kNOWLEDGE HUB
Unlocking Wisdom, One File at a Time
Newsletter #1
Our 1st newsletter edition, introducing you to the AI-DAPT concept. May 2024
Newsletter #2
Our 2nd newsletter edition, introducing our four real-life demonstrators and research agenda. January 2025
Newsletter #3
About platform’s technical implementation, Reference Architecture, and upcoming participation at ADRF25.
Leaflet #1
Leaflet about AI-DAPT, presenting its main pilots, research agenda and concept.
Effector: A Python package for regional explanations
Effector is a Python library for analyzing regional feature effects, helping interpret models by identifying where feature impacts vary across subgroups.
Fast and Accurate Regional Effect Plots for Automated Tabular Data Analysis
r-RHALE is a fast explainability method that shows how feature effects vary across subgroups in tabular data, even with large datasets and complex models.
Big Data Visualisation in the Maritime Industry
VesselAI reviews big data tools for maritime needs and shows how Apache Superset can visualize AIS data, routes, and weather to support decision-making.
Utilizing large-scale human mobility data to identify determinants of physical activity
Using mobility data from 20M+ people, this study reveals how visits to places like cafes, parks, and grocery stores relate to fitness habits.
Bridging Data and AIOps for Future AI Advancements with Human-in-the-loop.
The AI-DAPT Concept
AI-DAPT uses synthetic data and hybrid ML to build trustworthy AI for healthcare, robotics, energy, and manufacturing.
Data Ingestion and Harmonisation for the Maritime Domain
Maritime data is diverse and complex. A new service uses NLP to unify raw maritime data into a common format, enabling better AI and machine learning applications—no coding required.
D1.1 Automated AI Pipeline End User Needs and Scientific Technology Radar
D1.1 defines AI-DAPT’s user needs, ethical principles, and tech focus areas—like XAI, synthetic data, and hybrid models—based on input from its four real-world pilots.
D1.2 Automated AI Pipeline Design & Technical Requirements
Outlines AI-DAPT’s technical needs, user roles, and system workflows, forming the foundation for platform architecture.
D6.1 Dissemination Communication Engagement Exploitation
Details AI-DAPT’s communication and engagement plan, including key channels, early outreach, and target audiences.
D7.1 Project Management Handbook
The handbook guides AI-DAPT’s management, detailing roles, timelines, and processes to develop a trustworthy, automated AI framework combining data, models, and human input.
D7.2 ARGOS DMP
The AI-DAPT DMP defines how project data is managed using FAIR principles, ensuring security, accessibility, and ethical handling across health, robotics, and energy demonstrators.
D7.2 Data Management Plan
The DMP outlines how AI-DAPT manages data ethically and securely, following FAIR principles, across its health, robotics, energy, and manufacturing use cases.
Newsletter #1
Our 1st newsletter edition, introducing you to the AI-DAPT concept. May 2024
Newsletter #2
Our 2nd newsletter edition, introducing our four real-life demonstrators and research agenda. January 2025
Newsletter #3
About platform’s technical implementation, Reference Architecture, and upcoming participation at ADRF25.
D1.1 Automated AI Pipeline End User Needs and Scientific Technology Radar
D1.1 defines AI-DAPT’s user needs, ethical principles, and tech focus areas—like XAI, synthetic data, and hybrid models—based on input from its four real-world pilots.
D1.2 Automated AI Pipeline Design & Technical Requirements
Outlines AI-DAPT’s technical needs, user roles, and system workflows, forming the foundation for platform architecture.
D6.1 Dissemination Communication Engagement Exploitation
Details AI-DAPT’s communication and engagement plan, including key channels, early outreach, and target audiences.
D7.1 Project Management Handbook
The handbook guides AI-DAPT’s management, detailing roles, timelines, and processes to develop a trustworthy, automated AI framework combining data, models, and human input.
D7.2 ARGOS DMP
The AI-DAPT DMP defines how project data is managed using FAIR principles, ensuring security, accessibility, and ethical handling across health, robotics, and energy demonstrators.
D7.2 Data Management Plan
The DMP outlines how AI-DAPT manages data ethically and securely, following FAIR principles, across its health, robotics, energy, and manufacturing use cases.
Effector: A Python package for regional explanations
Effector is a Python library for analyzing regional feature effects, helping interpret models by identifying where feature impacts vary across subgroups.
Fast and Accurate Regional Effect Plots for Automated Tabular Data Analysis
r-RHALE is a fast explainability method that shows how feature effects vary across subgroups in tabular data, even with large datasets and complex models.
Big Data Visualisation in the Maritime Industry
VesselAI reviews big data tools for maritime needs and shows how Apache Superset can visualize AIS data, routes, and weather to support decision-making.
Utilizing large-scale human mobility data to identify determinants of physical activity
Using mobility data from 20M+ people, this study reveals how visits to places like cafes, parks, and grocery stores relate to fitness habits.
Bridging Data and AIOps for Future AI Advancements with Human-in-the-loop.
The AI-DAPT Concept
AI-DAPT uses synthetic data and hybrid ML to build trustworthy AI for healthcare, robotics, energy, and manufacturing.
Data Ingestion and Harmonisation for the Maritime Domain
Maritime data is diverse and complex. A new service uses NLP to unify raw maritime data into a common format, enabling better AI and machine learning applications—no coding required.
Leaflet #1
Leaflet about AI-DAPT, presenting its main pilots, research agenda and concept.