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Grant
Agency: Cordis | Branch: FP7 | Program: CP | Phase: ICT-2013.8.2 | Award Amount: 4.12M | Year: 2014

Public administrations (PA) need to cope with various challenges: new regulations, an aging workforce and the need for adopting their ICT. Technology-enhanced learning (TEL) represents thus a sensible option notably for rural local governments (RLG) that need to keep up with such changing environments, but do have limited ac-cess to training courses.From interviews of a pre-study with RLG in 5 European countries we know that obstacles are to include learning in the work process and a lack of training plans. There are deficiencies in communication and collaboration skills despite regular use of computer and mobile devices.EAGLEs main objective is to equip employees in RLG with a holistic training solution based on Open Educa-tional Resources (OER) and Open Source (OS) tools, supporting learning of critical transversal skills such as ICT literacy and professional management of change situations. EAGLE outcomes include an innovative change management model for a learning-enhanced work process, a proficiency-based cross-device OER curriculum, and contextualization tools for multilingual collaboration. These will be integrated in the novel EAGLE OER Open Learning platform that includes a new OER ontology for public administration and combines open data and learning platforms feeding into user services, argumentation technology tool and semantic search.Our solution will thus support OS business models for the benefit of technology and e-learning providers. It will be built on existing OS tools and frameworks, IEEE OER standards and the CKAN open data standards. The EAGLE consortium includes leaders in European OER, TEL, online assessment, mobile learning and e-government research as well as experts in organisational and pedagogical development, semantic web systems and services for PA.EAGLE will significantly advance the state-of-the-art in PA learning and introduce the tech-nology through our validation and associated partners in real-life RLG environments.


Grant
Agency: Cordis | Branch: FP7 | Program: CSA | Phase: ICT-2011.1.5 | Award Amount: 603.30K | Year: 2011

The EU-Project SMARD - European Support Framework on networked Media R&D for SMEs - has been designed to fill the existing gap within the exploitation chain between successful Networked Media Research and the commercialisation by SMEs.
The SMARD Consortium is led by MFG Baden-Wrttemberg and comprises European partners from Salzburg Region (Salzburg Research), Flanders (Interdisciplinary Institute for Broadband Technology (IBBT) and Piemonte (CSP innovazione nelle ICT).
The vision is to put advanced digital media technologies in the focus of future markets for SMEs from the digital media industries as well as web-economy and beyond. Therefore the project tackles two core questions: On the one hand the degree of the responsiveness of FP7 projects to SME needs and requirements and on the other hand on the needs of R&D at the stage for successful market introduction. The project aims to identify fields of action and to develop hands on processes and service packages for Networked Media R&D commercialization.
A Survey with at least 400 SMEs from all over Europe on needs of SMEs in the Digital Media industries and web economy regarding Networked Media R&D and an analysis of FP7 projects reading their responsiveness to SME needs and requirements will be conducted. The project will deliver approaches for practitioners in the field of technology transfer, summarized in a guideline.Recommendations towards a future European support framework for Networked Media R&D will reflect valuable lessons from the SMARD project.
SMARD puts a particular focus on ensuring sustainability and European impact of the project through targeted involvement of regional and international stakeholders throughout the project duration.


Grant
Agency: Cordis | Branch: FP7 | Program: CP | Phase: ICT-2013.4.1 | Award Amount: 4.35M | Year: 2013

