The course follows the geo-information cycle, guiding participants through data acquisition, storage, processing and visualisation.

The aim of this course is to provide an understanding of the statistical principles underlying experimentation. A proper set-up of an experiment is of utmost importance to be able to draw statistically sound conclusions.

In this module we study how to analyse data that are not normally distributed. We look at fractions (logistic regression), counts (Poisson regression, log-linear models), ordinal data (threshold models), and overdispersion. We discuss (quasi-) maximum likelihood estimation and the deviance.

Extend participants' basic knowledge of R by teaching them more advanced programming concepts and the use of R for more complex problem solving, going beyond just statistics.

Programming can serve multiple purposes. Purposes like developing applications and working with data are also very useful for research. For dealing with these issues, Python offers many libraries. Getting the skills of working with some of these libraries will enable future learning. This can be for more advanced programming applications, but also for self-learning to apply different libraries.

FAIR stands for “Findable, Accessible, Interoperable, Reusable” and is becoming increasingly important for sharing data, especially in research. We discuss the incentives and best practices of FAIR data management. 

This course introduces analog serious games (e.g. board and card games, narrative games) as tools to explore and foster food system transformation and challenges the participants to design new and/or adapt existing games and test them in a final event where they can showcase their prototypes. 

The aim of this course is to provide an introduction to R and R Studio. It introduces the participants to R language syntax, to enable them to write their own R code. They will also learn about R data-types and data-structures, and they will be taught how to explore the data and produce plots. The course will be a combination of lectures and practicals.

In this 5-day course, you will learn about the basics of computer vision, from the acquisition of good quality images to the use of Python programming to implement computer-vision solutions to extract relevant information for your domain. You will learn about more traditional image-processing techniques as well as state-of-the-art deep neural networks to process images and videos.

Classical statistics offers a powerful toolbox for data analysis. This toolbox, however, may not always be sufficiently flexible for modern data situations. 

Input data for spatial environmental models may have been measured in the field or laboratory, spatially interpolated, derived from remotely sensed imagery or obtained from expert elicitation. 

This course offers the basic theories in the field of remote sensing, starting from the information needs of various land applications. 

This course provides an introduction to modelling. Modelling concepts will be dealt with in detail, going through the basic steps to be taken. 

Researchers trying to summarize the constantly growing body of published research are increasingly using meta-analysis. The focus of this 2-day course will be on concepts of linear models and mixed linear models in meta-analysis. The statistical software R will be used.

Machine learning plays an increasingly important role in many scientific areas, including geo-information science and remote sensing, ecology, biosystems engineering, and bioinformatics. 

This course will empower attendees to maximize and defend their scientific interpretations based on their expert knowledge as informed by quantitative results. A new procedure, “causal knowledge analysis” will be introduced as a way to document expert knowledge and set the stage for SEM analyses. Once that is accomplished, structural equation modeling techniques will be presented as a methodology for representing and evaluating hypotheses involving networks of relationships.

The purpose of this course is to make the participants familiar with general statistical concepts describing uncertainty, and methods to compute prediction uncertainty and sensitivity coming from uncertain parameter values. 

This course aims to provide PhD candidates with a solid background in standard and more advanced geostatistical methods, such that they can apply these in their own research. The course is a mix of theory and practice, with case studies that are analysed using R and contributed geostatistical packages. 

This two-day workshop is aimed at providing a basic understanding of creating and evaluating de novo assembly using long read technologies. 

In this workshop, participants will learn the principle of tidy data, how to transform and combine datasets using the tools from the tidyverse and how to generate advanced visualization with the ggplot2 package.

This course is primarily aimed at participants who are in the start-up of a modelling project. We purposefully aim to involve people from different scientific backgrounds. The emphasis is not on the mathematical, computational, and statistical aspects of modelling per se, but on the elements of the modelling process before that. You will learn about the scoping of a model, and to think critically about the choices in modelling.

The course Multivariate Analysis offers a thorough introduction to multivariate statistical methods, tailored for researchers working with complex datasets where multiple variables are measured simultaneously.

This is a refresher course aimed at PhD candidates. The level is that of a second course in Statistics. 

This is a brief introductory workshop especially aimed at those who have little or no experience working with LaTeX. There will be a theoretical and a hands-on practical part. The theoretical part covers the basics of what TeX and LaTeX are, how they compare with Office text processors, and how to get started with writing documents. The practical is a hands-on demonstration of some of the more useful features of LaTeX, such as making Gantt charts, bibliography management and automatic acronym expansion.

In this module we discuss how to analyse data for which the assumption of independence is violated. In this course, you will learn all about it!

This course will provide the participants with an overview of a range of methods related to the five soil functions and will provide detailed practical training in a subset of measures. The training will be a combination of lectures, laboratory and field sessions (interactive lectures and practical sessions each day). Assessing a range of measurement types, from simple visual assessments in the field, to training in microscope identification techniques for nematodes and earthworms, and functional measures in the lab such as MicroResp. All methods described in the course will be made available to participants as well as advice on how to analyse the data.

The aim of the course is to help experienced R users to tackle the problems they face when analysing big data sets. The course will consist of a mixture of lectures and computer labs (in a ratio of approximately 60/40), so there is plenty of time for hands-on exercises.

The aim of this course is to introduce participants to the Shiny ecosystem and show them how to approach data exploration, storytelling and communication by going beyond static reports and graphs, and create engaging and interactive data stories.

This online course consists of 5 modules representing a total of approximately 40 hours of work. Each module consists of video files with short theory presentations, followed by exercises using real data sets, and video files discussing the solutions. 

This course presents a conceptual framework for ecological modelling: covering elementary growth models and probability distributions needed to mathematically model processes. The models are confronted with the data, using state of the art statistical methods. 

The course will focus on Concepts, such as: wicked problems, socio-ecological systems, complex systems, participatory modelling, simulation strategies.