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We have developed two novel axial anisotropic inversion codes for magnetotelluric (MT) data: a full axial inversion and a decoupled axial inversion. Both codes are based on the data space Gauss–Newton inversion method used in the WSINV3DMT code, enhanced with OpenMP parallelization and Intel MKL PARDISO direct solver for improved computational efficiency. The full axial inversion searches for three axial anisotropic resistivity elements ρ xx , ρ yy , and ρ zz . Based on our prior influence studies, we identified contrasting influence patterns: ρ xx has a strong influence on Zxy and Zyy, whereas ρ yy has a strong influence on Zyx and Zxx, while ρ zz has negligible impact on all responses. We, therefore, proposed a decoupled axial inversion. This novel technique splits the full axial inversion into two independent modes: ρ xx - and ρ yy -modes. The ρ xx -mode requires just the Zxy and Zyy responses to recover ρ xx , while the ρ yy -mode requires the other two responses. By splitting the inversion, each mode requires half the data parameters and reduces model parameters to one-third compared to the full axial inversion. The substantial parameter reduction is the key factor leading to significantly faster processing times and lower memory requirements. Both codes were validated with the synthetic model. Utilizing OpenMP and direct solvers, the full axial inversion processed 256 MT stations in 65 min per iteration or a total of 195 min for three iterations to converge. The decoupled inversion achieved significantly faster processing, requiring just 32 min per iteration or a total of 96 min to finish the inversion. In addition, the decoupled inversion requires just a fraction of the memory used in the full axial inversion. This makes it practical to operate even on the standard personal computers of current technology. We further applied the decoupled inversion to real MT data acquired in the Northwestern Pacific Ocean. The resulting 3-D inverted ρ xx and ρ yy models support the existence of the anisotropy occurring at 60–120 km depth beneath the ocean floor, in agreement with previous studies. [ABSTRACT FROM AUTHOR]

This study aims to propose a data space-based collaboration platform targeting continuous process industries such as continuous casting of steel and oil refining. The proposed collaboration platform serves as a basic environment in which supply chain participants share data through a data space, further supporting effective collaboration among the participating companies. Specifically, it implements a collaboration model among supply chain participants that reflects the characteristics of continuous process industries in which the supply chain is centered around the core suppliers. In fact, the key issue of continuous process industries is how to effectively distribute the manufacturing capabilities and production capacities of key suppliers with large-scale equipment to demand companies. In this context, this study (1) examines the results of existing research on data space-based collaboration platforms, (2) derives the differentiating factors and implementation strategies of the collaboration process among supply chain participants in continuous process industries, (3) presents the basic structure and operating framework of the collaboration platform, and (4) validates the proposed collaboration model through a simulation study based on a hypothetical scenario. The experimental results show that the proposed method is not only superior to a conventional distributed approach but also achieves similar performance to a centralized approach. [ABSTRACT FROM AUTHOR]

Focusing on the classification of business data resources within a unified business semantic environment is an important method to simplify the data environment and a crucial approach to studying data intelligence. A multi-value chain data space is a typical business semantic heterogeneous complex data environment. This paper summarizes the characteristics of multi-value chain business data resources and proposes a study on their classification using business semantic logic. By constructing a semantic-based relational model for multi-value chain business data resources and a multi-value chain business lexicon, this paper unifies the semantics of business data resources. This creates conditions for their classification according to business logic. Based on the feature transformation of business data resources, this paper proposes a clustering algorithm for multi-value chain business data resources (Business data resource classification algorithm for multi-value chain data space, BDRCA4MVCDS) aimed at a data space, completing the classification of business data resources. Finally, comparative experiments with KMeans and KABSA demonstrate the clustering effectiveness of the proposed algorithm (BDRCA4MVCDS) as well as its good stability and adaptability. [ABSTRACT FROM AUTHOR]

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Copyright of Cyber Security & Data Governance is the property of Editorial Office of Information Technology & Network Security 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.)

