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Data spaces are conceptualised as a trusted and secure distributed data ecosystem through which to exchange resources in the Web. Several efforts define guidance toward data space implementation, such as reference architectures and frameworks. As yet, the proposed data space solutions do not provide common and mature implementation options yet, and this gap between concept and implementation risks confusing users and developers. However, well-recognised organisations have been developing solutions and standards that address interoperability and good data exchange practices for decades, especially in the domain of geospatial information and remote sensing. Therefore, this paper compares the available solutions, providing the mapping and integration of the proposed blueprints to available interoperable standards. This concrete mapping, followed by a discussion with experts, results in a proposal of integrated reference data space building blocks, and an overview of the related standards and solutions. It is designed to support the effective practical implementation of data spaces and to guide future solution developments. This work can form the base for effective collaboration among different organisations, clearly identifying their scopes. A key role is apparent for standards and use cases from the remote sensing and geospatial domains, which have achieved wide adoption and maturity over the past years. [ABSTRACT FROM AUTHOR]

According to the European Data Strategy established in 2020, the European Open Science Cloud (EOSC) is described as "the basis for a science, research and innovation data space... and will be connected and articulated with the sectoral data spaces", being one of the 14 European Common Data Spaces. While current cross-sectoral interactions with the EOSC are realised through the association's Health Data Task Force, advancements in other EU priorities, such as the Green Deal and the Digital Strategy, should be accelerated in the green and digital transitions and integrated by reinforcing each other to be climate-neutral by 2050. With this motivation, this paper systematically reviews data spaces and digital twins (DTs) within the context of research and innovation. Specifically, focusing on the relevance of the EOSC to the Green Deal Data Space (GDDS) and DTs of the Earth, the relationship between them is explored using a topic search with various keyword combinations in the Web of Science and CORDIS databases. Based on the selected scientific articles and projects, collaboration opportunities are mapped to connect relevant stakeholders. Furthermore, existing and developing service components that could contribute to technical building blocks for the GDDS are identified. In summary, key findings are highlighted, addressing the current gaps and opportunities among the GDDS initiatives presented in this review. [ABSTRACT FROM AUTHOR]

Copyright of Uporabna Informatika is the property of Slovensko Drustvo Informatika 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.)

The rapidly evolving field of mobility data spaces, integral to the contemporary technological landscape, creates unique challenges and opportunities in the context of legal compliance. This article argues that the EU Horizon MobiSpaces project can serve as an instructive example of how these challenges might be successfully navigated within a current operational and research environment. The article aims to explore the nuanced legal frameworks that govern mobility data space projects, focusing mainly on data protection, data security, and the implications of artificial intelligence. The primary objective of this article is to describe a practical example of data protection and security by design built on a technologically robust architecture. By doing so, it endeavors to ensure that these tools are not only technologically advanced but also aligned with ongoing legal principles across multiple fields of technology law. This alignment is critical for fostering innovation that is responsible, compliant, and better aligned to societal values and legal norms. Through an in-depth analysis of MobiSpaces, the article aims to offer valuable insights that could be instrumental for similar technology-driven projects related to data mobility in related settings. [ABSTRACT FROM AUTHOR]

One of the key challenges of data management for smart manufacturing is dealing with data originating from both physical processes and virtual digital technologies. As image and sensor‐based production monitoring deliver a wealth of data along the process chain, artificial intelligence (AI) enables enhanced data analysis and new insight regarding relevance of observed process deviations. With constantly increased availability of data from manifold and specific sources, the complexity and heterogeneity of information structures are also growing rapidly. This is especially true for highly variable research, scale‐up, and pilot production, which poses new demands on data acquisition, data management, and data preprocessing. Herein, a unified framework for integrating an ontology and graph‐based data space with data acquisition and data analytics to improve data consistency, documentation of workflows, as well as the reproducibility of observations and results is presented. The framework consists of several open‐source web services that form an ontology‐based data space where physical and virtual process chains are represented by a semantic data fabric built from findable, accessible, interoperable, and reusable resource descriptions framework self‐descriptions. The feasibility of the proposed framework is demonstrated for a laboratory‐scale Li‐ion battery cell production facility with AI applied to two data analytics use cases. [ABSTRACT FROM AUTHOR]

