Hamburg has been the place of four successful European Researchers’ Nights from 2009 to 2012 with around 30.000 visitors and more than four million people reached by the awareness campaign each year. Learning from our past events, we want to broaden our reach with a unique and attractive concept, for 2018 and 2019. RESEARCH RIDE will combine science and fun to raise awareness to the impact of research in our FUTURE LIFE, to enhance researchers’ public recognition and to stimulate interest in research careers. RESEARCH RIDE is a special train, operating between Hamburg’s main stations on the Hamburg metropolitan network on Friday night between 5 p.m. and 11 p.m reaching 70,000 people. Inside the waggons different event-formats will be presented, such as presentations, insta-photo booth, exhibitions, elevator-pitches, speed dating with experts, TED talks etc. Direct interaction between the public and MSC researchers and project coordinators of many trendy and entertaining EU funded projects on the train, will be provided in an exciting atmosphere for young people between 14 and 25 years. Additionally, side-events taking place in seven metro stations on the line of the RESEARCH RIDE will be promoted directing the visitors to many research associated locations. With the help of the Young European Federalists of Hamburg, in order to create a better understanding of the EU and promote European networking, the EU waggon during the event will visualize the spirit and role of the EU in general with a particular focus on its predominant part in research. To maximize the outreach outcome of the European Researchers’ Night, essential pre-events will take place during the Hamburg Climate Week. A trendy cultural (i.e. open-air movie-screenings) and educational programme (workshops with school children inside the Climate Planet preparing material presented during the RESEARCH RIDE) will advertise the proposed RESEARCH RIDE. So let’s take a walk on the RESEARCH RIDE!

The Integrated Assessment Models (IAMs) make it possible to represent many interactions between humans and the environment, thus, in their current development, can they properly contribute to the evaluation of public policies and to trans-sectoral decision-making into a context of transitions to support sustainability? The Dubai Declaration, drafted at the conclusion of the 2018 UN World Data Forum acknowledges that the data demands for the 2030 Agenda for Sustainable Development require new solutions that leverage the power of new sources of data and technology. However, this is a difficult task for many countries. In fact, the way in which the SDG monitoring indicators are implemented depends on the availability of data, processing capacities, existing data infrastructures and institutional arrangements at the national level, which is essential to involve end-users’ communities. This research is based on the integration of top-down and bottom-up approaches to data production for the monitoring of sustainable development indicators and the development of essential variables to strengthen robustness, legitimacy, relevance and transparency of integrated climate models. The proposal has been structured around some scientific and methodological requirements, namely, (i) explore and consolidate the heterogeneity and the role of multisectoral data in the development of indicators of the sustainability locally, (ii) democratize monitoring indicators by highlighting and demonstrating the role of bottom-up and local data to complement official indicators, and, finally, (iii) generate sustainable development management boards locally and globally. This could also contribute to the improvement of existing predictive models, in particular large-scale climate models (IPCC) Taking this into account, this project therefore aims at establishing an assessment framework applicable to different spatial scales by promoting sustainable development strategies compliant with climate mitigation and adaptation needs. The specific objectives of the project are (i) the analysis of existing models, (ii) the identification of essential variables of sustainable development, and (iii) the validation of a new integrated analytical framework at the interface between predictive climate models and variables defined rather locally. On the basis of the state of the art of current practices and the development of existing integrated assessment models, initially deployed to study the complex interactions between humans and the environment with a view to mitigating climate change, a new generation of integrated models is expected resulting from conceptual and methodological advances at the frontier of data sciences and social sciences for the evaluation of public policies and decision-making support in a context of transitions. This model will be applied in two use cases through the integration of a citizen and participatory observatory to strengthen the stakeholders’ inclusiveness. Each use case will be centered around a country where development and conservation issues are numerous and complex, as social, economic, political and environmental dynamics remain intertwined and where development and environmental protection policies are conditioned on international aid: Haiti, and Madagascar. The Horizon Europe call targeted will therefore allow us to explore new scientific frontiers for a better representation of heterogeneity (more geographic and sectoral details) by using different types of models (local and global), by linking different scientific disciplines, particularly social sciences and data sciences.

The consortium of this European Training Network (ETN) "BigStorage: Storage-based Convergence between HPC and Cloud to handle Big Data” will train future data scientists in order to enable them and us to apply holistic and interdisciplinary approaches for taking advantage of a data-overwhelmed world, which requires HPC and Cloud infrastructures with a redefinition of storage architectures underpinning them - focusing on meeting highly ambitious performance and energy usage objectives. There has been an explosion of digital data, which is changing our knowledge about the world. This huge data collection, which cannot be managed by current data management systems, is known as Big Data. Techniques to address it are gradually combining with what has been traditionally known as High Performance Computing. Therefore, this ETN will focus on the convergence of Big Data, HPC, and Cloud data storage, ist management and analysis. To gain value from Big Data it must be addressed from many different angles: (i) applications, which can exploit this data, (ii) middleware, operating in the cloud and HPC environments, and (iii) infrastructure, which provides the Storage, and Computing capable of handling it. Big Data can only be effectively exploited if techniques and algorithms are available, which help to understand its content, so that it can be processed by decision-making models. This is the main goal of Data Science. We claim that this ETN project will be the ideal means to educate new researchers on the different facets of Data Science (across storage hardware and software architectures, large-scale distributed systems, data management services, data analysis, machine learning, decision making). Such a multifaceted expertise is mandatory to enable researchers to propose appropriate answers to applications requirements, while leveraging advanced data storage solutions unifying cloud and HPC storage facilities.