Unified Data Research Articles

Concepts for a Semantically Accessible Materials Data Space: Overview over Specific Implementations in Materials Science

This article describes advancements in the ongoing digital transformation in materials science and engineering. It is driven by domain‐specific successes and the development of specialized digital data spaces. There is an evident and increasing need for standardization across various subdomains to support science data exchange across entities. The MaterialDigital Initiative, funded by the German Federal Ministry of Education and Research, takes on a key role in this context, fostering collaborative efforts to establish a unified materials data space. The implementation of digital workflows and Semantic Web technologies, such as ontologies and knowledge graphs, facilitates the semantic integration of heterogeneous data and tools at multiple scales. Central to this effort is the prototyping of a knowledge graph that employs application ontologies tailored to specific data domains, thereby enhancing semantic interoperability. The collaborative approach of the Initiative's community provides significant support infrastructure for understanding and implementing standardized data structures, enhancing the efficiency of data‐driven processes in materials development and discovery. Insights and methodologies developed via the MaterialDigital Initiative emphasize the transformative potential of ontology‐based approaches in materials science, paving the way toward simplified integration into a unified, consolidated data space of high value.

Integration and Optimization of Route Planning and Inventory Management in Supply Chain Transaction Systems: A Comprehensive Analysis

This comprehensive article analysis explores the integration and optimization of route planning and inventory management within supply chain transaction systems, examining their transformative impact across various industries. The article investigates the evolution of modern logistics operations, emphasizing the crucial role of advanced technologies, blockchain frameworks, and integrated systems in enhancing operational efficiency. Through a detailed examination of core components including algorithmic foundations, real-time tracking systems, demand forecasting mechanisms, and unified data architectures, this article presents a holistic view of contemporary supply chain optimization. The article analysis encompasses multiple sectors, including retail, e-commerce, and manufacturing, demonstrating how integrated systems have revolutionized traditional approaches to inventory management and route optimization. By exploring performance metrics, return on investment, and future trends, this article provides valuable insights into the implementation strategies and scalability considerations essential for modern supply chain systems. The article highlights the significance of cross-functional optimization and cloud-based solutions in achieving operational excellence while maintaining high levels of customer satisfaction and cost efficiency.

Unifying and Linking Data Sources in Medical and Public Health Research

Unifying and Linking Data Sources in Medical and Public Health Research

An efficient computational framework for data assimilation of fractional-order dynamical system with sparse observations

We introduce an efficient computational framework for data assimilation of fractional dynamical systems, extending traditional data assimilation techniques to fractional models. This framework offers effective computational methods that eliminate the need for complex adjoint model derivations and algorithm redesign. We establish the fundamental problem formulation, develop both the AtD and DtA approaches, and derive adjoint forms and numerical schemes for each method. Additionally, we create a unified fractional-order variational data assimilation framework applicable to both linear and nonlinear models, incorporating both explicit and implicit discrete methods. Specific discretization schemes and gradient formulas are derived for three distinct types of fractional-order models. The method's reliability and convergence are verified, and the effect of observation sparsity and quality is examined through numerical examples.

A unified VCF data set from nearly 1,500 diverse maize accessions and resources to explore the genomic landscape of maize.

Efforts to capture and analyze maize nucleotide diversity have ranged widely in scope, but differences in reference genome version and software algorithms used in these efforts inhibit comparison, and these data are generally not available in an easy-to-use visualization platform for quick access and analysis. To address these issues, The Maize Genetics and Genomics Database has collaborated with maize researchers to offer variant data from a diverse set of 1,498 inbred lines, traditional varieties, and teosintes through a standardized variant-calling pipeline against version 5 of the B73 reference genome. The output was filtered for mapping quality, completeness, and linkage disequilibrium, and annotated based on variant effects relative to the B73 RefGen_v5 gene annotations. MaizeGDB has also updated a web tool, SNPversity 2.0, to filter, visualize, and download genotype sets based on genomic locations and accessions of interest, and added external data sets to demonstrate SNPversity 2.0's broad usage. MaizeGDB plans to host annual updates of these resources as additional resequencing data become available, with plans to expand to all publicly available sequence data.

PHI-base - the multi-species pathogen-host interaction database in 2025.

