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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]

AbstractA unique attribute of the nuclear industry is the extent by which designers, developers, operators, and regulators pay attention to demonstrating the safety case. Nuclear installation resiliency or safety takes overriding priority over function and performance. The preparation of the safety case heavily relies on extensive experimental campaigns that challenge the target design under a spectrum of postulated accident scenarios. For safety reasons, these experiments, typically conducted in subscale test apparatuses intended to reproduce the postulated accident scenarios and event sequences realistically, but subject to proven similarity criteria, are often referred to as scaling analyses. Since the dawn of the nuclear industry, significant resources have been committed to the construction and operation of such test facilities, and a massive amount of data has been generated. Such test data have been and are still used to validate the fundamental assumptions of nuclear power plant phenomena.As we move into the digital world, capturing this multiple decades worth of information in a transparent, easily accessible, and usable manner for the public and industry stakeholders is of paramount importance. Over the last few years, FPoliSolutions has been actively developing an enterprise digital data ontology platform (OGMA) intended to do just that. The goal is to help designers use experimental data to assess their safety case evaluation models. The platform or application programming interface (API) was designed to ontologically organize the experimental data, as well guide the user in the complex analytics associated with the interpretation and use of such test results. The OGMA API supports the user in accessing a library of sophisticated mathematical procedures for performing scaling analyses. For this purpose, the dynamical system scaling (DSS) procedure invented by Dr. Jose’ Reyes was formulated as one of the services in the digital platform.These methods have been demonstrated to provide an excelled mathematical apparatus to identify similarity criteria or quantify distortions between measured data and the target prototypical conditions. Moreover, DSS can provide a level of synthesis across separate effect tests and integral effect tests that has the promise of yielding efficiency in the evaluation code assessment activities, as setting up evaluation models that support the safety case of current and future nuclear power plants is often a daunting and expensive task. [ABSTRACT FROM AUTHOR]

Objective: The goal of this work is to reduce the amount of manual work required to go from data capture to regulatory submission. It will be shown that the use of Siamese networks will allow for the generation of embeddings that can be used by traditional machine learning classifiers to perform the classification at much higher levels of accuracy than standard approaches. Methods: Siamese networks are a method for training data embeddings such that data within the same class are closer with respect to a given distance metric than they are to data points in another class. Because they are designed to learn similarity within pairs of data points, they work well in situations where the number of classes is relatively large compared to the number of training samples. In this work, we will show that embeddings generated via a Siamese network from metadata associated with electronic data capture forms can be used to predict the associated SDTM field. Results: With a relatively simple network coupled with a basic classification algorithm, the proposed method can achieve accuracies greater than 90%, which is significantly higher than what has been achieved with traditional methods, with many of the inaccurate mappings due to a lack of training data. In many cases, there is a 15% increase in accuracy vs. more traditional methods. Conclusion: Leveraging Siamese networks, it is possible to generate embeddings that efficiently represent data fields in a lower dimensional space. This allows the creation of a system that can automatically map between data schemas at high levels of accuracy. Such systems represent the first step in automating one of the many labor-intensive data management tasks associated with clinical trials. [ABSTRACT FROM AUTHOR]

Oceanographic mooring missions are critical for advancing our understanding of the world's oceans and their role in the broader Earth system. These missions rely on the deployment and maintenance of complex observing systems, comprising subsurface, profiling, and surface moorings, which are designed to collect valuable data from the ocean interior. The success of these missions depends on meticulous planning and strategic execution, which are facilitated by the use of Field Service Plans (FSPs). FSPs are comprehensive and adaptable planning tools that integrate safety measures, contingency plans, and timelines to ensure the successful and safe recovery, maintenance, and deployment of mooring systems. In this paper, we provide a comprehensive guide to drafting, implementing, and adapting FSPs tailored to the unique challenges posed by oceanographic mooring missions. We highlight the critical role of FSPs in optimizing safety, data quality, and mission success, while also underscoring the need for flexibility and adaptation. Our aim is to provide practical guidance and a template for researchers, expedition leaders, and institutions engaged in oceanographic research. [ABSTRACT FROM AUTHOR]

