We are pleased to present the second version of the annually updated TwinShip Data Management Plan, which defines how data will be collected, generated, processed, stored, shared, and protected throughout the project. The first version is available on ResearchGate. 

📊 A comprehensive project data ecosystem

TwinShip is expected to manage approximately 5–6 TB of data, depending on the vessel type, operational route, and data-collection activities. The datasets are organised into five main categories:

🚢 Ship performance data
📈 Business constraints data
🌱 Green fuel and technology data
⚓ Online port data
🌦️ Weather data

These datasets are fundamental to developing and validating TwinShip’s digital-twin technologies and achieving the project’s research and innovation objectives.

🔓 Open science with responsible data governance

TwinShip aims to make project data publicly available whenever possible. However, datasets containing commercially sensitive or business-confidential information will remain protected, based on decisions made by the project consortium.

💻 Project-generated software will be released as open source.
📚 Scientific publications will be made available through open-access channels.
🔎 Supporting the FAIR principles

All project data will be managed according to the FAIR principles:

✅ Findable: Supported by globally unique and persistent identifiers.
✅ Accessible: Available through standardised communication protocols, with session- and API-key-based authentication and authorisation.
✅ Interoperable: Structured using recognised vocabularies and ontologies, including the VesselAI ontology.
✅ Reusable: Documented and governed to support future scientific, industrial, and technological applications.

☁️ The datasets will be hosted through the VesselAI platform, while data security will be maintained through encryption, access controls, data-space restrictions, and auditing mechanisms.

The Data Management Plan follows the European Commission’s Guidelines on FAIR Data Management in Horizon 2020, supporting responsible research, open science, and secure data sharing.