The DARE platform provides all the necessary tools to research developers to develop and execute their experiments written using either the fine-grained workflow specification library dispel4py or CWL. Moreover, the DARE platform offers data provenance to the users so as to track and analyze their data. The DARE platform is cloud-ready, integrating containerized software applications running on top of Kubernetes. Kubernetes enables automated deployment, scaling and management of containerized applications. Research developers can interact with the DARE platform by making use of its RESTful API. DARE API is a composition of RESTful Web APIs exposed by the containerized versions of the underlying DARE platform components. For more information about the DARE Project visit our site at http://project-dare.eu/.
DebateLab intends to pioneer research towards unlockng the potential of the new dialectical nature of the Web, relying on the coupling of mature fields namely Computational Argumentation, Natural Language Processing, Information Retrieval, Machine Learning, and the Semantic Web. The aim of DebateLab is dual purpose; a) conduct research towards devloping the required thoeoretical infrastructure for representing, mining and reasoning with online arguments, and b) develop and deliver a suite of tools and services (e-journalism domain) supporting the uptake and initial exploitation of the related technologies.
I worked in the FAIR4Fusion project as a Software Engineer. The project’s goal was to make European (or nationally when permitted) funded data more widely available to the fusion community, other science communities, funding bodies, and the public at large in order to maximise the impact of, the data and demonstrate the importance of the work done at relevant sites. I was involved in the development of a demonstrator, written in python using the Django Framework.