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HBO/WO

MBO

8 mei 2018

10-23: Data analysis (descriptive)- ASSIGNMENT 4: HOW TO USE SPSS?

In this assignment the following items will be explained regarding this assignment: The aim of this assignment is to get to know SPSS and practice some basic aspects of this statistical program.

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Henk van der Kolk

8 mei 2018

07-25: Measurement - ASSIGNMENT 4: HOW TO USE SPSS?

In this assignment the following items will be explained regarding this assignment: The aim of this assignment is to get to know SPSS and practice some basic aspects of this statistical program.

Document

Henk van der Kolk

8 mei 2018

07-03: Measurement - micro lecture CHANGING VARIABLES IN SPSS USING RECODE

In this micro lecture the following items will be explained regarding this lecture: Changing levels of measurement of a variable in SPSS Recoding (into different variables)

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Henk van der Kolk

8 mei 2018

10-01: Data analysis (descriptive)- micro lecture A DATA MATRIX IN SPSS

In this micro lecture the following items will be explained regarding this lecture: A basic introduction to the data matrix in SPSS.

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Henk van der Kolk

8 mei 2018

07-02: Measurement - micro lecture A DATA MATRIX IN SPSS

In this micro lecture the following items will be explained regarding this lecture: A basic introduction to the data matrix in SPSS

Website

Henk van der Kolk

8 mei 2018

10-13: Data analysis (descriptive)- micro lecture SPSS FOR ABSOLUTE BEGINNERS

In this micro lecture the following items will be explained regarding this lecture: A basic introduction to SPSS. Stop de video every now and then, ‘do’ it.

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Henk van der Kolk

2025

A very short introduction to R

Being able to use a programming environment such as R is a valuable skill for our students. R provides opportunities for using larger and more complex datasets in practicals, case studies and theses. Compared to other programming languages, it is relatively easy to learn, which makes it suitable for education. Because R is open access, graduates can keep using it when they can no longer use the university’s software licences. “A (very) short introduction to R” teaches the basics in 1-2 hours with explanations and short exercises. This tutorial is used by lecturers worldwide. We expanded this tutorial to a series of self-study modules of 1-2 hours each. The modules can be used by: Lecturers who would like to use R in their computer practicals, but are afraid that learning R will consume time from the course content. The self-study modules enable these lecturers to use R with little time investment, since they can simply assign students to do (some of) the modules as preparation for their own computer practical. Students who lag behind in programming skills. The background of students in courses becomes increasingly diverse, which sometimes leads to discontent between advanced and less advanced students. With these modules the less advanced students can mend their deficits independently before they come to class, thereby levelling the differences between students and improving the learning experience of all. Students who are writing their BSc, MSc or PhD thesis. Without programming skills, students often fall back on Excel for processing and analysing data (which is often not suitable for the task) and their supervisors spend much time teaching them how to deal with the data. With the self-study modules, the supervisor’s time can be used more effectively to interact with the student about the scientific question to be tackled. Researchers and professionals who want to start using R. The modules are short and free of charge, which lowers the threshold for beginners.

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Claudia Brauer

2 juli 2025

Data Analysis and Visualization using R

Course is in English Learning outcomes: Apply Core R Programming Skills Learners will be able to write and execute R scripts using basic and complex data types, utilize built-in functions, and implement flow control structures to automate data analysis tasks. 2. Import, Clean, and Manipulate Data Learners will demonstrate the ability to read various data file formats, tidy and transform dataframes using both traditional methods and modern packages such as tidyr and dplyr, and handle date-time and text data efficiently. 3. Visualize Data Effectively Learners will create and customize data visualizations using the ggplot2 package, including revisiting advanced plotting techniques and adding interactivity to graphical outputs. 4. Perform Exploratory Data Analysis (EDA) Learners will conduct EDA using R, applying appropriate statistical summaries and visualizations to uncover patterns, trends, and insights from datasets. 5. Utilize R Packages and Resources Independently Learners will identify, install, and apply relevant R packages to extend functionality, troubleshoot code, and solve data analysis problems, including working through exercises and interpreting provided solutions. Duration Consists of 18 lessons Study points: 5 EC

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Michiel Noback

29 oktober 2025

A Novel Approach to Artificial Fracture Pattern Generation

In this recorded presentation from glasstec 2024, Leon Bohmann, from TU Darmstadt, focuses on the problem of spontaneously broken glass, specifically how it fractures into numerous fragments, a process which is not yet fully understood and currently requires expensive physical testing for safety validation. The speaker proposes a numerical simulation approach to predict these fracture patterns. By analyzing over 130 experiments, key data on fragment orientation and intensity relative to the impact point was gathered. The simulation method involves randomly distributing points based on this data, placing ellipses on them, and then using a watershed algorithm to generate realistic fracture patterns. The results show a promising visual and statistical match to actual broken glass, offering a potential alternative to costly physical tests. Special thanks to Messe Düsseldorf GmbH for providing the footage, and to Leon Bohmann for contributing to the Open GLASSroom OER collection. The creation of these resources has been (partially) funded by the ERASMUS+ grant program of the European Union under grant no. 2022-1-DE01-KA220-HED-000089750. Neither the European Commission nor the project's national funding agency DAAD are responsible for the content or liable for any losses or damage resulting of the use of these resources.

Video

Leon Bohmann