ABSTRACT Aims Design Setting Participants Results Discussion Conclusion and Impact Recommendations To explore the experiences, and perceptions of nurses who are told to withhold clinical information from children diagnosed with serious illnesses.An interpretative qualitative phenomenological design was used for the study. Sampling was purposive and data were collected using semi‐structured interviews from nurses working in a paediatric setting within the preceding 5 years. Nurse‐participants described their personal experiences of withholding situations (‘non‐disclosure directives’). Their accounts of specific examples of situations (‘personal accounts’) were extracted from the interview transcripts and analysed using inductive content analysis to identify patterns and concepts within the descriptions. Data management was supported using Dedoose.Nurse‐participants were interviewed face‐to face and online between November 2019 and December 2020.Twenty‐six Australian nurses who have cared for seriously ill children within the preceding 5 years participated in the study.Thirty‐nine accounts of non‐disclosure situations (personal accounts) were extracted from interview transcripts. Four types of non‐disclosure were identified in the personal accounts: withholding information, temporary withholding, lying and combined withholding/lying. The inductive content analysis identified three key aspects of nurse‐participants' accounts: beliefs and perceptions (about the child and parents), the nature of the experience and feelings about the experience.Moral distress occurred when nurses' actions conflicted with their moral values in response to a non‐disclosure directive, particularly when asked to directly lie to children. Nurses felt disempowered by the parent's authority and the institutional hierarchy that limited their ability to participate in discussions where a non‐disclosure directive was given.Nurse‐participants were afforded a platform for reflection which proved valuable in allowing them to process their experiences in a non‐disclosure situation.Nurses directed to withhold medical information from children should be afforded opportunities for reflection to mitigate moral distress. Nurses should be included in the planning of ethically appropriate responses to withholding directives. [ABSTRACT FROM AUTHOR]

Most scientific facilities produce large amounts of heterogeneous data at a rapid pace. Managing users, instruments, reports and invoices presents additional challenges. To address these challenges, EMhub, a web platform designed to support the daily operations and record‐keeping of a scientific facility, has been introduced. EMhub enables the easy management of user information, instruments, bookings and projects. The application was initially developed to meet the needs of a cryoEM facility, but its functionality and adaptability have proven to be broad enough to be extended to other data‐generating centers. The expansion of EMHub is enabled by the modular nature of its core functionalities. The application allows external processes to be connected via a REST API, automating tasks such as folder creation, user and password generation, and the execution of real‐time data‐processing pipelines. EMhub has been used for several years at the Swedish National CryoEM Facility and has been installed in the CryoEM center at the Structural Biology Department at St. Jude Children's Research Hospital. A fully automated single‐particle pipeline has been implemented for on‐the‐fly data processing and analysis. At St. Jude, the X‐Ray Crystallography Center and the Single‐Molecule Imaging Center have already expanded the platform to support their operational and data‐management workflows. [ABSTRACT FROM AUTHOR]

Introduction Disease and non-battle injuries (DNBIs) cause substantial losses among military personnel. NATO has monitored DNBIs among its personnel since 1996 using multiple versions of a tool now called EpiNATO-2, but the surveillance system has never been systematically evaluated. Following a request from NATO to the CDC, the objective of this study was to assess surveillance system attributes of EpiNATO-2 using CDC's updated guidelines for evaluating public health surveillance systems. Materials and Methods Between June and October 2022, a literature review and key informant interviews were conducted to assess the following attributes: usefulness, simplicity, flexibility, data quality, acceptability, sensitivity, positive predictive value, representativeness, timeliness, stability, informatics system quality, informatics service quality, and informatics interoperability. Key informant interviews were conducted in Kosovo, Germany, and remotely with EpiNATO-2 users spanning three levels: clinical and data entry personnel (tactical level); regional medical and public health officers (operational level); and senior commanders and other governmental entities (strategic level). Results Fourteen EpiNATO-2 users participated in interviews, representing 3 of the 5 major NATO missions, 3 partner entities, and 7 nationalities. All users (100%) reported that the system did not meet their needs, with most users noting the following challenges: lack of clearly defined system objectives; poor data quality due to ambiguous case definitions and frequently unsubmitted reports (37% missing during January to June 2022); long delay between the occurrence of health events and the availability of corresponding data (≥2 weeks); and an antiquated and inflexible data management system. Overall, performance was deemed unsatisfactory on 11 of the 13 attributes. Conclusions This multinational sample of EpiNATO-2 users at all military levels reported that the system is currently not useful with respect to its stated objectives. Opportunities exist to improve the performance and usefulness of EpiNATO-2: improve case definitions, modernize data infrastructure, and regularly evaluate the surveillance system. [ABSTRACT FROM AUTHOR]

