This lecture presents an introduction to an end-to-end scientific computing workflow utilising Docker containers.In the first part, attendees learn about the fundamentals of containerisation and the advantages it brings to scientific software. Participants then familiarise with Docker technologies and tools, discovering how to manage and run containers on personal computers, and how to build applications of increasing complexity into portable container images. Particular emphasis is given to software resources which enable highly-efficient scientific applications, like MPI libraries and the CUDA Toolkit.The second part proposes hands-on exercises that revisit and expand upon the examples provided in the first part.The last part of the lecture focuses on deploying Docker images on high-end computing systems, using a container engine capable of leveraging the performance and scalability of such machines, while maintaining a consistent user experience with Docker.This lecture is based on material produced in the context of the ESiWACE European Center of Excellence (CoE) and presented during the "ESiWACE Summer School on Effective HPC for Climate and Weather" in 2020 and 2021.Contact: alberto.madonna@cscs.ch

The goal of this text is to provide a practical introduction to systems integration by designing and implementing an actual system. Readers are taken through a project that builds a containerized web application using Docker and then expands it to use the Kubernetes orchestration framework. Through the practical examples topics such as version control, interchange formats, front end design, messaging frameworks, container-based virtualization, and container orchestration are covered.

Reproducibility is unquestionably at the heart of science. Scientists face numerous challenges in this context, not least the lack of concepts, tools, and workflows for reproducible research in today's curricula.This short course introduces established and powerful tools that enable reproducibility of computational geoscientific research, statistical analyses, and visualisation of results using R (http://www.r-project.org/) in two lessons:1. Reproducible Research with R MarkdownOpen Data, Open Source, Open Reviews and Open Science are important aspects of science today. In the first lesson, basic motivations and concepts for reproducible research touching on these topics are briefly introduced. During a hands-on session the course participants write R Markdown (http://rmarkdown.rstudio.com/) documents, which include text and code and can be compiled to static documents (e.g. HTML, PDF).R Markdown is equally well suited for day-to-day digital notebooks as it is for scientific publications when using publisher templates.2. GitLab and DockerIn the second lesson, the R Markdown files are published and enriched on an online collaboration platform. Participants learn how to save and version documents using GitLab (http://gitlab.com/) and compile them using Docker containers (https://docker.com/). These containers capture the full computational environment and can be transported, executed, examined, shared and archived. Furthermore, GitLab's collaboration features are explored as an environment for Open Science.Prerequisites: Participants should install required software (R, RStudio, a current browser) and register on GitLab (https://gitlab.com) before the course.This short course is especially relevant for early career scientists (ECS).Participants are welcome to bring their own data and R scripts to work with during the course.All material by the conveners will be shared publicly via OSF (https://osf.io/qd9nf/).