Dashboard

This course introduces vim and provides resources for jump-starting your vim journey to learn the motions and to start customising your environment.  

This course is designed to teach the fundamental concepts and practical guidelines for ensuring that everyday data generation and management tasks fit into reproducible scientific workflows. The course emphasizes the importance of open data formats, and recommends using Markdown for documentation. Participants will also learn how to use Gitlab and Github, two data collaboration platforms, for tracking and managing data and documentation across different interfaces such as command line, IDEs and web browser. Git's underlying version control capabilities will be covered in detail during the hands-on sessions. 

This course is designed to teach the basics of everyday Linux Containers usage. Participants will learn what Linux Containers are and why they are relevant to today's scientific practice. They will learn hands-on Docker, the most popular container technology, and by the end of the course, they should be able to build simple container images by themselves. They will also be introduced to Singularity/Apptainer, a more suitable container software for HPC environments.

This course will give a quick revision of essential general concepts for using a cluster well followed by specific examples utilising slurm to run jobs on the cluster. 

This course is an introduction to the cluster. It will explain the key concepts of a cluster and how to launch a simple job. Some general advice on best practice and practices to be avoided will be covered. 

he Open Microscopy Environment (OME) is an open-source software project that develops tools to enable access, analysis, visualization, sharing and publishing microscopy data. OMERO, a component of OME, is a client-server software, designed to efficiently manage, visualize and analyze microscopy images and associated metadata.

The course will give an overview of important concepts and methods used to analyse “Biomedical data”. The emphasis will be on the understanding of statistical concepts and their interpretation in a research framework. After a general introduction on probability theory and statistical inference, an emphasis will be made on common statistical methods. Particular cases will be used as illustrative examples.

This course will introduce using the cluster and to highlight the way to think and strategise about running jobs on the cluster. The course will give a refresher on Linux skills before switching to discuss clusters in general and then focusing on how to use the resources at CRG.

This course will give an introduction to cluster computing in general, the basics of using Linux and using the computer clusters available at CRG and CNAG. The course will consist of 3 sessions each of 3 hours.The first 2 will be online and will cover introductory material, the third will be in the training room and will be a hands-on workshop using the cluster and expanding on material covered in the first 2 sessions.

This crash course was born to spread the word on the existence of R packages that offer great functions to quickly explore, present, and communicate our results.  During the sessions, we will use data visualization to try to answer questions and create new hypotheses by exploring the sample datasets and your own.

The Open Microscopy Environment (OME) is an open-source software project that develops tools to enable access, analysis, visualization, sharing and publishing microscopy data. OMERO, a component of OME, is a client-server software, designed to efficiently manage, visualize and analyze microscopy images and associated metadata.

This course will give an introduction to cluster computing in general, the basics of using Linux and using the computer clusters available at CRG and CNAG. The course will consist of 3 sessions each of 3 hours.The first 2 will be online and will cover introductory material, the third will be in the training room and will be a hands-on workshop using the cluster and expanding on material covered in the first 2 sessions.

The course will give an overview of important concepts and methods used to analyse “Biomedical data”. The emphasis will be on the understanding of statistical concepts and their interpretation in a research framework. After a general introduction on probability theory and statistical inference, an emphasis will be made on common statistical methods. Particular cases will be used as illustrative examples.

This crash course was born to spread the word on the existence of R packages that offer great functions to quickly explore, present, and communicate our results.  During the sessions, we will use data visualization to try to answer questions and create new hypotheses by exploring the sample datasets and your own.