Data Management
1
2
3
4
5
Download this chapter (PDF)
Increased application of FAIR principles to enable digital ecosystems in Europe

The FAIR principles are guidelines on datasets being findable, accessible, interoperable, and reusable. Increased application of FAIR principles may lead to digital ecosystems, a web of FAIR data and services, where digital resources are not only shared but also semantically linked, automatically interpreted, and reliably reused across domains. Beyond data, a key evolution is the application of FAIR principles to all research outputs, including supporting frameworks such as software, computational workflows, and scientific models. This convergence is facilitated by FAIR Digital Objects (FDOs), which include persistent identifiers and rich metadata, alongside Knowledge Graphs that structure the semantic relationships between these FDOs. The shift to applying FAIR beyond just ‘data only’, to include digital research objects, enables automation, reproducibility, and information discovery, while also fostering cross-domain innovation on a larger scale.

Maturity

WATCH
PLAN
ACT
Show the other PLAN trends
Changing dynamics in Responsible AI From large to small language models AR and smart glasses become more prominent Increasing AI-enhanced user experience Growing big tech competition for the XR stack Increased application of FAIR principles to enable digital ecosystems in Europe Growing relevance of TRUST principles for data repositories to secure data More decentralisation of data ownership enabled by SSI solutions Emergence of organisational wallets Push for transparent supply chains with digital product passports Bringing computing resources closer to the ‘edge’ Growing need for automated cloud and edge resource management Growing emphasis on neuromorphic (brain-inspired) computing Integrating photonics for accelerated information processing The battle for airwaves AI/Ops, digital twinning and the emergence of the LLM in network operations Power usage and ecological footprint From ad hoc experiments to applications Towards Fault-Tolerant Quantum Computing (FTQC) Cyber-physical technology calls for resilient minds A growing urge for chip sovereignty in Europe

Drivers

Impact

education

Education

  • Knowledge Graphs (KGs) are being integrated into Learning Management Systems (LMSs) such as Brightspace and Canvas to model relationships between learning objectives, content, and competencies.
  • Fostering links between learner profiles, learning progress, and course requirements, KGs enable personalised learning through adaptive learning paths.
Research

Research

  • The web of FAIR data and services significantly impacts research by accelerating scientific discovery and promoting reusability.
  • FAIR Digital Objects (FDOs), enriched with fine-grained metadata and versioning, enhance the reproducibility, traceability, and reuse of research data, software, and workflows.
Operations

Operations

  • Knowledge Graphs (KGs) can enhance operational systems by linking data from Learning Management Systems (LMSs) and Student Information Systems (SIS).
  • The integration of KGs enables institutions to map the student journey from pre-admission to graduation and subsequent alumni engagement.
More info about Data Management?
Visit surf.nl
Link SURF icoon