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The main objective of this session is to show how EOSC services can be used for managing active research data (i.e. data transfer, storage, and sharing) and for preserving final research data (i.e. data archiving and publishing). During this training, we will give a brief overview of the EUDAT Services in the data life cycle and demonstrate how these services operate and integrate with each other to meet the data management requirements of research communities and comply with the FAIR principles - which require the data to be properly documented, annotated, archived, published and accessible to the wider community.

This training track is relevant for researchers, IT support people, and service providers who operate services for Open Science.

The main objective of this session is to demonstrate how end-users can perform data analysis on large volume of datasets, and produce reusable results following the FAIR principles. During this training track, the latest features of the EGI DataHub, including the interoperability with the EGI Jupyter Notebooks and the EUDAT B2Handle and B2Find services, will be also introduced.

This training track is relevant for researchers, IT support people, and service providers who operate services for Open Science.

Jupyter provides a powerful environment for expressing research ideas as notebooks, where code, test and visualizations are easily combined together on an interactive web-frontend. JupyterHub allows to deploy a multi-user service where users can store and run their own notebooks without the need of installing anything on their computers. This is the technology behind the EGI Notebooks service and other similar Jupyter-based services for research.

In this training we will demonstrate how to deploy a JupyterHub instance for your users on top of Kubernetes and explore some of the possible customisations that can improve the service towards your users like integration with authentication services or with external storage systems. After this training, the attendees will be able to deploy their own instance of JupyterHub on their facilities.

Target audience: Resource Center/e-Infrastructure operators willing to provide Jupyter environment for their users.

Pre-requisites: basic knowledge of command-line interface on Linux.

Elastic Cloud Computing Cluster (EC3) is a tool to create elastic virtual clusters on top of Infrastructure as a Service (IaaS) providers, either public (such as Amazon Web Services, Google Cloud or Microsoft Azure) or on-premises (such as OpenNebula and OpenStack). We offer recipes to deploy TORQUE (optionally with MAUI), SLURM, SGE, HTCondor, Mesos, Nomad and Kubernetes clusters that can be self-managed with CLUES: it starts with a single-node cluster and working nodes will be dynamically deployed and provisioned to fit increasing load (number of jobs at the LRMS). Working nodes will be undeployed when they are idle. This introduces a cost-efficient approach for Cluster-based computing.