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IoT Lab is a research platform exploring the potential of crowdsourcing and Internet of Things for multidisciplinary research with more end-user interactions. IoT Lab is a European Research project which aims at researching the potential of crowdsourcing to extend IoT testbed infrastructure for multidisciplinary experiments with more end-user interactions. It addresses topics such as: - Crowdsourcing mechanisms and tools; - “Crowdsourcing-driven research”; - Virtualization of crowdsourcing and testbeds; - Ubiquitous Interconnection and Cloudification of testbeds; - Testbed as a Service platform; - Multidisciplinary experiments; - End-user and societal value creation; - Privacy and personal data protection.
HunCLARIN is a strategic research infrastructure of Hungary’s leading knowledge centres involved in R&D in speech- and language processing. It contains linguistic resources and tools that form the basis of research. The infrastructure has obtained an “SKI” qualification (Strategic Research Infrastructure) in 2010, and has been significantly expanded since. Currently comprising 36 members, the infrastructure includes several general- and specific-purpose text corpora, different language processing tools and analysers, linguistic databases as well as ontologies. RIL HAS was a co-founder of the European CLARIN project, which aims at supporting humanities and social sciences research with the help of language technology and by making digital linguistic resources more easily available. In accordance with these goals HunClarin makes the research infrastructures developed by the respective centres directly accessible for researchers through a common network entry point. A general goal of the infrastructure is to realise the interoperability of the collected research infrastructures and to enable comparing the performance of the respective alternatives and to coordinate different foci in R&D. The coordinator and contact person of the infrastructure is Tamás Váradi, RIL HAS.
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With the KIT Whole-Body Human Motion Database, we aim to provide a simple way of sharing high-quality motion capture recordings of human whole-body motion. In addition, with the Motion Annotation Tool (https://motion-annotation.humanoids.kit.edu/ ), we aim to collect a comprehensive set of whole-body motions along with natural language descriptions of these motions (https://motion-annotation.humanoids.kit.edu/dataset/).