The Pathogen-Host Interactions Database (PHI-base) has, since 2005, provided manually curated genes from fungal, bacterial and protist pathogens that have been experimentally verified to have important pathogenicity, virulence and/or effector functions during different types of interactions involving human, animal, plant, invertebrate and fungal hosts. PHI-base provides phenotypic annotation and genotypic information for both native and model host interactions, including gene alterations that do not alter the phenotype of the interaction. In this article, we describe major updates to PHI-base. The latest version of PHI-base, 4.17, contains a 19% increase in genes and a 23% increase in interactions relative to version 4.12 (released September 2022). We also describe the unification of data in PHI-base 4 with the data curated from a new curation workflow (PHI-Canto), which forms the first complete release of PHI-base version 5.0. Additionally, we describe adding support for the Frictionless Data framework to PHI-base 4 datasets, new ways of sharing interaction data with the Ensembl database, an analysis of the conserved orthologous genes in PHI-base, and the increasing variety of research studies that make use of PHI-base. PHI-base version 4.17 is freely available at www.phi-base.org and PHI-base version 5.0 is freely available at phi5.phi-base.org.

GDPR Compliance of Hospital Management Systems in the UAE

While the UAE is making strides in healthcare digitalization and adopting global best practices, the absence of a unified data protection framework equivalent to the GDPR poses significant challenges for hospital management systems (HMS) in the region. This gap creates uncertainties in compliance, especially regarding cross-border data transfers, third-party vendor management, and the protection of patients' privacy rights. The lack of clear regulations tailored to the UAE’s unique healthcare landscape hinders the implementation of robust data protection measures, raising concerns about potential data breaches, legal liabilities, and the overall trustworthiness of healthcare institutions. Addressing these challenges is crucial for aligning the UAE’s healthcare sector with international standards while ensuring the security and privacy of patient data in a rapidly evolving digital environment. The General Data Protection Regulation (GDPR) has significantly impacted hospital management systems (HMS) by setting strict data protection requirements. This study provides a systematic literature review of GDPR compliance in HMS, focusing on key challenges such as regulatory complexity, permission management, data subject rights, data breaches, third-party vendor management, and cross-border data transfers. Suggested mitigation measures include privacy by design, data protection impact assessments, improved consent management, robust breach detection, and efficient vendor management. Legislative reforms are needed to clarify GDPR's application to healthcare. The study also highlights increased investments in privacy technologies, improved patient trust, and the demand for advanced solutions. Future research should explore the effectiveness of these mitigations, GDPR's impact on patient satisfaction, ethical data processing, and standardized data protection frameworks in healthcare. Achieving GDPR compliance is crucial for protecting patient data, building trust, and ensuring secure and ethical use of healthcare information. This study aims to guide healthcare organizations, particularly hospitals, along with regulators and researchers, in navigating these challenges and implementing effective solutions.

Some legal issues of development of underground space for urban planning needs

The article is devoted to certain legal aspects of the development of underground space for urban planning needs in Ukraine Peculiarities of legal regulation of the process of development of underground space in domestic legislation both in general and for urban planning needs in particular are considered. The features of the concepts «underground space», «land», «land» in legislation and scientific doctrine are analyzed. Proposals for improving the legal regulation of social relations related to the creation and use of underground spatial objects for urban planning needs have been formulated. The problematic issues of creation and legalization of the latter are considered. The history of approaches to the emergence and registration of the ownership of underground objects related to land plots, the world experience of the use and operation of 3D cadastres, the legal and technical features of the introduction and use of such information are highlighted. The need to create a 3D cadastre system in Ukraine is emphasized, the current state of the State Land Cadastre and the State Register of Real Property Rights is analyzed, aspects of development in recent years in the direction of digitization and the use of geospatial data are explored, and further prospects for the introduction of 3D cadastre in our country are outlined. Individual examples of registration of ownership of underground objects in European countries, in particular the countries of Northern Europe, are considered. Using the example of the legislation and judicial practice of the Kingdom of the Netherlands, it is demonstrated how it is necessary to solve the problem of registering the ownership of underground components of objects that go beyond the land plots on which the above-ground parts of buildings are located, and relevant proposals are made to change the current domestic legislation. The concept of creation of the National System of Real Estate Registers and Cadastres, developed by the Cabinet of Ministers of Ukraine, was considered, and in-depth unification and synchronization of data on land plots as spatial objects and underground buildings in the relevant electronic state registers was proposed.

Revolutionizing urban emission tracking: Enhanced vehicle ratios via remote sensing techniques

This study presents research that examines the use of several remote sensing methods to quantify automobile emissions in China. The research provides a unified data processing technique, using methodologies such as plume chasing (PCH) and point sampling (P-sampling) to measure vehicle emissions accurately. This method accurately calculates the ratios of gaseous combustion, namely NOx/CO2, in different driving scenarios and verifies the technique using controlled vehicle emissions measurements. The results demonstrate the method’s ability to identify variations in emission levels linked to engine operation and alterations in after-treatment systems. This provides significant information for monitoring emissions in metropolitan areas such as Wuhan, China. The suggested method has substantial promise for improving air quality initiatives in China.

