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Issues And Trends In Healthcare Delivery System

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Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
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Leveraging technologies for data management and sharing to foster collaboration and implement data spaces.

Ioannis Chrysakis1,2,3, Gerasimos Antzoulatos4, Luis Sánchez5

  • 1Netcompany S.A., Research and Innovation Development Department (RID), Luxembourg.

Data in Brief
|May 13, 2026
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Summary

This study outlines requirements for European data spaces, focusing on data management and sharing challenges. It provides best practice recommendations for scalable, cross-domain data spaces supporting sustainable innovation.

Keywords:
Data ecosystemsData managementData sharingData spaces

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Area of Science:

  • Data Science
  • Information Systems
  • European Policy

Background:

  • The European Strategy for Data promotes Common European Data Spaces to foster economic growth and data utilization.
  • Data spaces are crucial for strategic sectors, requiring robust data management and sharing capabilities.
  • Despite initiatives, data spaces face significant development and implementation challenges.

Purpose of the Study:

  • To identify essential requirements for effective data management and sharing within European data spaces.
  • To develop a conceptual framework for applying data management and sharing principles to data spaces.
  • To evaluate practical solutions and identify best practices through analysis of European-funded projects.

Main Methods:

  • Literature review to establish a conceptual framework for data management and sharing in data spaces.
  • Analysis of six representative European-funded projects to assess practical implementation of proposed solutions.
  • Evaluation of projects based on requirements including trust, business models, interoperability, workflow orchestration, energy efficiency, and data quality.

Main Results:

  • Identified key requirements and prominent issues in data space development, covering trust, interoperability, and data quality.
  • Highlighted widely accepted strategies like semantic standards, data catalogues, distributed ledger technologies, and federated identity management.
  • Recognized recurring patterns, common practices, and implementation differences across projects, alongside open research gaps.

Conclusions:

  • The study provides concrete recommendations for researchers and practitioners to achieve best practices in data space implementation.
  • It aims to inform future initiatives towards scalable, stable, and cross-domain data spaces.
  • The findings support sustainable innovation and long-term collaboration across Europe through mature data space ecosystems.