Recent years witness an upsurge in the quantities of digital research data, offering new insights and opportunities for improved understanding. Text and data mining is emerging as a powerful tool for harnessing the power of structured and unstructured content and data, by analysing them at multiple levels and in several dimensions to discover hidden and new knowledge. However, text mining solutions are not easy to discover and use, nor are they easily combinable by end users. OpenMinTeD aspires to enable the creation of an infrastructure that fosters and facilitates the use of text mining technologies in the scientific publications world, builds on existing text mining tools and platforms, and renders them discoverable and interoperablethrough appropriate registriesand a standards-based interoperability layer, respectively. It supports training of text mining users and developers alike and demonstrates the merits of the approach through several use cases identified by scholars and experts from different scientific areas, ranging from generic scholarly communication to literaturerelated tolife sciences, food and agriculture, and social sciences and humanities. Through its infrastructural activities, OpenMinTeD’s vision is tomake operational a virtuous cycle in which a) primary content is accessed through standardised interfaces and access rules b) by well-documented and easily discoverable text mining services that process, analyse, and annotate text c) to identify patterns and extract new meaningful actionable knowledge, which will be used d) for structuring, indexing, and searching content and, in tandem, e) acting as new knowledge useful to draw new relations between content items and firing a new mining cycle. To achieve its goals, OpenMinTeD brings together different stakeholders, content providers and scientific communities, text mining and infrastructure builders, legal experts, data and computing centres, industrial players, and SMEs.

CYBELE generates innovation and create value in the domain of agri-food, and its verticals in the sub-domains of PA and PLF in specific, as demonstrated by the real-life industrial cases to be supported, empowering capacity building within the industrial and research community. Since agriculture is a high volume business with low operational efficiency, CYBELE aspires at demonstrating how the convergence of HPC, Big Data, Cloud Computing and the IoT can revolutionize farming, reduce scarcity and increase food supply, bringing social, economic, and environmental benefits. CYBELE intends to safeguard that stakeholders have integrated, unmediated access to a vast amount of large scale datasets of diverse types from a variety of sources, and they are capable of generating value and extracting insights, by providing secure and unmediated access to large-scale HPC infrastructures supporting data discovery, processing, combination and visualization services, solving challenges modelled as mathematical algorithms requiring high computing power. CYBELE develops large scale HPC-enabled test beds and delivers a distributed big data management architecture and a data management strategy providing 1) integrated, unmediated access to large scale datasets of diverse types from a multitude of distributed data sources, 2) a data and service driven virtual HPC-enabled environment supporting the execution of multi-parametric agri-food related impact model experiments, optimizing the features of processing large scale datasets and 3) a bouquet of domain specific and generic services on top of the virtual research environment facilitating the elicitation of knowledge from big agri-food related data, addressing the issue of increasing responsiveness and empowering automation-assisted decision making, empowering the stakeholders to use resources in a more environmentally responsible manner, improve sourcing decisions, and implement circular-economy solutions in the food chain.

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.

The agri-food industry faces numerous challenges dealing with societal, public health, individual nutrition and environmental, food waste and overall food system sustainability challenges. Imbalances and disconnected food markets are generating undesirable trade-offs between the food supply, the consumption patterns, quality of nutrition and the environment. Interoperability and data sharing across agri-food supply networks is limited. Data can revolutionise the food industry and foster its contribution to inclusive and sustainable food systems. Data can be used to assist these stakeholders in making informed decisions on how to operate in a more sustainable and inclusive manner. In this way, they increase the efficiency of the food industry through the optimisation of relevant operations and the reduction of waste, promoting transparency and demonstrate their commitment to ethical and sustainable production. FoodDataQuest will develop ground-breaking data-driven solutions based on an integrated methodological framework that explores new types of private and public data sources, data from “unconventional players” and non-competitive data and leverages data sharing mechanisms in order to provide the EU food chain stakeholders with increased insights and enhance the transition towards sustainable healthy diets. The proposed framework will include guidelines and data collection strategies, to drive the food system transformation towards inclusive, sustainable, healthy diets within the boundaries of legal and policy frameworks. FoodDataQuest will co-create and test advanced data-driven solutions based on AI and ML algorithms, following a multi-actor approach that will serve as a lighthouse that positively impacts a fair, healthy and environmentally friendly food system. Last, FoodDataQuest will engage citizens into industry's data-driven innovations balancing between data openness and protection of private and sensitive data of multiple stakeholders.

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.