A StarGAN and transformer-based hybrid classification-regression model for multi-institution VMAT patient-specific quality assurance.

The field of artificial intelligence (AI)-based patient-specific quality assurance (PSQA) for volumetric modulated arc therapy (VMAT) faces challenges in terms of developing general models across institutions due to the prevalence of multi-institution data collection and multivariate heterogeneity. Building a general model that is capable of handling diverse multi-institution data is critical for enabling large-scale integration and analysis. This study aims to develop a star generative adversarial network (StarGAN) and transformer-based hybrid classification-regression PSQA framework to address unification of heterogeneous data from different institutions. A StarGAN and transformer-based hybrid classification-regression model was developed as a general PSQA framework to predict gamma passing rates (GPRs) and classify quality assurance (QA) results as "Pass" or "Fail" at multiple institutions. A total of 1815 VMAT plans were collected from eight institutions to develop the general PSQA framework and perform clinical commissioning and implementation. Among them, 20 independent clinical plans from each of eight institutions, for a total of 160 plans, were used for the clinical commissioning, and 205 new clinical plans from eight institutions were used for clinical implementation. For the 3%/3, 3%/2, and 2%/2mm gamma criteria, the sensitivity of the proposed PSQA framework with pretraining was 90.13%, 92.03%, and 95.84%, respectively, while the specificity was 76.01%, 76.12%, and 85.34%, respectively. The mean absolute errors (MAEs) of the proposed PSQA framework with pretraining were 1.36%, 2.37%, and 3.96%, respectively, while the root-mean-square errors (RMSEs) were 2.31%, 3.89%, and 5.17%, respectively. The results demonstrated visible improvement at multiple institutions. For clinical commissioning, the deviations between the predicted and measured results were all within 3% for 3%/3 and 3%/2mm at eight institutions. For clinical implementation, all failure plans were correctly identified by the proposed PSQA framework. The general PSQA framework enables diverse clinical data sources to be handled to achieve enhanced model performance and generalizability, and provides a solution to the unification of heterogeneous data from different institutions to construct robust QA models. This approach can be clinically deployed for VMAT QA.

Application and platform decoupling strategy of substation integrated monitoring system

Abstract Aiming at the problems of poor data opening and sharing ability and closed application ecology of substation integrated monitoring system, the open monitoring system of a substation based on platform + APP architecture is proposed, which realizes the decoupling of APP and the monitoring system vendor platform through the security reinforced container technology, unified data interaction technology, and data service-oriented middleware technology. The decoupling of APP and platform promotes the construction of an open and shared industrial ecosystem for substation-integrated monitoring systems, which in turn improves the level of substation intelligence.

The software investigation of the new multimodal imaging system for one-stop health examination

The present investigation aimed to explore the software for one-stop health examination by multimodal and multifunction imaging. In order to implement the system, the software framework architecture and its views are proposed in the article. Taking magnetic acoustic magnetic induction imaging technology as an example, based on the study of the imaging mechanism such as a deeper understanding of the essence of the crucial charge transfer process in biology of each modality, in order to better understand life phenomena. And a unified data format is used for image fusion, stitching, detection, and recognition of each modality, simultaneously ensuring the reliability and credibility of the software. The multimodal multi-function one-stop medical imaging system is feasible to health examination, and its unique innovation and methodology will certainly promote the commercialization for the target system. Results indicate the system will achieve faster imaging speed, higher resolution of the applications of biomedical imaging.

Source apportionment of particle number size distribution at the street canyon and urban background sites

Abstract. Particle size is one of the key factors influencing how aerosol particles affect their climate and health effects. Therefore, a better understanding of particle size distributions from various sources is crucial. In urban environments, aerosols are produced in a large number of varying processes and conditions. This study intended to develop the source apportionment of urban aerosols by utilising a novel approach to positive matrix factorisation (PMF). The particle source profiles were detected in particle number size distribution data measured simultaneously in a street canyon and at a nearby urban background station between February 2015 and June 2019 in Helsinki, southern Finland. The novelty of the method is combining the data from both sites and finding profiles for the unified data. Five aerosol sources were found. Four of them were detected at both of the stations: slightly aged traffic (TRA2), secondary combustion aerosol (SCA), secondary aerosol (SecA), and long-range-transported aerosol (LRT). One of the sources, fresh traffic (TRA1) was only detected at a street canyon. The factors were identified based on available auxiliary data. Additionally, the trends of the found factors were studied, and statistically significant decreasing trends were found for TRA1 and SecA. A statistically significant increasing trend was found for TRA2. This work implies that traffic-related aerosols remain important in urban environments and that aerosol sources can be detected using only particle number size distribution data as input in the PMF method.