IntroductionData-driven medicine is essential for enhancing the accessibility and quality of the healthcare system. The availability of data plays a crucial role in achieving this goal.MethodsWe propose implementing a robust data infrastructure of FAIRification and data fusion for clinical, genomic, and imaging data. This will be embedded within the framework of a distributed analytics platform for healthcare data analysis, utilizing the Personal Health Train paradigm.ResultsThis infrastructure will ensure the findability, accessibility, interoperability, and reusability of data, metadata, and results among multiple medical centers participating in the BETTER Horizon Europe project. The project focuses on studying rare diseases, such as intellectual disability and inherited retinal dystrophies.ConclusionThe anticipated impacts will benefit a wide range of healthcare practitioners and potentially influence health policymakers.

The definition of a tourism data space (TDS) in the Balearic Islands is a complex process that involves identifying the types of questions to be addressed, including analytical tools, and determining the type of information to be incorporated. This study delves into the functional requirements of a Balearic Islands' TDS based on the study of scientific research carried out in the field of tourism in the Balearic Islands and drawing comparisons with international scientific research in the field of tourism information. Utilizing a bibliometric analysis of the scientific literature, this study identifies the scientific requirements that should be met for the development of a robust, rigorous, and efficient TDS. The goal is to support excellent scientific research in tourism and facilitate the transfer of research results to the productive sector to maintain and improve the competitiveness of the Balearic Islands as a tourist destination. The results of the analysis provide a structured framework for the construction of the Balearic Islands' TDS, outlining objectives, methods to be implemented, and information to be considered. [ABSTRACT FROM AUTHOR]

The number of data sources and models in the mobility and transport domain strongly proliferated in the last decade. Most formats have been created to enable specific and innovative applications. On the other hand, the available data models present a certain degree of complexity in terms of their integration and management due to partial overlaps, and in most cases, they could be exploited alternatively to implement the same smart and latest innovative solutions. This paper offers an overview of data models, standards and their relationships. A second contribution highlights any possible exploitation of data models for implementing operational processes for city transportation management and for the feeding of simulation and optimization processes that produce other data results in other data models. The final goal in most cases is the monitoring and control of city transport conditions, as well as the tactic and strategic planning of city infrastructure. This work was developed in the context of the CN MOST, a national center of sustainable mobility in Italy, and it is based on exploiting the Snap4City platform. [ABSTRACT FROM AUTHOR]

This article delves deeply into the burgeoning field of Tourism Data Spaces (TDS) in Europe, focusing on how technologies like Big Data and IoT are redefining the tourism sector. This technological shift is steering traditional tourist destinations towards smarter, more sustainable models. The study utilizes a multifaceted approach, combining documentary and bibliographical analysis with empirical data from the EU's DATES project. By employing the Drivers, Pressures, State, Impacts, Responses (DPSIR) model, it provides a nuanced understanding of the dynamics in TDS. The findings underscore TDS's pivotal role in improving decision-making and personalizing tourism services. The study also acknowledges the growing need for detailed tourism information to enhance travel planning and experience personalization. Furthermore, it highlights the importance of efficient and secure data management in tourism. This comprehensive analysis points to a future where data-driven insights foster more sustainable, tailored travel experiences. Additionally, the research illuminates both the challenges and prospects inherent in implementing TDS, stressing the importance of clear governance, technical standards, and balancing diverse stakeholder interests in the tourism industry. By addressing these challenges, the article posits that TDS can make a significant contribution to the innovation and sustainability of the tourism sector. [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.)

Industry 4.0 supports the vision of networked machines in decentralized production plants across the value chain. Hence, it requires highly connected partners exchanging relevant data about products, processes, and production resources. This paper proposes the usage of data spaces and digital twins to enable this Industry 4.0 vision and investigates the building blocks to realize a data space for Industry 4.0, e.g., the integration of digital twins inside the data space based upon the latest specification of the Industry 4.0 Asset Administration Shell. A prototypical implementation shows the feasibility of storing product carbon footprints inside a digital twin and sharing it over a data space with other partners. [ABSTRACT FROM AUTHOR]

Copyright of Cyber Security & Data Governance is the property of Editorial Office of Information Technology & Network Security 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.)