With the tremendous increase in multimedia content on the Web and in corporate intranets, discovering hidden meaning in raw multimedia is becoming one of the biggest challenges. Analysing multimedia content is still in its infancy, requires expert knowledge, and the few available products are associated with excessive price tags, while still not delivering sufficient quality for many tasks. This makes it almost impossible for normal companies, particularly SMEs, to make use of this technology. Also, analysis components typically operate in isolation and do not consider the context (e.g. embedding text) of a media resource. MICO addresses these problems as follows:\n\n- it will consider media in cross-media context, allowing to analyse media resources as well as connected content, including video, images, audio, text, link structure and metadata\n- it will investigate cross-media analysis along the complete, distributed analysis chain, namely extraction, metadata publishing, querying and recommendations\n- it will develop harmonized models and software services for orchestration of analysis components, representing and publishing of analysis results, and querying and evaluating such results\n- it will contribute its main software development results as Open Source components to two established Apache projects, Apache Marmotta and Apache Stanbol, simplifying the use of the technology in industrial products\n\nThe technology will be evaluated in two complementary use cases: crowd sourcing for academic research at Zooniverse (most successful crowd science platform) and video sharing at KIT digital (one of the biggest enterprise video sharing providers). Through its involvement in the Apache Software Foundation, the project partners will reach out to many technology companies and have access to a large community already at the beginning of the project. Publishing the main results as Open Source will enable particularly SMEs to benefit from cross-media analysis technology.


Grant
Agency: Cordis | Branch: FP7 | Program: CP-CSA-Infra | Phase: INFRA-2012-1.1.3. | Award Amount: 8.43M | Year: 2013

ARIADNE is a proposal to bring together and integrate the existing archaeological research data infrastructures so that researchers can use the various distributed datasets and new and powerful technologies as an integral component of the archaeological research methodology. There is now a large availability of archaeological digital datasets that altogether span different periods, domains and regions; more are continuously created as a result of the increasing use of IT. They are the accumulated outcome of the research of individuals, teams and institutions, but form a vast and fragmented corpus and their potential is constrained by difficult access and non-homogenous perspectives. This integrating activity will enable trans-national access of researchers to data centres, tools and guidance, and the creation of new Web-based services based on common interfaces to data repositories, availability of reference datasets and usage of innovative technologies. It will stimulate new research avenues in the field of archaeology, relying on the comparison, re-use and integration into current research of the outcomes of past and on-going field and laboratory activity. Such data are scattered amongst diverse collections, datasets, inaccessible and unpublished fieldwork reports grey literature, and in publications, the latter still being the main source of knowledge sharing. It will contribute to the creation of a new community of researchers ready to exploit the contribution of Information Technology and to incorporate it in the body of established archaeological research methodology. To achieve this result the project will use a number of integrating technologies that build on common features of the currently available datasets, and on integrating actions that will build a vibrant community of use. The overall objective outlined above will be achieved through subordinate goals, which altogether will enable the provision of advanced Integrated Infrastructure.


Grant
Agency: Cordis | Branch: FP7 | Program: CP | Phase: ICT-2013.4.3 | Award Amount: 1.86M | Year: 2014

To make publishing and processing of linked data easy, the proposed project develops a set of integrated software components based on open-source Linked Data Platform best practices. The tightly integrated components support the multilingual data value chain from data exploration (e.g. identifying structured and unstructured data sources), extraction (e.g. using named entity recognition, RDF conversion), enrichment (e.g. interlinking, crowdsourcing), and delivery (e.g. analytics, apps for desktop and mobile devices). These components run on an open-source data platform with various enterprise-grade storage solutions.The vision is to make publishing and reuse of linked data as easy as possible for the end user thanks to a thriving market economy with data publishers, developers, and consumers along the value chain. Making data reusable and interoperable within and outside the organization requires a fundamentally different ap-proach to storing knowledge. The best name is probably a Logical Data Warehouse...because it focuses on the logic of information ...[for] giving integrated access to all forms of information assets. Only with integrated access to the data is it possible to have apps on top of that data that scale across single use cases and provide real added value.Fusepool LDAP (Linked Data Analytics Processing) derives its name from the idea of fusing and pooling linked data with analytical processing on top of it. Because linked data is multidimensional data, it lends itself to analytical processing such as consolidation (e.g. aggregation within a dimension), drill-down (e.g. navigating through the details), and slicing and dicing (e.g. viewing an aspect from different dimensions). However, an integrated publishing and processing workflow with integrated user interfaces is still missing. The lack of an integrated publishing and processing environment makes it difficult and time-consuming for data publishers and consumers to engage with linked data.

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