In recent years, diversified measurements reflect the system dynamics from a more comprehensive perspective in system modeling and analysis, such as scalars, waveform signals, images, and structured point clouds. To handle such multimodal structured high-dimensional (SHD) data, combining a large amount of data from multiple sites is necessary (i) to reduce the inherent population bias from a single site and (ii) to increase the model accuracy. However, impeded by data management policies and storage costs, data could not be easily shared or directly exchanged among different sites. Instead of simplifying or facilitating the data query process, we propose a federated multiple tensor-on-tensor regression (FedMTOT) framework to train the individual system model locally using (i) its own data and (ii) data features (not data itself) from other sites. Specifically, federated computation is executed based on alternating direction method of multipliers (ADMM) to satisfy data-sharing requirements, while the individual model at each site can still benefit from feature knowledge from other sites to improve its own model accuracy. Finally, two simulations and two case studies validate the superiority of the proposed FedMTOT framework. [ABSTRACT FROM AUTHOR]

While the demand for evidence-based policing (EBP) has increased and researchers continue to generate important research findings, few targeted educational efforts have been made to nurture effective police-researcher collaboration and integrate EBP into police practice. The current study evaluates a 16-month-long Applied Criminology and Data Management Course (ACDC), which aimed to train police executives and early-career researchers to work together, produce EBP research, and disseminate their findings. Survey and observational data assess participant progress on four key Kirkpatrick evaluation model metrics: reaction, learning, behavior, and results. ACDC participants demonstrated satisfaction with the program and knowledge of skills required for effective collaboration and research. Most notably, they demonstrated application of skills through applied research and dissemination of results. Participants proved committed to advancing EBP via trainings to police executives and community stakeholders, publications, presentations, promotions to various relevant positions, and secured grant funding. Overall, these preliminary results suggest that ACDC can serve as a successful and feasible model for advancing EBP. [ABSTRACT FROM AUTHOR]

Laboratory courses are key to science, technology, engineering, and mathematics (STEM) programs in higher education, but often lack the flexibility for distance learning and inclusiveness for all students. To address these challenges, TU Bergakademie Freiberg introduced remote laboratories not only for international degree programs and the "Disti4Study" application, which provides interactive visualization of industrial thermal separation processes. This paper focuses on the development and didactic challenges associated with the "Continuous Distillation Laboratory". It illustrates how rethinking the technical and didactic frameworks of traditional laboratories can align with new learning objectives, such as those related to Industry 4.0, including the Internet of Things and data management. [ABSTRACT FROM AUTHOR]

With the rising prevalence of digital data, infrastructures and platforms in education, the challenge of conceptualizing and investigating 'intermediaries' has substantially increased. This not only refers to various new types of actors that have been materializing around practices of data infrastructuring (e.g., data management), but equally to the rising empowerment of data infrastructures themselves as intermediaries of policy and governance. The aim of this article is to sharpen our conceptual understanding of this interrelation between intermediaries and data infrastructuring. More specifically, the article suggests to approach intermediaries through a lens on performative contexting, thus shifting the focus towards how 'intermediary contexting' is used, by whom and where exactly, rather than seeking to map intermediaries as an object 'from the outside'. Data infrastructuring, then, can be regarded both as part, and as a result, of such contexting efforts. Using Estonia as a case study, it is shown what we see (differently) when applying such a lens to the digital transformation of education. The findings hereby indicate a gradual emergence of what could be described as 'governance by intermediarization': a process in which more and more actors are shifted into the (self)contexting as infrastructural stewards, while the politics of digital transformation become centered – i.e., seemingly depoliticized – around asserting continuous change through digital connection. [ABSTRACT FROM AUTHOR]

