The strategic goal of e-ROSA is to provide guidance to EU policies by designing and laying the groundwork for a long-term programme aiming at achieving an e-infrastructure for open science in agriculture that would position Europe as a major global player at the forefront of research and innovation in this area. Through a foresight approach, the project will build a shared vision of a future sustainable e-infrastructure for research and education in agriculture and make it operable through pragmatic recommendations that will be reflected in a common roadmap. This will be achieved through a process of co-design, involving mainly research and education communities but also practitioners and EU policy makers, and will build on the existing projects, networks, international alliances or initiatives that the project will systematically map and integrate in the analysis of grand challenges ahead and the identification of priorities and solutions towards an open, digital and data-intensive science in agriculture.

AGINFRA+ addresses the challenge of supporting user-driven design and prototyping of innovative e-infrastructure services and applications. It particularly tries to meet the needs of the scientific and technological communities that work on the multi-disciplinary and multi-domain problems related to agriculture and food. It will use, adapt and evolve existing open e-infrastructure resources and services (AGINFRA, OpenAIRE, EGI, EUDAT, D4Science), in order to demonstrate how fast prototyping and development of innovative data- and computing-intensive applications can take place. AGINFRA+ will evolve and develop further the resources and services of the AGINFRA research data e-infrastructure, which has been developed in the context of the FP7 agINFRA project and is now being operated and evolved by key stakeholders in agriculture and food (including Agroknow, the Food and Agriculture Organisation of the United Nations, INRA, Wageningen UR, the Chinese Academy of Agricultural Sciences and others).

EFRA will explore how extreme data mining, aggregation and analytics may address major scientific, economic and societal challenges associated with the safety and quality of the food that European consumers eat. EFRA’s goals are: i) develop and test solutions to discover and distil food risk data from heterogeneous and dispersed/scarce data sources with minimal delay and appropriate format; ii) design relevant human aspects & interactions with users to measure usefulness for human risk prevention actions in real-world use-cases iii) demonstrate how solutions enable the development of trustworthy, accurate, green and fair AI systems for food risk prevention iv) achieve groundbreaking advances in performance and effectiveness of food risk data discovery, collection, mining, filtering, and processing; v) integrate relevant technologies (big data, IoT, AI) to foster links to food data innovator communities vi) position its contributions into the overall ecosystem of public & private stakeholders that share data, technology and infrastructure to ensure the safety and quality of food in Europe. To achieve these goals, EFRA will design, test, and deploy tools and undertake appropriate initiatives to facilitate their uptake, elicit feedback, and engage stakeholders. The EFRA tools are: (i) EFRA Data Hub, offering intelligent crawlers and data annotation & linking modules to search, mine, process, annotate, and link dispersed, multilingual, heterogeneous, and deep/hidden food safety data sources (ii) EFRA Analytics Powerhouse: offering modules running over a green cloud HPC that distil useful insights & signals from the EFRA Data Hub to train privacy-preserving, explainable, green food risk prediction AI models (iii) EFRA Data & Analytics Marketplace: A front-facing user-friendly web app that allows interested users to discover, purchase/use, and contribute data, AI models, and analytics modules, creating an economy where data holders and data consumers engage and trade.

STELAR will design, develop, evaluate, and showcase an innovative Knowledge Lake Management System (KLMS) to support and facilitate a holistic approach for FAIR (Findable, Accessible, Interoperable, Reusable) and AI-ready (high-quality, reliably labeled) data. The STELAR KLMS will allow to (semi-)automatically turn a raw data lake into a knowledge lake. This is achieved by (1) enhancing the data lake with a knowledge layer, and (2) developing and integrating a set of data management tools and workflows. The knowledge layer will comprise: (a) a data catalog offering automatically enhanced metadata for the raw data assets in the lake, and (b) a knowledge graph that semantically describes and interlinks these data assets using suitable domain ontologies and vocabularies. The provided tools and workflows will offer novel functionalities for: (a) data discovery and quality management; (b) data linking and alignment; and (c) data annotation and synthetic data generation. The KLMS will combine both human-in-the-loop and automatic approaches, to leverage background knowledge of domain experts while minimizing their involvement. To reduce manual effort and time, it will increase the automation of finding and selecting relevant data sources, configuring, and tuning the involved data management tools, and designing, executing, and monitoring end-to-end data processing workflows adapted to different user needs. The KLMS will include specialized tools and functions for geospatial, temporal, and textual data. An organization, ranging from a data-intensive SME to the operator of a data marketplace, will be able to use the STELAR KLMS to increase the readiness of its data assets for use in AI applications and for being shared and exchanged within a common data space. The STELAR KLMS will be pilot tested in diverse, real-world use cases in the agrifood data space, one of the nine data spaces of strategic societal and economic importance identified in the European Strategy for Data.

Big data is becoming a hype that is going to completely redefine industries within very traditional sectors like agriculture, food and beauty. The emergence of niche big data companies like Enolytics (“bringing big data insights to the wine industry”) is threatening to disrupt these industries against the interests of the EU. BigDataGrapes wants to build upon the rich historical, cultural and artisan heritage of Europe in order to change this picture. It aims to support all European companies active in two key industries powered by grapevines: the wine industry and the natural cosmetics one. It will help them respond to the significant opportunity that big data is creating in their relevant markets, by pursuing two ambitious goals: a. To develop and demonstrate powerful, rigorously tested, cross-sector data processing technologies that go beyond-the-state-of-the-art towards increasing the efficiency of companies that need to take important business decisions dependent on access to vast and complex amounts of data, and assess them in challenges informed by the grapevine-powered industries. b. To create a large-scale, mulifaceted marketplace for grapevine-related data assets, increasing the competitive advantage of companies that serve with IT solutions these sectors and helping companies and organisations evolve methods, standards and processes to help them achieve free, interoperable and secure flow of their data. BigDataGrapes is targeting technology challenges of the grapevine-powered data economy as its business problems and decisions requires processing, analysis and visualisation of data with rapidly increasing volume, velocity and variety: satellite and weather data, environmental and geological data, phenotypic and genetic plant data, food supply chain data, economic and financial data and more. It therefore makes a perfectly suitable cross-sector and cross-country combination of industries that are of high European significance and value.