Data sharing has facilitated the digitisation of society. We can access our bank accounts or make an appointment with our doctor anytime and anywhere. To achieve this, we have to share certain information, whether personal, professional, etc. This may seem like a minor cost for an individual user, but actually the data economy as the backbone of a digital transformation that is reshaping all aspects of human life. However, one of the major concerns arises regarding what happens to such individual data; once shared, control over it is often lost. For that reason, users and companies are reluctant to share their data. The European Union, through its European Strategy for Data, is establishing a policy and legal framework for establishing a single market for data in Europe by improving the trust and fairness of the data economy. Data spaces are a commitment to sharing data in a reliable and secure way, but this endeavour should, of course, not be at the expense of privacy rights. In recent years, Privacy-Enhancing Technologies (PETs) have emerged to achieve data sharing and privacy preservation that can address the requirements of data spaces around sensitive citizen and business data. In this work, we review existing PETs and assess their relevance, technological maturity, and applicability in the context of common European data spaces. Finally, we illustrate the benefits of secure data sharing via Federated Learning in a healthcare use case, where the preservation of privacy is a primer requirement and is therefore to be guaranteed.

This paper presents the approach adopted by the EGI-ACE project for the setup and delivery of Data Spaces for various scientific domains. The work was implemented by members of the EGI e-infrastructure and of several European Research Infrastructures in the context of the European Open Science Cloud programme. Our results are several Data Space services that enable the reuse and exploitation of open, scientific big data for compute intensive use cases. The paper illustrates the EGI-ACE approach through two examples: (1) EMSO ERIC Data Portal for seafloor and water column research and (2) ENES Data Space for climate research.

Data spaces are conceptualised as a trusted and secure distributed data ecosystem through which to exchange resources in the Web. Several efforts define guidance toward data space implementation, such as reference architectures and frameworks. As yet, the proposed data space solutions do not provide common and mature implementation options yet, and this gap between concept and implementation risks confusing users and developers. However, well-recognised organisations have been developing solutions and standards that address interoperability and good data exchange practices for decades, especially in the domain of geospatial information and remote sensing. Therefore, this paper compares the available solutions, providing the mapping and integration of the proposed blueprints to available interoperable standards. This concrete mapping, followed by a discussion with experts, results in a proposal of integrated reference data space building blocks, and an overview of the related standards and solutions. It is designed to support the effective practical implementation of data spaces and to guide future solution developments. This work can form the base for effective collaboration among different organisations, clearly identifying their scopes. A key role is apparent for standards and use cases from the remote sensing and geospatial domains, which have achieved wide adoption and maturity over the past years.

According to the European Data Strategy established in 2020, the European Open Science Cloud (EOSC) is described as “the basis for a science, research and innovation data space… and will be connected and articulated with the sectoral data spaces”, being one of the 14 European Common Data Spaces. While current cross-sectoral interactions with the EOSC are realised through the association’s Health Data Task Force, advancements in other EU priorities, such as the Green Deal and the Digital Strategy, should be accelerated in the green and digital transitions and integrated by reinforcing each other to be climate-neutral by 2050. With this motivation, this paper systematically reviews data spaces and digital twins (DTs) within the context of research and innovation. Specifically, focusing on the relevance of the EOSC to the Green Deal Data Space (GDDS) and DTs of the Earth, the relationship between them is explored using a topic search with various keyword combinations in the Web of Science and CORDIS databases. Based on the selected scientific articles and projects, collaboration opportunities are mapped to connect relevant stakeholders. Furthermore, existing and developing service components that could contribute to technical building blocks for the GDDS are identified. In summary, key findings are highlighted, addressing the current gaps and opportunities among the GDDS initiatives presented in this review.