Addressing integration in the organization of palliative care in Belgium: a multilevel ecosystems approach using the analytic hierarchy process (AHP) method

BackgroundPalliative care is becoming an essential component of healthcare, but there is insufficient research on how integration across different levels of care (micro, meso, and macro) is realized in practice. Without such integration, care may become fragmented, leading to suboptimal patient outcomes. While many studies have explored palliative care models, there is a gap in understanding how priorities for integrated care align across these levels within healthcare ecosystems. Specifically, it is unclear whether key actions at each level are shared, coordinated, and supported effectively, making it difficult to implement sustainable, cohesive care strategies. Our study aims to explore the extent to which important goals (i.e., priorities) are shared across the micro, meso, and macro levels of the palliative care ecosystem in Flanders, Belgium.MethodsWe applied a multimethod study using the analytic hierarchy process method (AHP). This consists of three sequential steps: a broad literature search and interviews with Belgian stakeholders (n = 12) to determine the criteria for the organization of integrated care; focus groups (n = 8) with patients, their relatives and caregivers to establish the completeness and relevance of the criteria; and prioritization of the criteria using a questionnaire among 305 Flemish participants (patients, relatives, caregivers and policy makers).ResultsOur findings revealed that integration is imbalanced, with priorities being most emphasized at the micro level (57%), followed by the meso (29%) and macro (14%) level. Functional enablers dominate at the macro (80%) and meso organizational level (67%), while normative enablers are emphasized at the meso professional (67%) and micro level (75%). Effective palliative care requires vertical coordination of these enablers: for instance, transparent communication with patients at the micro level depends on cross-organizational information exchange at the meso level, supported by a unified data system at the macro level.ConclusionAchieving integrated palliative care requires deliberate alignment of priorities across all levels of the ecosystem. While each level plays a unique role, palliative care is comprehensive and effective only by sharing both functional and normative enablers across micro, meso, and macro level.

Unlocking public infrastructures? State aid and the common European data space

EU state aid rules are part of the EU’s competition policy, a form of soft law developed to nudge states away from public investments in many areas. Rules were used to impose that market competition holds a privileged position, and that enacting social, political, or economic benefits that might stifle competition requires a complicated legal and political negotiation process between the European Commission, Member States, and private companies. Nonetheless, many strategic projects or situations in recent years led to significant state aid. The Commission utilised a variety of means to encourage both private and public investments in EU based digital infrastructure that is critical for public media services and for unified European data space. Despite EU’s balancing act between the private and public interests, business interests are dangerously capturing the development in the media data space. To grasp the social and economic contribution of publicly financed productive activities, we approach public broadcasting systems as producers of public wealth of a peculiar kind whose activities and goals are defined by their public purpose, rather than the imperatives of capital and profitmaking. Unifying data spaces with too many design decisions conceded to capitalist production runs the risk of further neglecting social foundations of member states. We argue that the desired digital transformation would be more successful if it moved towards affirmative approaches to public services and public infrastructures, recognising the historical and contemporary contribution of the production of public wealth and public media in member states.

Data-Driven Clustering Analysis for Representative Electric Vehicle Charging Profile in South Korea.

As the penetration of electric vehicles (EVs) increases, an understanding of EV operation characteristics becomes crucial in various aspects, e.g., grid stability and battery degradation. This can be achieved through analyzing large amounts of EV operation data; however, the variability in EV data according to the user complicates unified data analysis and identification of representative patterns. In this research, a framework that captures EV charging characteristics in terms of charge-discharge area is proposed using actual field data. In order to illustrate EV operation characteristics in a unified format, an individual EV operation profile is modeled by the probability distribution of the charging start and end states of charge (SoCs).Then, hierarchical clustering analysis is employed to derive representative charging profiles. Using large amounts of real-world, vehicle-specific EV data in South Korea, the analysis results reveal that EV charging characteristics in terms of the battery charge-discharge area can be summarized into seven representative profiles.