Open access to data underpinning published results is a key pillar of scientific reproducibility. Making data available at scale also provides opportunities for data reuse, encouraging the development of new analysis approaches. In this poster article, accompanying a recorded talk, we will explain the benefits of publicly archiving your image data alongside your published manuscripts, as well as highlight what resources are available to do this. This will include the BioImage Archive, EMBL‐EBI's new resource for biological image data, https://www.ebi.ac.uk/bioimage‐archive/. We will look at how image data submission works, how to prepare in advance for archiving your data and upcoming developments. [ABSTRACT FROM AUTHOR]

At the second stage of China Spallation Neutron Source (CSNS-II), it is predicted that 2 PB raw experimental data will be produced annually from twenty instruments. Scientific computing puts forward higher requirements for data sharing, utilization, retrieval, analysis efficiency, and security. However, the existing data management system (DMS) based on ICAT has several limitations including poor scalability of metadata database, imperfect data-management lifecycle and inflexible API. To ensure the accuracy, usability, scalability and efficiency of CSNS-II experimental data, a new scientific data management system is therefore designed based on the DOMAS framework developed by the Computing Center of IHEP. The data acquisition, transmission, storage and service systems are re-designed and tailored specifically for CSNS-II. Upon its completion, the new DMS will overcome the existing challenges and offer functions such as online display, search functionality and rapid download capabilities for metadata, raw data and analyzed data; flexible and user-friendly authorization; and data lifecycle management. Ultimately, the implementation of the new Data Management System (DMS) is expected to enhance the efficiency of experimental data analysis, propelling CSNS-II to achieve international advanced standards. Furthermore, it aims to reinforce self-reliance and technological strength in the field of science and technology at a high level in China. The development and deployment of the new DMS begin at the end of 2023. [ABSTRACT FROM AUTHOR]

The Digital Twin (DT) technology has shown promise in transforming complex engineered systems. However, its adoption in the Architecture, Engineering, Construction, and Operation (AECO) field, particularly for Built Cultural Heritage (BCH) conservation, is still in its early stages. This study presents a systematic literature review of 85 academic publications to evaluate the current state of DT implementation in heritage building conservation and identifies areas for optimising preventive management. In addition, this review explores interpretations of the DT concept in this field, addressing overlaps with Historical/Heritage Building Information Modelling (HBIM), discusses DT functionalities and presents existing frameworks. The findings demonstrate the potential of DT to revolutionise BCH conservation through a holistic approach. However, further focus is needed on features and tools for enhancing performance-based management with targeted strategies and advanced data analysis. Future research should prioritise developing these aspects to fully leverage the potential of the DT paradigm in BCH conservation. [ABSTRACT FROM AUTHOR]

Research data mismanagement (RDMM) is a serious threat to accountability, reproducibility, and re-use of data. In a recent article in this journal, it was argued that RDMM can take two forms: intentional research misconduct or unintentional questionable research practice (QRP). I disagree because the scale for severity of consequences of research misbehavior is not bimodal. Furthermore, intentionality is difficult to prove beyond doubt and is only one of many criteria that should be taken into account when deciding on the severity of a breach of research integrity and whether a sanction is justified. Making a distinction between RDMM that is research misconduct and RDMM which not puts too much emphasis on intentionality and sanctioning. The focus should rather be on improving data management practices by preventive actions, in which research institutions should take a leading role. [ABSTRACT FROM AUTHOR]

