The need to advance financial capability and financial awareness among informed citizens and market participants was identified as a major target to achieve an improved social performance, better client protection and, ultimately, greater societal well-being. The new socio-economic landscape in the post-crisis world brought changes in the financial, labour and pension markets along with changes in the public finance decisions from the political sphere. Technological developments enable and enhance the availability of large volumes of information on themes relevant to financial decision making. However, the volume of information, the existence of often ad hoc sources, and the documented existence of cognitive limitations by individuals when it comes to the processing of big data plus widespread financial illiteracy found even within developed economies, including those of the EU point to the need for (a) specialized financial education toolkits available to the wider public (b) advanced crowd-sourcing tools to process financial data, extract and present collective knowledge, (c) advanced forecasting models exploiting the market sentiment to identify market trends and threats, (d) novel personalized recommendation systems to support financial decisions according to the user’s profile (financial literacy level, interests, demographic characteristics etc). Based on these requirements, PROFIT will develop a platform built on Open Source components. The platform's functionalities will be pilot-tested in collaboration with the members of the European Federation of Ethical and Alternative Banks (FEBEA), an institution committed to responsible banking and finance. The outcomes of the project are expected to enable best practices that can be made available to the wide public in the European Union.

The rapid growth of web data creates demand for software engineering methods which can build and maintain applications that extract, process and publish web data. However, converting Big Data sources into high-quality, structured knowledge for use in business processes is usually considered data engineering. ALIGNED will develop models, methods and tools for engineering information systems based on co-evolving software and web data. Tools for model-driven software evolution based on Linked Data sources, runtime data quality analytics, human data curation and process integration will aid more efficient governance, increased agility and higher productivity. The opportunities for web data have recently led to intense research and innovation, but most efforts are siloed in the software or data spaces. Past integration of data and software engineering used formal ontologies rather than Linked Data. ALIGNED will provide lightweight methods for European data and software engineering industries to exploit the new opportunities in web data. Information companies like Wolters Kluwer need better ways to extract web data and build applications on top of it. Public bodies like the UK National Health Service (NHS) need evolving systems for data collection and re-use. Web data publishers like DBpedia need new methods to improve data quality. Scientific publications, industry workshops, training programs, open source tools and engaging the OMG, W3C and ISO standards bodies will transfer ALIGNED outputs. ALIGNED combines world class researchers in model driven software engineering (Oxford are transforming NHS systems), Linked Data quality (Leipzig and Trinity College have published foundational papers) and web systems (Leipzig are co-creators of DBpedia) with innovative enterprises (Wolters Kluwer have Linked Data in production systems, Semantic Web Company lead the world in enterprise Linked Data) and pioneering expert-curated data publishers (Oxford Anthropology and Posnan).

Operation & Maintenance (O&M) costs are the main cost driver in offshore energy due to the difficult accessibility to the WTs, but also due to the environmental conditions. O&M costs can account for up to 30% of the levelised cost of energy (LCOE) and sensing & monitoring systems could help attain the expected fall to 70 EUR/MWh by 2030. The highest criticality (in €/kWh) in offshore wind is caused by structural failure, that mainly occurs due to corrosion processes non-adequately neither predicted nor monitored. For that reason, it is crucial to implement new monitoring, diagnosis, prognosis and control tools into the offshore wind farms (WFs) to enable Wind Farm Operators (WFOs) to take predictive smart O&M decisions fully considering structural components real and future status. WATEREYE aims to develop an integral solution that will allow to WFOs a 4% reduction of OPEX, accurately predicting the need for future maintenance strategy and increasing the offshore wind annual energy production. To this end, WATEREYE will: 1/ develop a monitoring system capable of remotely estimating the corrosion level in exact WT locations (tower, splash-zone, tower-platform junction) as a supporting tool for predictive maintenance to considerably reduce the O&M costs and reduce the risk for operation failures; New Ultrasound corrosion sensors (ad-hoc, low-cost, high accuracy, fast-response, non-invasive) will be developed, as well as high efficient and robust wireless communications specifically conceived for offshore WTs hard communicating environment. Besides, a novel drone-based mobile platform to move one mobile sensor inside the WT tower will be developed. 2/ develop enhanced prediction models by analysing the acquired data in novel ways (semantic models); 3/ develop WT & WF control algorithms with accurate consideration of the structural health, giving operators freedom to choose the best balance between energy production, protective control, and predictive maintenance.

Language technologies increasingly rely on large amounts of data and better access and usage of language resources will enable to provide multilingual solutions that would support the emerging Digital Single Market in Europe. However, data is rarely ‘ready-to-use’ and language technology specialists spend over 80% of their time on cleaning, organizing and collecting datasets. Reducing this effort promises huge cost savings for all sectors where language technologies are required. An essential part of the Extract-Transform-Load process involves linking datasets to existing schemas, yet few specialists take advantage of linked data technologies to perform this task. In this project we aim to increase the uptake of language technologies by exploiting the combination of linked data and language technologies, that is Linguistic Linked Open Data (LLOD), to create ready-to-use multilingual data. Prêt-à-LLOD aims to achieve this by creating a new methodology for building data value chains applicable to a wide-range of sectors and applications and based around language resources and language technologies that can be integrated by means of semantic technologies, in particular the usage of Linguistic Linked Open Data (LLOD). The project will develop novel tools for the transformation and linking of datasets, and apply these to both data and metadata in order to provide multi-portal access to heterogeneous data repositories. We will study how we can automatically analyze licenses in order to deduce how data may be lawfully used and sold by language resource providers. Finally, we will provide tools to combine language services and resources into complex pipelines by use of semantic technologies. This will lead to sustainable data offers and services that can be deployed to many platforms, including as-yet-unknown platforms, and can be self-described with linked data semantics. This toolkit will be validated in four pilots, where novel data value chains will be built for pharma