EFRA Infrastructure

EFRA aims to conduct ground-breaking research and develop novel technology to deliver an operational data and analytics platform especially for food safety risk prevention (the first of its kind in Europe).

Multilingual Innovation

EFRA will facilitate the collection, anonymization, validation, structuring and modelling of diverse, multilingual data sets from around the world that can help better understand, foresee and mitigate risks in the food supply chain.

The EFRA Platform

A cloud-based, Green, High Performance Computing (HPC) platform composed by three tools:



A data platform able to search, mine, process, annotate, and link dispersed, heterogeneous, and deep/hidden food safety data sources.

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An analytics and AI platform with an extensible set of modules running over a green cloud High Performance Computing (HPC) able to distil useful insights & signals from the EFRA Data Hub, and train green AI models for food risk predictions.

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A front-facing user-friendly web app that allows interested users to discover, purchase/use, and contribute with raw data, AI models, and analytics modules, thus creating an economy where data holders and data consumers can engage and trade.

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Project Objectives

EFRA Data Hub

Τhe main goal of the EFRA Data Hub is to collect data from heterogenous, multilingual, dispersed public data sources across the web such as.

The EFRA Data Hub offers a set of linking & annotation tools that experts can use to annotate the extracted data. Both annotated and linked data are ready for further processing and uptake by the EFRA Analytics Powerhouse.

Project Objectives

EFRA Analytics Powerhouse

The main goal of the EFRA Analytics Powerhouse is to distil usable signals from the data gathered in the EFRA Data Hub and train holistic AI models able to predict food safety risks. 

For this purpose an extensible set of Natural Language Understanding (NLU) micro-modules are deployed over specific parts of the annotated & linked data. 

Tailored and optimised NLU-micro modules for specific types of data able to identify, understand and categorise potential emerging or ongoing Food Risk situations.

Utilisation of multiple NLU micro-modules will be intelligently deployed on the same data item to extract multiple useful signals and distil a higher-level insight. 
“Vectorisation” of noisy and unstructured textual data by direct mapping of NLU micro-modules to parameters used in the AI risk prediction models.


EFRA will pioneer a two-level AI training approach and ensure privacy preservation:

  • 1.

    Training on company premises
  • 2.

    Federated learning across organisations

Supported by the EFRA Analytics Powerhouse through the following processes:

Project Objectives

EFRA Data & Analytics Marketplace

Availability of the EFRA tools and data through the EFRA Data & Analytics Marketplace. A front-facing user-friendly web app to discover, purchase/use, and contribute with raw data, AI models, and analytics modules, where users can engage and trade Faster and Cheaper.

Build upon an API Gateway able to manage the access to the EFRA Data Hub and Analytics Powerhouse. 

Customised APIs for accessing specific parts of data such as:

  • Precise Hazards
  • Countries
  • Types 

and analytics services (e.g., train an AI model over a dataset). 

The EFRA Data & Analytics Marketplace will also allow any interested user to upload datasets, crawlers, AI models, and NLU micro-modules and create a dynamic Food Risk Data Economy. 

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