A high-quality hydrographic observational database is essential for ocean and climate studies and operational applications. Because there are numerous global and regional ocean databases, duplicate data continues to be an issue in data management, data processing and database merging, posing a challenge on effectively and accurately using oceanographic data to derive robust statistics and reliable data products. This study aims to provide algorithms to identify the duplicates and assign labels to them. We propose first a set of criteria to define the duplicate data; and second, an open-source and semi-automatic system to detect duplicate data and erroneous metadata. This system includes several algorithms for automatic checks using statistical methods (such as Principal Component Analysis and entropy weighting) and an additional expert (manual) check. The robustness of the system is then evaluated with a subset of the World Ocean Database (WOD18) with over 600,000 in-situ temperature and salinity profiles. This system is an open-source Python package (named DC_OCEAN) allowing users to effectively use the software. Users can customize their settings. The application result from the WOD18 subset also forms a benchmark dataset, which is available to support future studies on duplicate checks, metadata error identification, and machine learning applications. This duplicate checking system will be incorporated into the International Quality-controlled Ocean Database (IQuOD) data quality control system to guarantee the uniqueness of ocean observation data in this product. [ABSTRACT FROM AUTHOR]

The surge in applications necessitates a more intelligent and automated Higher Education Admission Management System (HEAMS). This research proposes a novel Deep Learning (DL)-based HEAMS utilizing a Cloud-Edge Architecture. The first step collects applicant data like transcripts, test scores, essays, and recommendations. Edge devices perform initial cleaning and preprocessing on these data to ensure quality and privacy. These preprocessed data using normalization and feature extraction using the Latent Dirichlet Allocation (LDA) are then transferred to the cloud where DL models, such as Convolutional Neural Networks (CNNs) for essays or Recurrent Neural Networks (RNNs) for transcripts, are trained. These models learn complex patterns from historical labeled data (admitted/not admitted) to predict an applicant’s success probability. During application evaluation, new data are fed through the trained models on the edge, generating probabilities for predefined classifications — high-potential, moderate, or low-potential. The cloud receives these probabilities and combines them with predefined admission criteria like minimum GPA. This combined analysis leads to a final classification using Novel Lite Convolutional Neural Network with Hybrid Leader-based Optimization (Lite CNN-HLO) for each applicant — admitted, waitlisted, or rejected and admission management system by refining admission decisions for admitted, waitlisted, and rejected applicants based on institutional priorities and constraints. The system not only generates classifications but also provides detailed model score breakdowns for transparency. This Cloud-Edge HEAMS offers improved efficiency, reduced workload for admissions staff, and potentially fairer decisions by mitigating bias through data-driven analysis. [ABSTRACT FROM AUTHOR]

ABSTRACT Regional factors correlated with recruitment of black bass (largemouth bass; Micropterus nigricans and smallmouth bass; Micropterus dolomieu), two important fishes, are rarely studied, despite the importance of recruitment variation in influencing fish populations. We sought to identify factors that drove variation in age‐0 and age‐1 black bass abundance. Age‐0 or age‐1 black bass catch per unit effort (CPUE) in reservoirs in Kansas and Illinois, USA, was positively correlated with local short‐term (i.e., mean April precipitation) environmental variables. In contrast, age‐0 or age‐1 black bass CPUE was generally negatively correlated with long‐term environmental variables and river regulation metrics (i.e., reservoir elevation) in the Mississippi and Missouri river reservoirs. Our findings highlight that consideration of spatiotemporal scale is important when managing black bass populations. [ABSTRACT FROM AUTHOR]

The rapid advancement of Industry 4.0 necessitates close collaboration among material research institutions to accelerate the development of novel materials. However, multi-institutional cooperation faces significant challenges in protecting sensitive data, leading to data silos. Additionally, the heterogeneous and non-independent and identically distributed (non-i.i.d.) nature of material data hinders model accuracy and generalization in collaborative computing. In this paper, we introduce the MatSwarm framework, built on swarm learning, which integrates federated learning with blockchain technology. MatSwarm features two key innovations: a swarm transfer learning method with a regularization term to enhance the alignment of local model parameters, and the use of Trusted Execution Environments (TEE) with Intel SGX for heightened security. These advancements significantly enhance accuracy, generalization, and ensure data confidentiality throughout the model training and aggregation processes. Implemented within the National Material Data Management and Services (NMDMS) platform, MatSwarm has successfully aggregated over 14 million material data entries from more than thirty research institutions across China. The framework has demonstrated superior accuracy and generalization compared to models trained independently by individual institutions. Industry 4.0 requires collaboration among material research institutions, but data silos hinder progress. Here the authors present MatSwarm, a swarm-learning framework that integrates secure computing and data sharing in the National Material Data Management and Services (NMDMS) platform. [ABSTRACT FROM AUTHOR]