Platform-based manufacturing is substantially changing the way production is conceived and performed. Companies do not know who is making their parts, part manufacturers do not necessarily own the machines, and knowledge and decisions cross the borders of firms. This revolutionary approach is already becoming a reality thanks to a wealth of innovations related to manufacturing science as well as information and communication technologies, and novel business models that meet together at the right moment of their evolution. Considering the existing literature and digging into the phenomenon by interviewing decision-makers, the paper reconstructs the roots of these developments, analyzes the challenges posed to the manufacturing sector, focuses on recent challenges and opportunities, and finally delineates visions for the future. [ABSTRACT FROM AUTHOR]

The accuracy of offshore wind power forecasting (OWPF) is the basis for guaranteeing the safe dispatch and economic operation of power systems, which can reduce the technical and economic risks faced by power market participants. Combining the theoretical approaches of data space and deep learning, this study proposes a hybrid short-term OWPF framework based on the integrated consideration of spatial and temporal correlations of regional field clusters in the data space and the 3DTCN-CBAM-LSTM model. Firstly, other offshore wind farms with spatio-temporal correlation with the target offshore wind farm are screened out using the K-means spatial clustering algorithm. The historical power sequences determined to be similar to the target offshore wind farm and feature data of target offshore wind farms are used as inputs to the prediction model to improve the efficiency of the framework's power prediction. Secondly, the short-term power prediction framework of offshore wind power based on the 3DTCN-CBAM-LSTM model is constructed, using the 3DTCN model's ability to capture the spatio-temporal dynamic characteristics of offshore wind farm data to improve the accuracy of the prediction model. The temporal characteristics of the data are strengthened using the LSTM model to improve the stability of the prediction model. Finally, the CBAM mechanism is introduced to combine channel attention and spatial attention to eliminate irrelevant information and capture the spatio-temporal characteristics of the data in a more targeted way, which further improves the accuracy of OWPF. To verify the practicality and reliability of the wind power prediction framework described in this study, the differences between the prediction results of the framework and those of the benchmark model are compared using single-step prediction and multi-step prediction (4, 6, 8, and 12 steps). Compared with the benchmark model (BPNN), the single-step prediction results show that the MAE, MAPE, and MSE metrics of the 3DTCN-CBAM-LSTM model are reduced by 20.004 MW, 50.13%, 55.61%, and 80.29%, respectively, and the R2 is improved by 3.39%, which is a substantial improvement in prediction performance. The multi-step prediction results show that, with the progression of the prediction steps, the 3DTCN-CBAM-LSTM model has the highest prediction accuracy and model fit at each step. Therefore, the proposed 3DTCN-CBAM-LSTM model presents obvious advantages and reliability in the prediction of offshore wind power multi-step power. • A data-driven model considering the spatio-temporal correlation is adopted for OWPF. • K-means spatial clustering algorithm is used to screen offshore wind farms. • A 3DTCN is used to capture spatio-temporal dynamic properties of offshore wind farm data. • The CBAM is introduced to improve the model's ability to capture the spatio-temporal features of offshore wind power data. • The superiority of the proposed prediction method is demonstrated using single-step and multi-step predictions. [ABSTRACT FROM AUTHOR]

The definition of a tourism data space (TDS) in the Balearic Islands is a complex process that involves identifying the types of questions to be addressed, including analytical tools, and determining the type of information to be incorporated. This study delves into the functional requirements of a Balearic Islands’ TDS based on the study of scientific research carried out in the field of tourism in the Balearic Islands and drawing comparisons with international scientific research in the field of tourism information. Utilizing a bibliometric analysis of the scientific literature, this study identifies the scientific requirements that should be met for the development of a robust, rigorous, and efficient TDS. The goal is to support excellent scientific research in tourism and facilitate the transfer of research results to the productive sector to maintain and improve the competitiveness of the Balearic Islands as a tourist destination. The results of the analysis provide a structured framework for the construction of the Balearic Islands’ TDS, outlining objectives, methods to be implemented, and information to be considered.