The development of a data-driven digital economy in the EU is based on data-sharing. However, the current scenario is marked by fragmented data silos and big tech dominance, hindering data interoperability in the Digital Single Market. Regulatory efforts have begun addressing this by reducing platform data control and enhancing user empowerment, promoting data portability rights in different legal instruments. Nevertheless, a unified data-sharing infrastructure is essential for the efficacy of these measures. Data spaces are anticipated to provide the needed technical framework, supported by emerging legal regulations to ensure free information movement through this infrastructure. This development raises the question of whether a new EU fundamental freedom is being established, specifically for data. The existing four freedoms – movement of goods, capital, services and people – have been instrumental in shaping the Digital Single Market. However, the progression of societal digitalisation might necessitate a distinct data-centric freedom. This article examines whether the EU’s strategies and regulations are evolving towards a fifth fundamental freedom for data, potentially acting as a cohesive force for various data-related initiatives. [ABSTRACT FROM AUTHOR]

Copyright of Yearbook of Antitrust & Regulatory Studies is the property of University of Warsaw 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.)

In the tourism sector, access, use and exchange of data is key due to the potential of the data economy, and because the vocation of a regional organization such as the European Union is precisely the internal market. And this is so because the free movement of production factors also includes data, whether personal, non-personal or mixed. The need to guarantee the free circulation of data, through the European data spaces, obliges us to consider the legal perspective, having to assess the existence of tourist, personal, non-personal or mixed data, as well as high value data, all of this around principles such as data sovereignty, privacy, security, interoperability and proportionality. This article analyzes all these issues, while proposing the design and construction of an architecture for tourism data spaces.

The EU data economy's growth hinges on access to extensive data, with open banking initiatives encouraging data sharing in the financial services industry. Artificial intelligence (AI)-driven tools play a vital role in harnessing the potential of this data, but training these systems carries inherent risks, including the introduction of new or the perpetuation of existing biases. This article examines the role of the European Financial Data Space (EFDS) in AI training, using consumer creditworthiness evaluation as a case study. It investigates whether the EFDS facilitates responsible AI development, considering data protection concerns like legal basis, transparency and control. The objective is to enhance the regulatory proposal for the EFDS, ensuring a well-founded legal framework for utilizing AI responsibly in the financial sector within the broader context of European Data Spaces. [ABSTRACT FROM AUTHOR]

Copyright of Uporabna Informatika is the property of Slovensko Drustvo Informatika 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.)

This paper presents the approach adopted by the EGI-ACE project for the setup and delivery of Data Spaces for various scientific domains. The work was implemented by members of the EGI e-infrastructure and of several European Research Infrastructures in the context of the European Open Science Cloud programme. Our results are several Data Space services that enable the reuse and exploitation of open, scientific big data for compute intensive use cases. The paper illustrates the EGI-ACE approach through two examples: (1) EMSO ERIC Data Portal for seafloor and water column research and (2) ENES Data Space for climate research., Competing Interests: No competing interests were disclosed., (Copyright: © 2024 Sipos G et al.)

Data sharing has facilitated the digitisation of society. We can access our bank accounts or make an appointment with our doctor anytime and anywhere. To achieve this, we have to share certain information, whether personal, professional, etc. This may seem like a minor cost for an individual user, but actually the data economy as the backbone of a digital transformation that is reshaping all aspects of human life. However, one of the major concerns arises regarding what happens to such individual data; once shared, control over it is often lost. For that reason, users and companies are reluctant to share their data. The European Union, through its European Strategy for Data, is establishing a policy and legal framework for establishing a single market for data in Europe by improving the trust and fairness of the data economy. Data spaces are a commitment to sharing data in a reliable and secure way, but this endeavour should, of course, not be at the expense of privacy rights. In recent years, Privacy-Enhancing Technologies (PETs) have emerged to achieve data sharing and privacy preservation that can address the requirements of data spaces around sensitive citizen and business data. In this work, we review existing PETs and assess their relevance, technological maturity, and applicability in the context of common European data spaces. Finally, we illustrate the benefits of secure data sharing via Federated Learning in a healthcare use case, where the preservation of privacy is a primer requirement and is therefore to be guaranteed., (© 2024 The Author(s).)