SCUBA implements a storage format-agnostic API for single-cell data access in R

While robust tools exist for the analysis of single-cell datasets in both Python and R, interoperability is limited, and analysis tools generally only accept one object class. Considerable programming expertise is required to integrate tools across package ecosystems into a comprehensive analysis, due to their differing languages and internal data structures. This complicates validation of results and leads to inconsistent visualizations between analysis suites. Conversion between object formats is the most common solution, but this is difficult and error-prone due to the rapid pace of development of the analysis suites and their underlying data structures. To address this, we created SCUBA (Single-Cell Unified Backend API), an R package that implements a unified data access API for all common R and Python single-cell object formats. SCUBA extends the data access approach from the widely used Seurat package to SingleCellExperiment and anndata objects. SCUBA also implements new data-specific access functions for all supported object types. Performance scales well across all SCUBA-supported formats. In addition to performance, SCUBA offers several advantages over object conversion for the visualization and further analysis of pre-processed single-cell data. First, SCUBA extracts only data required for the operation at hand, leaving the original object unmodified. This process is simpler, less error prone, and less memory intensive than object conversion, which operates on the entire dataset. Second, code written with SCUBA can use any supported object class as input, with simple and consistent syntax across object formats. This allows a single analysis script or package (like our interactive single-cell browser, scExploreR) to work seamlessly with multiple object types, reducing the complexity of the code and improving both readability and reproducibility. Adoption of SCUBA will ultimately improve collaboration and reproducible research in single-cell analysis by lowering the barriers between package ecosystems.

Data Modeling and Synchronization Method to Align Power Trading Rules for Integrated Energy Management Systems

Integrated energy systems (IESs) couple multiple energy sources to promote clean energy and reduce emissions. IESs need to participate in business activities, such as power trading, aided by automated data-driven systems to achieve optimal and economical operation. However, challenges arise due to the lack of unified data model standards and the semantic ambiguity of rules, on top of the difficulty of synchronizing data across heterogeneous subsystems of integrated energy management systems (IEMSs). Previous research on power trading data models was limited to certain application scenarios and heterogeneities. This study pivots from proposing model standards to a standard neutral way to align power trading rules across IEMS subsystems. The method features a framework for power trading rules and a software platform called the power trading rule synchronizer. The proposed approach can minimize semantic ambiguity and ensure the automated rule synchronization across subsystems of IEMSs. A case study demonstrated the application of the proposed method, saving an estimated 672 man-days in implementing the rules in 16 subsystems of the IEMS, contributing directly and indirectly to reducing emissions. This study provides a foundation for aligning other rules in IEMSs, such as carbon and gas trading rules.

Met4DX: A Unified and Versatile Data Processing Tool for Multidimensional Untargeted Metabolomics Data.

Liquid chromatography-mass spectrometry (LC-MS) is a powerful tool in untargeted metabolomics, enabling the high-sensitivity and high-specificity characterization of metabolites. The integration of ion mobility (IM) with LC-MS, known as LC-IM-MS, enhances the analytical depth, facilitating more comprehensive metabolite profiling. However, the complexity of data generated by these technologies presents significant challenges in data processing. Addressing these challenges, we developed Met4DX, a unified and versatile software tool for processing both 3D and 4D untargeted metabolomics data. Met4DX incorporates a new MS1-oriented peak detection approach coupled with our bottom-up assembly algorithm, enabling highly sensitive and comprehensive peak detection in untargeted metabolomics data. Additionally, Met4DX employs a uniform quantification strategy to enhance the precision of peak integration across different samples. The software provides a user-friendly interface that simplifies data processing with default parameter sets, consolidating peak detection, alignment, quantification, and other procedures into a single streamlined workflow. Together, Met4DX offers a comprehensive solution for multidimensional metabolomics data processing, transforming raw data from diverse MS instruments into a final feature table containing quantification and identification results. We postulate Met4DX facilitates metabolite discovery in biological samples by deciphering the complex untargeted metabolomics data. Met4DX is freely available on the Internet (https://met4dx.zhulab.cn/).

A Novel Kind of Knowledge Graph Construction Method for Intelligent Machine as a Service Modeling

With the development of Intelligent Machine as a Service (IMaaS), devices increasingly require personalization, intelligence, and service orientation, making resource modeling a key challenge. Knowledge graph (KG) technology, known for unifying heterogeneous data, has become an essential tool for modeling and analyzing manufacturing resources. On this basis, this study proposes a novel resource KG construction method for IMaaS. First, an E-R diagram is used to divide the constant and variable entities and set the constant attributes and the constant relationships. Then, the triplets are named, the value space is set, and the schema layer is constructed. Finally, the related information about devices is used to fill the data layer, and then, the knowledge graph is generated. Meanwhile, this study utilizes desktop FDM 3D printing devices as a case example for validation. The method proposed in this study can enhance the accuracy and maintainability of equipment resource management in the manufacturing sector, effectively promoting subsequent activities such as management, analysis, and decision-making.