Training materials

Digital Skills Workshops are based on tried and tested high-quality materials from Software and Data Carpentry and Code Refinery as well as materials developed at the Center. All materials are collaboratively developed with the research community and are openly available. 

Our training materials

The Netherlands eScience Center actively develops new lessons in the areas of our expertise. Click on some of the topics below to learn more.

Parallel Programming

These materials teaches the principles of parallel programming in Python using Dask, Numba and Snakemake. More importantly, it gives insight into how these different methods perform and when they should be used.

GPU Programming

These materials provide learners with the fundamental knowledge that they need to start their journey into the world of programming GPUs. After a brief introduction to the specificities of GPUs and how they differ from traditional processors, participants will experience various ways of using them with Python.

Deep Learning

This hands-on introduction to the first steps of Deep Learning is intended for researchers who are familiar with (non-deep) Machine Learning. We start with explaining the basic concepts of neural networks and then go through the different steps of a Deep Learning workflow.

Geospatial Python

Teaches how to deal with geospatial raster and vector data in Python. Amongst others, the material treats reading and writing data, processing data, doing calculations with it, and creating publication-worthy figures.

R Packaging

Wrapping your code and data into a package helps you create a more robust, more reproducible and more enjoyable research coding experience. Using the materials for the R Packaging workshop, students will take a scripted project and build their own R Package.

Other training materials


Software Carpentry

Software Carpentry lessons introduce basic lab skills for research computing. They cover three core topics: the Unix shell, version control with Git, and a programming language (Python or R). 

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Data Carpentry

Data Carpentry lessons focus on foundational skills needed to work effectively and reproducibly with data and code. They are domain-specific, teaching researchers the skills most relevant to their domain and using examples from their type of work. There are several types of data carpentry workshops for which the curriculum is organized by domain. 


Code Refinery

Code Refinery lessons aim to boost researchers’ good software practices and to consolidate knowledge from Carpentries  (Software, Data and Library Carpentry) workshops or prior experience. The focus is on tools for efficiently developing and maintaining research software.