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Zoekresultaten (49)

HBO/WO

MBO

8 mei 2018

10-11: Data analysis (descriptive)- micro lecture MERGING DATA FILES

In this micro lecture the following items will be explained regarding this lecture: How to merge datametrices in spss

Website

Henk van der Kolk

6 november 2021

Julia Data Science

This is an open-source and open access book on how to do Data Science using Julia. The book describes the basics of the Julia programming language DataFrames.jl for data manipulation and Makie.jl for data visualization. You will learn to: Read CSV and Excel data into Julia Process data in Julia, that is, learn how to answer data questions Filter and subset data Handle missing data Join multiple data sources together Group and summarize data Export data out of Julia to CSV and Excel files Plot data with different Makie.jl backends Save visualizations in several formats such as PNG or PDF Use different plotting functions to make diverse data visualizations Customize visualizations with attributes Use and create new plotting themes Add LaTeX elements to plots Manipulate color and palettes Create complex figure layouts

Open (Access) Tekst Boek

Jose Storopoli, Rik Huijzer, Lazaro Alonso

2021

Storing information safely

A module to learn how to organise, manage en structure your data and files, so it is safe.

Website

Maastricht University Library

7 maart 2021

Tutorial Part 2. Introduction to sensitivity analysis with agent-based modeling

This resources includes: Docx-file for tutorial part 2 Folder with r- and data-files for the tutorial part 2.

Document

George van Voorn, Guus ten Broeke

7 maart 2021

Tutorial Part 1- Introduction to sensitivity analysis with R and NetLogo

This resource includes: Docx-file for tutorial part 1 Folder with r- and data-files for the tutorial part 1.

Document

George van Voorn, Guus ten Broeke

15 mei 2025

Write Your DMP: online module materials

These are the raw materials used in the online training module "Write Your DMP" created within Utrecht University's Moodle platform, by Utrecht University's Research Data Management Support. This module was developed to help Utrecht University researchers to write their Data Management Plan. It is a self-paced module that takes about 1.5 hours to complete. The module is structured as follows: 0: Introduction 1: Prepare: data collection, data reuse, data documentation and metadata 2: Handle: data privacy, security, ownership and storage 3: Share: data preservation, data sharing, FAIR data 4: Evaluation and glossary In the deposit, you can find the following files: The module content in the form of a storyboard per chapter in the online platform (WriteYourDMP_raw_materials_20250509.pdf) Self-created images used in the platform (materials_Ulearning.zip). A raw Moodle export of the module that can be imported in any other Moodle platform (backup-moodle2-course-1203-w_dmp_ub-20250515.mbz)

Document

Utrecht University Research Data Management Support

24 maart 2026

How the Data Logging System Works

Main Concepts: Data Log Manager as the core of the data logging system Enabling logging and debug options File naming, timestamping, file types, and delimiters Automatic creation of log folders Data collectors and their roles (continuous vs. event-driven logging) Ordering columns in CSV files Using Unity events to trigger data collection Learning Goals: Understand how to configure the Data Log Manager for logging data Learn how to organize and customize data logs, including file settings and folder structure Recognize the difference between continuous and event-driven data collection Understand how to link game events to data logging through scripts and Unity events Know when to seek developer assistance for custom data logging needs Key Takeaways: The Data Log Manager controls all aspects of data logging, including file creation and data formatting Data collectors can log data continuously or only when specific events occur Column order in CSV files can be customized using order values Unity events provide a flexible way to trigger data logging during gameplay Default settings simplify setup, but customization is possible for advanced needs Further Exploration: Search for “Unity Data Logging Best Practices” Search for “Unity Event System Tutorial” Search for “CSV File Handling in Unity” Read about “Custom Data Collectors in Unity” Explore “Runtime Folder Creation in Unity”

Video

Lucia M Rabago Mayer, Jordi Weldink, Amanda Poulin

31 juli 2025

Introduction to Research Data Management

Introduction to Research Data Management Audience: Students Level: introduction

WoDocument

Lucy O Shea, Sam Larson, Agustin Medina, Jessica Hrudey

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

2 juli 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