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Zoekresultaten (39)

HBO/WO

MBO

8 mei 2018

08-08: Data collection methods- micro lecture NON COMPARATIVE CONTENT ANALYSIS

In this micro lecture the following items will be explained regarding this micro lecture: Introducing non-comparative content analysis

Website

Henk van der Kolk

8 mei 2018

08-02: Data collection methods - micro lecture CONTENT ANALYSIS

In this micro lecture the following items will be explained regarding this micro lecture: (Different types of) content analysis Inductive and deductive coding Coding Coding scheme and ‘labels’

Website

Henk van der Kolk

2023

Lecture Notes chapter 3: Uncertainty Analysis

This "Maker Lab" course uses Jupyter Notebook files and the Python programming language for data analysis and visualization and uncertainty analysis. Chapter 3 of the lecture notes introduces Uncertainty Analysis using Python. Note: If you do not (yet) have Jupyter Notebook, you cannot fully utilize the .ipynb file. But the attached PDF will give an idea of its content and functionality. Note also: In order to interact with and fully utilize the Jupyter Notebook documents (including their hyperlinks and pointers to data and image files), it is helpful to save the entire collection locally in a way that preserves the original directory structure. This is easiest to accomplish by downloading all files at once via the JupyterPython-materials zip file and unpacking it (extracting the files) to your preferred local directory. This zip file of all Jupyter Notebook materials is located here: https://search.edusources.nl/materialen/d144774e-9705-489a-b3cd-28e3618f7324

Document

Forrest Bradbury

2023

Maker Lab Assignment: Programming and Data Analysis: Python Exercises

Before the Maker Lab students begin the open inquiry phases, they first learn and practice some important skills, supported by small assignments. In this assignment they are tasked with using a worksheet in Jupyter Notebook and the Python programming language for data analysis and visualization and uncertainty analysis of a provided data set. The analyzed data set is generated quickly via an online survey completed by all students. Note: If you do not (yet) have Jupyter Notebook, you cannot fully utilize the .ipynb file. But the attached PDF will give an idea of its content and functionality. Contents: student worksheet in Jupyter Notebook: Programming-and-DataAnalysis_Python-Exercises.ipynb (and .pdf) instructor link to Google Forms survey for data collection: https://docs.google.com/forms/d/e/1FAIpQLSfWsdadhq-v3ubt1p3EIkzxAVyAyQ-1hBYst_RodV7PEp9E7A/viewform?usp=sharing example of data file: example_Data_Collection_For_Python_Exercises.csv

Document

Forrest Bradbury

26 oktober 2025

Comparative Analysis of the Glass Plate Design

In this video, Ivana Rakić presents a comparative analysis of the economic impact of designing glass elements according to different standards, focusing on the new European draft standard FprEN 19100-1(2,3):2023, the existing EN 16612:2019, and the American ASTM E1300-16. The study examines common double-glazed insulating units under wind load, using the minimal required glass thickness for loadbearing resistance as the key comparison criterion. The results indicate that for annealed glass, all three standards yield similar economic outcomes. However, for heat-treated glass like heat-strengthened and thermally toughened glass, the new FprEN 19100 standard leads to a lower capacity utilisation factor, making it the most economical design choice, while the ASTM standard is the least economical. The analysis concludes that the choice of standard significantly influences design economy and highlights the need for further research incorporating factors like safety and long-term performance for a more comprehensive understanding. The video was produced by the Open GLASSroom project. Special thanks to Ivana Rakić and Nebojša Buljan 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

Ivana Rakić, Nebojša Buljan

8 mei 2018

08-11: Data collection methods- micro lecture TYPES OF DATA COLLECTION METHODS

In this micro lecture the following items will be explained regarding this micro lecture: An introduction to data collection methods A classification of data collection methods Examples: Surveys, content analysis and focusgroup analysis

Website

Henk van der Kolk

1 september 2017

Extreme value analysis

This presentation concerns: How do we choose flowrate or water levels to design hydraulic structures? Note that the powerpoint version may differ slightly from the PDF version. Please consult the PDF for the original content.

Presentatie

Jeremy Bricker

8 mei 2018

08-01: Data collection methods- micro lecture CONCEPTUALIZATION USING INTERVIEWS

In this micro lecture the following items will be explained regarding this micro lecture: Using interviews to develop new concepts

Website

Henk van der Kolk

8 mei 2018

02-02: Concepts - micro lecture CONCEPTUALIZATION USING INTERVIEWS

In this micro lecture the following items will be explained regarding this lecture: Using interviews to develop new concepts

Website

Henk van der Kolk

2023

Lecture Notes chapter 2: Introduction to Python Programming

This "Maker Lab" course uses Jupyter Notebook files and the Python programming language for data analysis and visualization and uncertainty analysis. Chapter 2 of the lecture notes introduces the Python programming language. It consists of four sub-chapters, each published as separate files. Note: If you do not (yet) have Jupyter Notebook, you cannot fully utilize these .ipynb files. But the attached PDFs will give an idea of their content and functionality. Note also: In order to interact with and fully utilize the Jupyter Notebook documents (including their hyperlinks and pointers to data and image files), it is helpful to save the entire collection locally in a way that preserves the original directory structure. This is easiest to accomplish by downloading all files at once via the JupyterPython-materials zip file and unpacking it (extracting the files) to your preferred local directory. This zip file of all Jupyter Notebook materials is located here: https://search.edusources.nl/materialen/d144774e-9705-489a-b3cd-28e3618f7324

Document

Forrest Bradbury