Background: Brace therapy's influence on adolescent idiopathic scoliosis's (AIS) natural course is inconclusive. Methods: Brace-treated AIS patients from 2016 to 2020 were examined regarding four endpoints at brace weaning: surgery need, curve progress ≥ 6° and > 45°, and curve improvement ≥ 6°. Prediction variables' influence was computed for the all-patients group and three subgroups (Subgroup 1: fulfilling the Scoliosis Research Society's [SRS] criteria, Subgroup 2: initial Cobb angle < 25°, Subgroup 3: initial Cobb angle > 40°). According to the data characteristics, parametric and non-parametric tests and binary logistic regression were performed. Results: The research included 69 patients. Overall, after brace weaning surgery was recommended for 20.3% of them, curve progression was ≥ 6° in 23.2%, the Cobb angle was beyond 45° in 11.6%, and the Cobb angle improved by ≥ 6° in 20.3%. Patients needing surgery had a significantly higher initial Cobb angle (38.8° ± 9.8° vs 27.8° ± 7.6°, p < 0.001), lower Risser stages (p = 0.010), and higher Nash and Moe degrees (p = 0.030). Patients with curve progress ≥ 6° were younger at first curve notification (12.4 ± 1.5 vs 13.7 ± 1.7 years, p = 0.011) and older at menarche (13.4 ± 1.1 vs 12.6 ± 1.2 years, p = 0.037). Furthermore, 21.6% of all Risser 3 and 4 patients had still curve progress ≥ 6°. In-brace correction was significantly higher in patients with curve improvement ≥ 6° vs < 6° (54.0% ± 31.2% vs 31.9% ± 30.7%; p = 0.019). Nine patients fulfilled the SRS criteria, 22 had initial Cobb angles < 25°, and 11 had > 40°. Real brace wear (RBW) in all groups had no significant effect on outcome. Two significant subgroup differences were found: Surgery recommendation and curve progression beyond 45° were significantly more frequent in the initial Cobb angle > 40° subgroup. Conclusions: Brace effectiveness can be assumed because curve improvement was achieved in nearly one-fifth with sufficient in-brace correction. However, no significant influence of RBW on the outcome endpoints was demonstrated. To clarify conflicting results, big data management, including untreated patients, must be employed to further research AIS's multifactorial influenced aetiology and course. Meanwhile, it is worth starting bracing in AIS in practice also beyond the SRS's criteria. [ABSTRACT FROM AUTHOR]

Cryogenic electron microscopy (Cryo-EM) is a widely used technique for visualizing the 3D structures of many drug design targets, including membrane proteins, at atomic resolution. However, the necessary throughput for structure-based drug design (SBDD) is not yet achieved. Currently, data analysis is a major bottleneck due to the rapid advancements in detector technology and image acquisition methods. Here we show "GoToCloud", a cloud-computing-based platform for advanced data analysis and data management in Cryo-EM. With GoToCloud, it is possible to optimize computing resources and reduce costs by selecting the most appropriate parallel processing settings for each processing step. Our benchmark tests on GoToCloud demonstrate that parallel computing settings, including the choice of computational hardware, as well as a required target resolution have significant impacts on the processing time and cost performance. Through this optimization of a cloud computing environment, GoToCloud emerges as a promising platform for the acceleration of Cryo-EM SBDD. The design philosophy, implementation, optimization, and performance evaluation of a cloud-computing platform for data analysis and management in Cryo-EM single particle analysis to achieve a significant reduction in processing time and cost. [ABSTRACT FROM AUTHOR]

