The European Open Science Cloud (EOSC) Photon and Neutron Data Service (ExPaNDS) project will expand, accelerate and support the data management and data services provided through the EOSC for major national Photon and Neutron Research Infrastructures (PaN RIs) in delivering world-leading science.

HIFIS aims to ensure an excellent information environment for outstanding research in all Helmholtz research fields and a seamless and performant IT-infrastructure connecting knowledge from all centres. It will build a secure and easy-to-use collaborative environment with efficiently accessible ICT services from anywhere. HIFIS will also support the development of research software with a high level of quality, visibility and sustainability.

A significant amount of research data produced within the Helmholtz Association are imaging data. In order to obtain such data, the association has a unique collection of imaging modalities at its disposal, ranging from nanoscale to global observations. Imaging science is an enabling technology used in all research fields of the association.

The Photon and Neutron Open Science Cloud (PaNOSC) is a European project for making FAIR data a reality in 6 European Research Infrastructures (RIs), developing and providing services for scientific data and connecting these to the European Open Science Cloud (EOSC).

Establish a single collaborative cluster of next generation European Strategy Forum on Research Infrastructures (ESFRI) facilities in the area of astronomy- and accelerator-based particle physics in order to implement a functional link between the concerned ESFRI projects and European Open Science Cloud (EOSC). Why ESCAPE?

The eXtreme DataCloud (XDC) is a EU H2020 funded project aimed at developing scalable technologies for federating storage resources and managing data in highly distributed computing environments. The services provided will be capable of operating at the unprecedented scale required by the most demanding, data intensive, research experiments in Europe and Worldwide.

The goal of this project is to provide a system for storing and retrieving huge amounts of data, distributed among a large number of heterogenous server nodes, under a single virtual filesystem tree with a variety of standard access, authentication and autorization methods.

MDLMA, acronym for ‘Multi-task Deep Learning for Large-scale Multimodal Biomedical Image Analysis’, is a joint research project of the Helmholtz-Zentrum Geesthacht (HZG), the Deutsche Elektronen-Synchrotron DESY, the University of Lübeck (UzL) and the company Syntellix AG. It is funded by the Federal Ministry of Education and Research (BMBF), grant number 031L0202A.

The WLCG DOMA project consists of several activities in the area of Data Organization, Management and Access, with a focus on the medium/long term evolution.

One of team, Philipp, is coordinating efforts around AI at DESY. As a first measure he is orginizing frequent presentations, inviting internal and external scientists. An IA @ DESY web space is in preparation.

Members and friends of RIC are frequently teaching at the HTW Berlin.