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Written on 01 May 2026.

Ship Data, Ontologies, Knowledge Graphs to Digital Twin

The figure illustrates how sensor data from an ocean-going vessel can be transformed into a digital twin through a structured data-modelling process. It shows that data collected from onboard systems is first organized using ontologies, which provide a shared semantic structure. These ontologies then support the creation of knowledge graphs that connect vessel systems, components, sensor observations, and operational context. Through this representation, the vessel can be modelled as a system of systems, forming the foundation for developing a digital twin structure for ocean-going vessels.

infographic

Written on 01 May 2026.

Sensor Data to Ship Digital Twin

This video demonstrates how sensor data from an ocean-going vessel can be connected through ontologies, thereby forming the foundation for knowledge graphs. A knowledge graph representation enables the vessel to be modelled as a system of systems (SoS), capturing the relationships among its subsystems, components, and operational data. This information makes the digital twin architecture for ocean-going vessels, which represents one of the main contributions of the TwinShip Horizon Europe project. The digital twin of the TwinShip futuristic vessel with net-zero emissions and autonomous navigation is presented in this video.

video

Written on 30 April 2026.

TwinShip in Data Week 2026

TwinShip Consortium Horizon Europe project will be presented at the Data Week, 6th of May, 2026 in Oslo, Norway, at the session: "Trusted maritime digital twins: Data management and AI compliance from ship to shore'' and you are welcome to attend and talk with the project members.

https://data-week.eu/2026-edition/programme/

workshop

Written on 24 April 2026.

Data Quality Governance Strategy

🚢 Data quality is the foundation of trustworthy maritime digital twins.

As shipping becomes increasingly digitalized, vessels generate large volumes of operational and navigation data that can support condition monitoring, fuel efficiency improvement, route optimization, and digital twin development. But this data is often affected by missing values, sensor faults, repeated readings, measurement noise, and inconsistent records.

In our latest deliverable, the Initial Data Quality Governance Strategy (DQGS), we present a structured approach for improving the quality of maritime datasets before and during digital twin development. The deliverable can be downloaded from: the Data Quality Governance Strategy (DQGS).

The DQGS is built around a layered framework that combines:

✅ Visual data evaluation
✅ Rule-based preprocessing using domain knowledge
✅ Metadata-driven quality checks
✅ Statistical and structure-based anomaly detection
✅ Digital-twin-supported anomaly detection and data recovery

A key principle of the framework is that data should be treated as an asset, not simply discarded. Instead of removing all anomalous or incomplete data, the approach focuses on identification, isolation, and recovery, helping preserve valuable operational information while improving model reliability.

The report also compares several preprocessing strategies, from strict structural cleaning and multivariate outlier filtering to row-preserving imputation and targeted cleaning of key performance variables. This provides flexibility when selecting the most suitable dataset for future modelling and analysis.

By integrating data governance directly into the digital twin workflow, the framework supports more reliable, traceable, and continuously improving maritime data-driven systems.

This work contributes to the development of trustworthy digital twins for cleaner, safer, and more energy-efficient shipping. 🌊⚓

deliverables

Written on 31 March 2026.

VesselAI Digital Platform Delivered.

🚢 Excited to highlight the VesselAI platform - an open-source maritime analytics platform designed to support the full data and innovation lifecycle in shipping.

📊 VesselAI brings together data ingestion, storage, exploration, SQL analytics, ETL, AI services, workflow orchestration, and digital twin capabilities in one integrated environment. This allows users to move from raw maritime data to actionable insights for vessel performance analysis, operational optimization, and environmental and economic assessment.

🤖 The platform supports advanced AI and machine learning workflows, including notebook environments, AI models, AI agents, and workflow management tools, while also enabling digital twin applications such as vessel and retrofit analysis, voyage planning, and optimization.

🔐 In a sector where access to maritime data is often limited, VesselAI helps enable more open, secure, and collaborative innovation through federated data sharing, encryption mechanisms, and semantic interoperability across systems.

🌍 By supporting academia, research, and industry stakeholders, VesselAI contributes to a stronger digital ecosystem for smarter, greener, and more efficient shipping.