Here we review the ‘what and how’ of molecular techniques used in the context of haematopathological diagnostics of both lymphoid and myeloid neoplasms. Keeping in mind that the required resources for molecular testing are not universally available, we will not only discuss novel and emerging techniques that allow more high‐throughput and sophisticated analyses of lymphoid and myeloid neoplasms, but also the more classical, low‐cost alternatives and even some workarounds for molecular testing approaches. In this review we also address other key aspects around molecular techniques for haematopatholgy diagnostics, including preanalytics, data interpretation, and data management, bioinformatics, and interlaboratory precision and performance evaluation. [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]

The Acta Bioética article from October 1, 2024 addresses the ethical and bioethical challenges in research data management in Chile, in relation to European regulations such as the GDPR and FAIR principles. The role of institutions such as La Referencia, CLACSO, and UNESCO in promoting practices aligned with European standards is highlighted. The importance of integrating responsible data management in research is emphasized, including the implementation of DPIAs in Chile to improve data protection. [Extracted from the article]

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]

Health care in developing countries often lacks adequate bookkeeping and national cancer registries, means of information that have proven to impact disease research and care. The true burden of brain tumors therefore remains unchecked and so does the extent of the problem. Therefore, this study aims to explore the challenges and potential strategies related to information management of brain tumors in low- and middle-income countries (LMICs). A comprehensive literature search conducted using databases such as PubMed, Scopus, Google Scholar, and Cumulated Index in Nursing and Allied Health Literature, without any language restrictions, from inception to October 20, 2022. Following screening and extraction of data, themes were generated using the information management domain of the National Surgical, Obstetric, and Anesthesia Plan framework. The final analysis includes 23 studies that highlighted the challenges to managing information to the surgical care given to brain tumors in LMICs, including lack of proper hospital record system (43%), lack of national brain tumor registry (67%), lack of local management guidelines (10%), and low research output (33%). Some of the proposed strategies in the literature to address these barriers include improving data management systems (45%), developing a population-based brain tumor registry (64%), and formulating local treatment guidelines (9%) for the management of brain tumors. In LMICs, improving patient outcomes and quality of life postneurosurgical intervention for brain tumors requires coordinated efforts to enhance information systems. The support of the government and public health professionals is vital in implementing strategies to achieve this goal. [ABSTRACT FROM AUTHOR]

Simple Summary: Animal births, much like human births, can encounter complications that threaten the well-being of both the mother and the offspring. While monitoring the birth process is essential for ensuring proper care, human supervision can be costly. Commercial systems do exist for large animals, but there are currently no comparable solutions for small ruminants, despite various research efforts. This study explores the application of inertial sensors to detect parturition in small ruminants. This study also highlights which factors most significantly influence the outcomes of each investigation and summarizes the main results regarding birth detection. The review shows that different approaches focus on recognizing particular animal behaviors and designing detection algorithms. Although none of the studies presented a completely reliable detection method, most produced encouraging results, indicating noticeable behavioral changes in the hours preceding birth. The birth process in animals, much like in humans, can encounter complications that pose significant risks to both offspring and mothers. Monitoring these events can provide essential nursing support, but human monitoring is expensive. Although there are commercial monitoring systems for large ruminants, there are no effective solutions for small ruminants, despite various attempts documented in the literature. Inertial sensors are very convenient given their low cost, low impact on animal life, and their flexibility for monitoring animal behavior. This study offers a systematic review of the literature on detecting parturition in small ruminants using inertial sensors. The review analyzed the specifics of published research, including data management and monitoring processes, behaviors indicative of parturition, processing techniques, detection algorithms, and the main results achieved in each study. The results indicated that some methods for detecting birth concentrate on classifying unique animal behaviors, employing diverse processing techniques, and developing detection algorithms. Furthermore, this study emphasized that employing techniques that include analyzing animal activity peaks, specifically recurrent lying down and getting up occurrences, could result in improved detection precision. Although none of the studies provided a completely valid detection algorithm, most results were promising, showing significant behavioral changes in the hours preceding delivery. [ABSTRACT FROM AUTHOR]

