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HBO/WO

MBO

August 1, 2026

Guidelines for Research Coding with AI

Welcome to this mini e-module on research coding with AI! This mini-course introduces responsible AI-assisted coding practices for academic research.

Website

Erasmus University Library

July 21, 2026

Introduction to Stochastic Hydrology

Hydrologic systems are both complex and extremely heterogeneous in time and space. As hydrological models are simplified versions of reality, their predictions are by definition subject to uncertainty. The field of stochastic hydrology focuses on quantifying this uncertainty along with prediction. This book aims to present an overview of the field of stochastic hydrology at an introductory level. A wide range of topics and methods will be treated, while each topic and method is only treated at a basic level: descriptive statistics; probability and random variables; hydrological statistics and extremes; random functions; time series analysis; geostatistics; forward stochastic modeling; and state prediction and Kalman filtering. The book can be used for a 10-15-week course at third-year undergraduate or first-year graduate level.

Document

Marc F.P. Bierkens

June 16, 2026

Open Education menu

We created a ready-to-use menu with 17 different Open Education practices to be implemented in your education. With each practice turned into its own dish and recipe, you can easily see how the practice can be implemented, with its difficulty level and examples. The goal is to provide you with ideas and examples on how to implement Open Science practices into your education. Or use the menu as a starting point for conversations about open educational practices with colleagues! Already an expert? The menu can also be used for a 1-hour workshop on Open Educational practices for others interested in Open Science. Let the participants select a dish from the menu they are interested in, and help them to adapt the dish so they can turn their own ideas into real practices.

Presentation

Jeroen Bosman, Ilcan Çiftcibaşi, Anneke van der Dussen, Ruud Dielen, Marie-Louise Goudeau, Pim Huijnen, Susanne Kirchhoff, Marc van Mil, Hanne Oberman, Annelies Pieterman-Bos

January 1, 2026

Virtual sea-going marine science fieldwork with VirtualShip

Marine science relies heavily on fieldwork at and in the ocean for data collection. Consequently, marine science students need training in sea-going fieldwork. However, such training is expensive, logistically demanding, and environmentally burdensome because research vessels emit substantial amounts of CO₂. To provide students with an opportunity to experience the realities of working at sea, we developed a platform for virtual oceanic fieldwork: the VirtualShip Classroom, which provides Python code, exercises, and 360 videos as virtual reality (VR) covering different aspects of fieldwork such as expedition planning, sampling, and data analysis. Whenever possible, students work with authentic tools and realistic datasets, such as the Marine Facilities Planning platform for designing research expeditions. Basic programming skills are advantageous, particularly when students analyze self-collected data. Students formulate their own research question in a region of the global ocean of their choice. They then investigate this question with a virtual research vessel operated through the bespoke VirtualShip software. Instruments currently implemented for deployment or onboard use are ADCP, CTD, XBT, Argo floats, surface drifters, and underway temperature and salinity sensors. This collection of teaching materials is accompanied by a suggested workflow, which outlines a series of classes on virtual ocean research.

Document

Emma Daniels, Erik van Sebille, Jamie Atkins

October 30, 2025

Programming Foundations

This introductory programming book offers a hands-on approach to learning Python, designed specifically for students of natural sciences who wish to enter the world of coding. Covering fundamental programming concepts - from variables and data types to functions and loops - the book presents examples and exercises rooted in applications across biology, physics, and mathematics. In addition to Python fundamentals, readers also build good practices of programming and working with computers, such as using the terminal, navigating an integrated development environment, tracking changes with Git, and writing pseudocode. Moreover, the book addresses how to usage generative AI tools effectively when learning the basics of programming. Through hands-on learning, each chapter builds both confidence and competence, guiding students from simple scripts to more complex code. By the end of the book, students will not only understand Python’s core principles but also have the skills and tools to tackle real coding challenges within their scientific disciplines in the age of AI.

Open (Access) Text Book

Nikolina Šoštarić

August 25, 2025

Python handbook for engineering students

This book offers a concise and practical introduction to Python programming, specifically tailored for Electrical Engineering students. It begins with the fundamentals to help readers get started, covering essential concepts such as variables, operations, control structures, and plotting. As the material progresses, students are introduced to core topics in numerical computation and the basics of signal processing using powerful Python libraries such as NumPy and SciPy. To support learning, the book provides numerous clear examples and an optional step-by-step guide designed to help readers work through the material at their own pace. This structured approach makes it easy for beginners to build confidence and develop practical coding skills. Designed as a fast and accessible entry point, the book equips students with the programming foundation needed to support and enhance their learning in other engineering courses.

Open (Access) Text Book

Bahareh Abdi

August 18, 2025

Geoscripting

The course is not a pure programming course, but has a rather strong focus on programming and scripting to solve geographical or spatial applied problems. During the course you will learn how to deal with both vector spatial data and raster data; such as satellite imagery used in remote sensing studies. The programming languages dealt with during the course are R and python. Professionals in private companies and research institutes are increasingly using scripting languages for applied spatial analysis. We notice that students still have a kind of 'scripting' barrier to overcome. Some students are afraid of scripting and others stick very closely to the examples given. We like to relax this attitude. From the perspective of an advanced course we like to change the cookbook approach in favor of a more explorative approach. Becoming more relaxed towards scripting and a more explorative approach means that some generic scripting principles must be known to create a 'core' script. On the other hand students have to learn which libraries are available for spatial data analysis and visualization and how these libraries can be used within the 'core' script.

Website

Dainius Masiliunas, Arend Ligtenberg, Corné Vreugdenhil, Nandika Tsendbazar

July 2, 2025

Intro to python programming

Learning Outcomes for Python Programming Modules 1. Develop and Document Python Code in Jupyter Notebooks Learners will create, document, and execute Python code using Jupyter Notebooks, effectively utilizing Markdown for readable and well-structured analysis. 2. Understand and Apply Python Data Types and Operations Learners will identify and manipulate core Python data types, perform data operations, and utilize comprehensions for efficient data processing. 3. Implement Program Logic and Functions Learners will control program flow using conditionals and loops, encapsulate logic within functions, and handle errors using Python’s exception mechanism. 4. Read, Write, and Process Data Files Learners will read from and write to files, apply regular expressions for text processing, and leverage core library functions for common data tasks. 5. Apply Object-Oriented Programming and Prepare for Data Analysis Learners will design and use classes, including data classes, and set up a Python environment with (Ana)conda, preparing to work with data analysis libraries such as pandas. Consists of 18 lessons (5EC).

Website

Michiel Noback

July 2, 2025

Data Analysis and visualization BML

Learning Outcomes You can explain the fundamental concepts of data, including data types, data structures, and the importance of data quality, and you can apply these concepts in the context of both Excel and R. You can import data from various sources (such as CSV, TXT, Excel files) into both Excel and R, and you are able to select the appropriate methods and functions for different data formats and structures. You can clean and prepare data for analysis in both Excel and R by applying techniques for identifying and handling missing values, outliers, inconsistencies, and duplicates. You can apply and interpret various data analysis functions in both Excel and R, including determining minimum and maximum values, calculating percentiles, selecting specific data based on criteria, applying conditional formatting to visually identify patterns, and sorting data to gain insights. You can create effective data visualizations in both Excel and R (such as bar charts, line charts, pie charts, and box plots) to communicate patterns, trends, and relationships in data, and you can customize the visualizations for a clear and purpose-driven presentation. Duration: 18 lessons (5EC).

Website

Jurre Hageman

July 1, 2025

Presentation Grey Literature

PowerPoint presentation Workshop Grey Literature

Presentation

Norma Fötsch, Rebecca Calcott