In recent years, numerous new approaches that rely on data-intensive methods have been developed for maritime assistance systems, leading to a compelling need for more elaborate verification and validation procedures. Modern testbeds that can meet these demands are often developed separately from the system itself and provided as generically usable services. However, the joint usage of such testbeds by multiple stakeholders from research and industry confronts them with various challenges in terms of data management: Data control and protection is required to preserve possible competitive advantages or comply with legal framework conditions. The resulting decentralization in data management complicates collaboration, especially in the joint processing and analysis of testbed data. In this paper, we present a decentralized software system, which can deal with these challenges by modelling interrelationships between the stakeholders in a data space, considering their various interests. With the help of a modular data management architecture, the organization of a testbed data basis, as well as the support of verification and validation processes and the evaluation of data streams is made possible. This is achieved with a workflow model for mapping complex and distributed data processing steps. We demonstrate the applicability of the system in an application scenario for the development of a maritime assistance system.

Copyright of Science Technology & Industry / Keji he Chanye is the property of Chinese Society of Technology Economics 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.)

Sharing observational and interventional health data within a common data space enables university hospitals to leverage such data for biomedical discovery and moving towards a learning health system. Objective: To describe the AP-HP Health Data Space (AHDS) and the IT services supporting piloting, research, innovation and patient care. Methods: Built on three pillars – governance and ethics, technology and valorization – the AHDS and its major component, the Clinical Data Warehouse (CDW) have been developed since 2015. Results: The AP-HP CDW has been made available at scale to AP-HP both healthcare professionals and public or private partners in January 2017. Supported by an institutional secured and high-performance cloud and an ecosystem of tools, mostly open source, the AHDS integrates a large amount of massive healthcare data collected during care and research activities. As of December 2021, the AHDS operates the electronic data capture for almost +840 clinical trials sponsored by AP-HP, the CDW is enabling the processing of health data from more than 11 million patients and generated +200 secondary data marts from IRB authorized research projects. During the Covid-19 pandemic, AHDS has had to evolve quickly to support administrative professionals and caregivers heavily involved in the reorganization of both patient care and biomedical research. Conclusion: The AP-HP Data Space is a key facilitator for data-driven evidence generation and making the health system more efficient and personalized. [ABSTRACT FROM AUTHOR]

Key Research Areas (KRAs) were identified to establish a semantic interoperability framework for intensive medicine data in Europe. These include assessing common data model value, ensuring smooth data interoperability, supporting data standardization for efficient dataset use, and defining anonymization requirements to balance data protection and innovation. [ABSTRACT FROM AUTHOR]

In Germany there is much agreement on a necessary step towards convergence in the domain of digital entities across sectors given the increasing number of emerging data spaces in research, industry and public services. Therefore a group of FAIR Digital Objects (FDO) experts is working on a proposal that will 1. demonstrate the functionality of FDOs, their added value and the scalability of their components, 2. establish an active FDO community across sectors and disciplines collaborating beyond the project, 3. establish a network of key persons promoting and advancing the FDO standard and its applications in collaboration with the FDO Forum, 4. implement a set of FDO applications for selected use cases from economy and applied research, 5. advancing the further development of FDO specifications and their transformation to international standards and 6. support the international FDO Forum initiative. In addition to the standard activities such as PR, outreach, organising meetings, management etc. the project is designed to support a few major pillars: • A basic infrastructure for FDOs will be made professional and usable by everyone interested based on what has already been developed in the realm of the FDO Forum. • Three use cases will be implemented to serve as demonstrators of FDOs: (a) A testbed of a set of repositories from research and industry including those that are applying standards developed in industrial initiatives such as Industry 4.0 and Int. Data Space Association. (b) Processes in time will be modelled with FDOs to demonstrate the secure mechanisms provided by FDOs. (c) Time will be spent on implementing methods where FDOs help to organise the huge data space as it will emerge in future. These use cases need to be worked out in the 3-year project that will act as demonstrators across borders. • Three research motivated use cases will be tackled as well. The collaboratiing experts believe that it is important already now to introduce quantum computing and their possible impact on data spaces. The group iof experts also wants to not only implement FDO applications but also wants to investigate the foundations of FDOs. In addition, two concrete cases have been selected to demonstrate the value of FDOs (cancer database, tomography imaging). • A Thinktank is planned to discuss many upcoming aspects related to FDOs and these large data spaces such as legal and ethical aspects, philosophy of information, social impact of digital transformation, codification of roles and usage scenarios, etc. • Other goals of the project are to further develop the FDO specifications in close collaboration with the FDO Forum based on the insights from the implementations, transform the specifications to international standards, and set up certification mechanisms. These activities will be led by standardisation organisations (DIN, DKE) which are embedded in ISO/IEC groups. It is an explicit wish of the funding institution for FDOs to help to create a global integrated data space which requires a close collaboration with existing industrial initiatives such as Big Data Value Association, Industry 4.0, Int. Data Space Association, Gaia-X etc. Therefore, the project partners are currently in discussion with these initiatives to bring in their expertise and components wherever that makes sense. Industry for example is busy formalising "roles" and "usages" which is not a topic of the FDO Forum. The project partners are in close contact with the German FDO experts already contributing to the FDO Forum discussions to integrate their expertise where possible into the project. Although the project will focus on activities in Germany we will seek to reach out to other European countries and beyond. It is obvious that the project will need to invest in training from the beginning leveraging on the already existing knowledge in the FDO Forum, DONA and ePIC for example. We are currently in discussion with the funding agency in shaping this project with the intention to start it already in 2022. With this project being granted we hope to be able to advance the international attempts to achieve a higher degree of convergence and thus efficiency in the domain of digital objects. [ABSTRACT FROM AUTHOR]

