convergent validity with other indicators of careless responding, more accurate parameter recovery and more accurate identification of careless respondents when compared to its predecessors. The proposed model does not require additional data collection effort, and thus researchers can routinely use it to control careless responses. We provide user-friendly syntax with detailed explanations online to facilitate its use. [ABSTRACT FROM AUTHOR]

The development of the digital era has created high interest among students in taking part in competitions organized by universities to improve their practical skills. The Business and System Innovation Challenge (BASIC) an international competition which is held annually by the Faculty of Information Systems, a University in Jakarta, plays a role in facilitating this. However, the entire competition process is still carried out manually, which often makes teams experience difficulties in managing data. This problem can be overcome by designing a BASIC website database using the Rapid Application Development (RAD) method. The benefit of creating this system is that it makes it easier for participants to register for competitions and admins to manage data. Problem analysis was carried out by building entity relationship diagrams, the PHP programming language using the Laravel framework, and the MySQL database. The results of designing the BASIC website are features such as registering for competitions, uploading competition work results, providing feedback, and evaluating work results. Admin can also process data on the number of competition participants each year into a report. Data processing becomes faster and more accurate through complex database design. [ABSTRACT FROM AUTHOR]

Surface hydrologic modeling becomes a problem when insufficient spatial and temporal information is available. It is common to have useful modeling periods of less than 15 years. The purpose of this work is to develop a methodology that allows the selection of meteorological and hydrometric stations that are suitable for modeling when information is scarce in the area. Based on the scarcity of data, a series of statistical tests are proposed to eliminate stations according to a decision-making process. Although the number of stations decreases drastically, the information used is reliable and of adequate quality, ensuring less uncertainty in the surface simulation models. Individual basin modeling can be carried out considering the poor data. The transfer of parameters can be applied through the nesting of basins to have information distributed over an extensive area. Therefore, temporally and spatially extended modeling can be achieved with information that preserves statistical parameters over time. If data management and validation is performed, the modeled watersheds are well represented; if this is not done, only 26% to 50% of the runoff is represented. [ABSTRACT FROM AUTHOR]

The problems of information isolation, inefficiency, and paper-based data archiving in traditional highway survey and design methods are investigated in this paper. A novel digital sandbox platform framework was developed to promote the efficiency of route design, model data integration, and information sharing. Under the presented framework, an integrated application method for both the Building Information Modeling (BIM) and Geographic Information System (GIS) technologies was designed by using Unreal Engine technology. Firstly, a digital base model was established by integrating multi-disciplinary BIM model data and GIS three-dimensional (3D) multi-scale scene model data. On this basis, using Unreal Engine technology for visualization development, a digital sandbox platform with the data visualization, traffic organization simulation analysis, 3D spatial analysis, component information query, and scene switching functions was developed, which satisfies the 3D visualization and digitalization needs in the current highway planning and design. Additionally, the Analytic Hierarchy Process (AHP) was employed to analyze the impact of digital base model on the development and application of platform modules, including five crucial factors: data accuracy, data representation, multi-source data fusion, data management capability, and scene semantic representation. Finally, the research results indicate that the proposed digital sandbox platform framework provides users with a platform for integrated data management, information sharing, and 3D data visualization, while reducing design time by 30%, total design cost by 12%, and land occupancy rate by 10%. [ABSTRACT FROM AUTHOR]