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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.

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Erasmus University Library

January 1, 2026

Rubric to grade (simple) student presentations

This rubric can be used to grade student presentations after their VirtualShip expedition. This is a more simple version that students with little programming knowledge should be able to do sufficiently.

Document

Emma Daniels, Jamie Atkins, Erik van Sebille

2026

Modelling of Biobased Production Systems

Open and interactive book for the course Modelling of Biobased Production Systems, a mandatory MSc course for the programme Biosystems Engineering, and a restricted optional course for the programme Biobased Sciences at Wageningen University.

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Daniel Reyes Lastiri, Rachel van Ooteghem, Tim Hoogstad

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

December 16, 2025

Abstract redeneren tijdens programmeerlessen

Digitale vaardigheden is een belangrijk en nieuw onderwijsgebied in het basisonderwijs. Onderdeel hiervan is computational thinking, wat leerlingen helpt bij het oplossen van problemen door elementen uit de informatica te gebruiken. Abstractie speelt hierbij een belangrijke rol. Abstractie is een complex concept. Onze inspanningen hebben ertoe geleid dat het makkelijker wordt om abstractie te herkennen en observeren, aan de hand van abstractielagen. Hiermee kunnen leerlingen op het juiste moment adequate ondersteuningen krijgen. We bespreken twee onderzoeken: in het eerste onderzoek hebben we observeerbaar gedrag gekoppeld aan abstractie, en in het tweede onderzoek hebben we bovenbouwleerlingen laten programmeren en daarin ook de link gelegd met abstractie. Tijdens het tweede onderzoek hebben we gebruikgemaakt van PRIMM, een lesopzet specifiek voor programmeerlessen in het basisonderwijs, die gezien kan worden als een vorm van structuur en scaffolding. Uit beide onderzoeken komen interessante uitkomsten: patronen in hoe leerlingen op verschillende abstractielagen redeneren en hoe leerlingen elkaar kunnen beïnvloeden. Aan de hand van een aantal voorbeelden lichten we de resultaten nader toe. We zijn nog niet klaar, want de uiteindelijke stap is kijken hoe een leerkracht een interventie kan toepassen in dit proces. We hebben met onze onderzoeken wel een duidelijke richting in hoe we die interventie kunnen vormgeven.

Document

Hylke Faber

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.

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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

Introduction - Java

Learning outcomes: Demonstrate proficiency in creating, structuring, and documenting Java projects using IntelliJ, applying keyboard shortcuts and best practices for naming and commenting code. • Apply fundamental Java programming concepts including data types, arrays, operators, flow control, methods, constructors, packages, and imports to build well-structured applications. • Design and implement object-oriented solutions using classes, interfaces, inheritance, encapsulation, access modifiers, inner classes, and core API features such as collections, generics, and exception handling. • Employ advanced Java APIs and techniques such as functional programming, streams, multithreading, concurrency, file I/O, regular expressions, annotations, and unit testing with JUnit 5. • Build and deploy web applications using Spring Boot, implementing RESTful services, MVC patterns, dependency injection, Thymeleaf templating, session management, database integration, Ajax, and web security features.

Website

Michiel Noback