Training materials

Our workshops are based on high-quality materials from Software CarpentryData 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.  

Use our training materials 

Do you want to teach digital skills workshops using our training materials? All materials that we develop are open-source, freely available and are co-developed with experts around the globe. Have a look at our material below. 

Are you planning on using our material in your teaching? Send us an email at . We can offer help on the best approach. In addition, we are always looking for feedback on our training materials.  

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

This is an intermediate level course on the basics of parallel programming with Python. Learners will learn to recognize problems that are suitable for parallel processing and get hands-on experience with optimizing inner loops and abstracting parallelism using Dask, Numba and Asyncio.

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 workshop gives an introduction to deep learning for researchers who are familiar with the basics of (non-deep) machine learning. The material teaches how to prepare data for deep learning, implement a basic deep learning model in Python with Keras, monitor and troubleshoot the training process and implement different layer types, such as convolutional layers.

Geospatial Python

Teaches how to deal with geospatial raster and vector data in Python. The lesson introduces a set of tools from the Python ecosystem and show how these can be used to carry out practical geospatial data analysis tasks. In particular, the lesson considers satellite images and public geo-datasets and demonstrate how these can be opened, explored, manipulated, combined, and visualized using Python.

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

Tools

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

CodeRefinery

CodeRefinery 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.