Disciplines: Difference between revisions
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== Climate == | == Climate == | ||
VI-SEEM will have strong impact on the Climate Modelling and weather forecasting communities. First, there is significant potential to share best practice and data for local and regional Climate Modelling, Weather forecasting and air quality simulations. The community will benefit from the combination of HPC and Grid computing jointly with the storage facilities as it heavily relies on data from very scattered locations. | VI-SEEM will have strong impact on the Climate Modelling and weather forecasting communities. First, there is significant potential to share best practice and data for local and regional Climate Modelling, Weather forecasting and air quality simulations. The community will benefit from the combination of HPC and Grid computing jointly with the storage facilities as it heavily relies on data from very scattered locations. | ||
Additionally, VI-SEEM will create many opportunities for users that have not collaborated before to engage in joint activities: with code repositories and training material for climate models, the VRE will create a highly productive working environment for Climate scientists from the 12 different research groups distributed over 10 countries. Finally, the regional research activity also links into global efforts towards understanding the climatic changes and challenges. The services provided by this community enable contingency planning and help understand climatic conditions within which our future societies will live. | Additionally, VI-SEEM will create many opportunities for users that have not collaborated before to engage in joint activities: with code repositories and training material for climate models, the VRE will create a highly productive working environment for Climate scientists from the 12 different research groups distributed over 10 countries. Finally, the regional research activity also links into global efforts towards understanding the climatic changes and challenges. The services provided by this community enable contingency planning and help understand climatic conditions within which our future societies will live. | ||
'''''User Community for [[Climate]]''''' | |||
== Culture Heritage == | == Culture Heritage == | ||
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Beyond the data needs, VI-SEEM will also facilitate the slow transition of the Cultural Heritage community towards computational more intensive activities, such as high detail rendering of 3D modelling, and simulations of environmental influence on historical buildings. Shared datasets, easy remote access and visualization enabled by the VI-SEEM platform will offer a novel approach to Cultural Heritage research that can foster innovation in methodologies and applications used. The Cultural Heritage Scientific Community of VI-SEEM consists of 11 research institutes from 7 countries. | Beyond the data needs, VI-SEEM will also facilitate the slow transition of the Cultural Heritage community towards computational more intensive activities, such as high detail rendering of 3D modelling, and simulations of environmental influence on historical buildings. Shared datasets, easy remote access and visualization enabled by the VI-SEEM platform will offer a novel approach to Cultural Heritage research that can foster innovation in methodologies and applications used. The Cultural Heritage Scientific Community of VI-SEEM consists of 11 research institutes from 7 countries. | ||
== Life | '''''User Community for [[Culture_Heritage]]''''' | ||
== Life Sciences == | |||
Life Sciences will largely benefit from VI-SEEM that will facilitate through the VRE the laborious analysis and processing of big data arising from decoding the human genome in health and disease. The associated data analysis challenges include capture, curation, analysis, search, sharing, storage, transfer, and visualization. The VI-SEEM community consists of 12 research institutes from 9 different countries of the region. | Life Sciences will largely benefit from VI-SEEM that will facilitate through the VRE the laborious analysis and processing of big data arising from decoding the human genome in health and disease. The associated data analysis challenges include capture, curation, analysis, search, sharing, storage, transfer, and visualization. The VI-SEEM community consists of 12 research institutes from 9 different countries of the region. | ||
The project will improve the innovation capacity as well as the efficient collaboration of researchers in the SEEM region by providing access to needed codes, data repositories, training material for data generation, processing and simulation setup. New knowledge will be produced and integrated into the existing e-Infrastructure. The Life Sciences community will lay the foundations for a larger infrastructure aiming to integrate all the laboratories that generate big data in the SEEM region in the future. | The project will improve the innovation capacity as well as the efficient collaboration of researchers in the SEEM region by providing access to needed codes, data repositories, training material for data generation, processing and simulation setup. New knowledge will be produced and integrated into the existing e-Infrastructure. The Life Sciences community will lay the foundations for a larger infrastructure aiming to integrate all the laboratories that generate big data in the SEEM region in the future. | ||
'''''User Community for [[Life_Sciences]]''''' | |||
Latest revision as of 15:07, 4 January 2017
Climate
VI-SEEM will have strong impact on the Climate Modelling and weather forecasting communities. First, there is significant potential to share best practice and data for local and regional Climate Modelling, Weather forecasting and air quality simulations. The community will benefit from the combination of HPC and Grid computing jointly with the storage facilities as it heavily relies on data from very scattered locations. Additionally, VI-SEEM will create many opportunities for users that have not collaborated before to engage in joint activities: with code repositories and training material for climate models, the VRE will create a highly productive working environment for Climate scientists from the 12 different research groups distributed over 10 countries. Finally, the regional research activity also links into global efforts towards understanding the climatic changes and challenges. The services provided by this community enable contingency planning and help understand climatic conditions within which our future societies will live.
User Community for Climate
Culture Heritage
The Cultural Heritage researchers pursue activities on a number of common themes and topics that will be impacted by the shared e-Infrastructure. Common data repositories and software, such as content management system MEDICI, algorithms for remote sensing image classification, idPromo for automatic object recognition etc., will advance the research capacity of the various groups to optimally utilize them. Beyond the data needs, VI-SEEM will also facilitate the slow transition of the Cultural Heritage community towards computational more intensive activities, such as high detail rendering of 3D modelling, and simulations of environmental influence on historical buildings. Shared datasets, easy remote access and visualization enabled by the VI-SEEM platform will offer a novel approach to Cultural Heritage research that can foster innovation in methodologies and applications used. The Cultural Heritage Scientific Community of VI-SEEM consists of 11 research institutes from 7 countries.
User Community for Culture_Heritage
Life Sciences
Life Sciences will largely benefit from VI-SEEM that will facilitate through the VRE the laborious analysis and processing of big data arising from decoding the human genome in health and disease. The associated data analysis challenges include capture, curation, analysis, search, sharing, storage, transfer, and visualization. The VI-SEEM community consists of 12 research institutes from 9 different countries of the region. The project will improve the innovation capacity as well as the efficient collaboration of researchers in the SEEM region by providing access to needed codes, data repositories, training material for data generation, processing and simulation setup. New knowledge will be produced and integrated into the existing e-Infrastructure. The Life Sciences community will lay the foundations for a larger infrastructure aiming to integrate all the laboratories that generate big data in the SEEM region in the future.
User Community for Life_Sciences