Scientific research involves a statement involving quantities and their values with respect to three different conceptual spaces. There is theoretical space, containing explanatory statements involving non-observable quantities and values. On the whole, theoretical space is concerned with why things happen. There is methodological space, containing statements about what must be done. While methodological space involves what is required to produce a given effect, it also involves aspects related to data analysis and, in the case of psychological research, quantities that are associated with the participants, many of which are both unknown and unmeasured. Data space involves statements concerning what was found, and the particular values obtained from a given study. Using Illusory Line Motion as an example, this article will explore how these ideas interact. We will explore why we see motion when none exists under different circumstances and offer multiple explanations for illusory line motion. We view these not as alternative explanations for a common illusion but as independent explanations for different illusions. [ABSTRACT FROM AUTHOR]

Abstract Background We previously reported on CERENKOV, an approach for identifying regulatory single nucleotide polymorphisms (rSNPs) that is based on 246 annotation features. CERENKOV uses the xgboost classifier and is designed to be used to find causal noncoding SNPs in loci identified by genome-wide association studies (GWAS). We reported that CERENKOV has state-of-the-art performance (by two traditional measures and a novel GWAS-oriented measure, AVGRANK) in a comparison to nine other tools for identifying functional noncoding SNPs, using a comprehensive reference SNP set (OSU17, 15,331 SNPs). Given that SNPs are grouped within loci in the reference SNP set and given the importance of the data-space manifold geometry for machine-learning model selection, we hypothesized that within-locus inter-SNP distances would have class-based distributional biases that could be exploited to improve rSNP recognition accuracy. We thus defined an intralocus SNP “radius” as the average data-space distance from a SNP to the other intralocus neighbors, and explored radius likelihoods for five distance measures. Results We expanded the set of reference SNPs to 39,083 (the OSU18 set) and extracted CERENKOV SNP feature data. We computed radius empirical likelihoods and likelihood densities for rSNPs and control SNPs, and found significant likelihood differences between rSNPs and control SNPs. We fit parametric models of likelihood distributions for five different distance measures to obtain ten log-likelihood features that we combined with the 248-dimensional CERENKOV feature matrix. On the OSU18 SNP set, we measured the classification accuracy of CERENKOV with and without the new distance-based features, and found that the addition of distance-based features significantly improves rSNP recognition performance as measured by AUPVR, AUROC, and AVGRANK. Along with feature data for the OSU18 set, the software code for extracting the base feature matrix, estimating ten distance-based likelihood ratio features, and scoring candidate causal SNPs, are released as open-source software CERENKOV2. Conclusions Accounting for the locus-specific geometry of SNPs in data-space significantly improved the accuracy with which noncoding rSNPs can be computationally identified.