With the accelerated development of globalization cross-border e-commerce market shows a booming trend. In this context the traditional operation mode has been difficult to meet the rapid changes and diversified needs of the cross-border market. At present the data obtained by cross-border e-commerce are enough to solve the internal management process and department needs. However due to the dispersion and use value of data the data information between different departments cannot be effectively communicated resulting in a large number of value data forming barriers. The resulting operational differences cannot fully peel away the data to observe the expression dilemma behind it resulting in a difference gap. It is impossible to form an accurate personalized and logical operation mechanism. The cross-border e-commerce market is highly complex and changeable. Single case studies can have a deep insight into the market status quo in a specific context accurately capture and explain the operation behavior in a specific environment under situational sensitivity to reveal its causal relationship enhance the persuasive power of research by collecting and analyzing detailed empirical data and make flexible and adaptive adjustments in the face of new problems of research objects in the research process. Therefore taking brand F as an example this paper integrates first-hand and second-hand research materials including internal documents interview records observation notes etc. analyzes the collaborative relationship between the research object and the research target through the single-case study method analyzes its potential operation rules based on the data-product-data theory and reveals the operation mechanism logic from the datadriven perspective. By grafting data technology and application the study reveals the realization mode of innovative operation mechanism with data value discovery-data value creation-data value realization as the research paradigm realizing the interaction and cooperation of design end production end and marketing end and integrating value chain links to promote the reconstruction of industrial chain. The construction of an ecological operation mechanism can gradually focus on trend benefits maximize their benefits optimize the coordination degree between vertical supply chain resources and supply departments improve product attribute labels strengthen consumer search accuracy improve consumer satisfaction and audience stickness avoid data stacking and functional limitations maximize information value and eliminate idle data and functions in time. In the future it is necessary to improve the extrapolation of research and the applicability to the global market by expanding the sample size and incorporating more consumer groups with multicultural backgrounds. In terms of technology iteration and follow-up the latest data technologies and operational tools can be continuously followed up and incorporated to more fully explore the potential and application of products. For the research on shortening cultural differences crosscultural perspective research can be strengthened to deeply analyze the influence of different cultures on consumer behavior preferences so as to optimize cross-cultural operation strategies. In terms of data management and analysis technology innovation it is necessary to explore advanced data management and analysis technology to deal with more complex data types and improve the accuracy and efficiency of data analysis. [ABSTRACT FROM AUTHOR]

Development of digital structural maintenance for federal highway engineering structures – Workshop "Digital structural maintenance including structural inspection" in Bonn in February 2024 This report presents the results of the workshop "Digital building maintenance including structural inspection", which took place in February 2024 in Bonn at the Federal Ministry for Digital and Transport. The aim of the event was to highlight market‐ready solutions related to digitalization and their future developments, as well as to formulate requirements for further developments by users. During individual workshop sessions, participants from industry and administration discussed the challenges, potentials, and next steps for implementation on the topics of structural inspection, non‐destructive testing, and monitoring. The results indicated that the field of building maintenance has significant potential for support through digital technologies. This includes, among other things, the adaptation of relevant regulations, the use of best practice applications, the integration of digital technologies into the maintenance process, support in the development of a data management system, and the training of relevant processes and workflows. [ABSTRACT FROM AUTHOR]

Digital data management for maintenance in offshore wind farms The DiMoWind‐Inspect research project explored the essential requirements for implementing digital methods in the maintenance of offshore wind turbines. Currently, data from all stages of the turbines' lifecycles are underutilized, leading to suboptimal maintenance actions being taken in terms of location, timing, and cost. A consistent structuring of previously often unstructured construction and maintenance data developed in the project enables their cross‐disciplinary, user‐specific availability. To accomplish this, the principles of the Reference Designation System for Power Plants RDS‐PP are applied. In this way, additional information, such as maintenance measures or defects, can be structured alongside components. The components of the structures are directly linked with information from inspections and other maintenance activities, following the Building Information Modeling approach. This allows for processing operational information about the condition of the structure and providing it for relevant calculations and assessments. As a use case, a two‐stage concept is presented, utilizing maintenance information to assess the fatigue life of a corrosion‐damaged structural detail in the support structure of an offshore wind turbine. [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]