Courses open for registration

This course is the follow-up of and will build on the 2023 Participatory Plant Breeding and Resilient Seed Systems postgraduate course that also focused on smallholder engagement in plant breeding and seed system development.

In this course, the participant will be provided with both the basic understanding of the principles of multivariate methods and the skills needed to use those methods in his/her own work. The course consists of lectures and computer practicals using RStudio.

This is a two-day course that covers the theory and practical application of the boundary line methodology proposed by Webb (1972).

This is a refresher course aimed at PhD candidates. The level is that of a second course in Statistics. We will refresh basic knowledge of Probability, Statistical Inference (Estimation with confidence intervals and Testing), t-tests, standard cases of the Linear Model: Regression, ANOVA and ANCOVA. For our analyses we use software written in R. Programs will be provided. Other topics: Experimental Design, Non-parametric Tests, and Chi-square Tests. Some time is reserved to discuss statistical problems of the participants.

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.

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. The role of sample size, randomization and the reduction of unwanted noise factors will be highlighted. The way errors propagate will be discussed. The difference between experimental unit and measurement units and consequences for statistical analysis will be discussed.

In this module we discuss how to analyse data for which the assumption of independence is violated. So: Do you have a nested experimental set-up? Or repeated measurements? Or weight of the same animal over time? Or pseudo-replication? Then, you are likely to need Mixed Models. In this course, you will learn all about it!

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.

Machine learning plays an increasingly important role in many scientific areas, including geo-information science and remote sensing, ecology, biosystems engineering, and bioinformatics. Today, scientific data are growing in complexity, size, and resolution, and scientists are challenged to leverage available data to inform decision making. In this course, you will learn how to model patterns and structures contained in data, and evaluate data-driven models, i.e. models that learn directly from observations the phenomena under study.

Resilience is the capacity of a system to maintain or recover certain functions while undergoing shocks and stresses. It emerges from the many interactions between people and natural and/or artificial system components, and the capacity of people to adapt. During this course, the participants become acquainted with different resilience concepts and their application from an interdisciplinary perspective. Accordingly, we will address how resilience theory can be used to tackle fundamental and societal issues from a socio-economic and bio-physical perspective and will provide a critical reflection on the relevance, use, and applicability of the concept of resilience. The objective of this course is thus to connect resilience concepts to viable applications by offering an efficacious analytical/computational approach.

Classical statistics offers a powerful toolbox for data analysis. This toolbox, however, may not always be sufficiently flexible for modern data situations. The Bayesian framework allows for the integration and inclusion of information from many sources as well as a natural quantification of uncertainty in subsequent analysis.

Modelling is a crucial part of today's science. Among other things, models are used for assessing sensitivity of systems to disturbances or changes in external factors, and for predictions of future system states. This course provides an introduction to modelling with a focus on systems analysis using dynamic simulation models.

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.

While much of statistics focusses on associations between variables and making predictions, the aim of structural equation modelling is to establish causal relationships between variables. The focus will be on classical structural equation models with a small number of (latent) variables, but we will also give an introduction to recent developments on methodology for high-dimensional data.

This post-graduate course on Theoretical Ecology will focus on bifurcations in dynamical systems and participants will learn quantitative approaches to assess qualitative changes in biological systems that occur when conditions change.

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 for participants who want to deepen their knowledge of R programming and be able to use R to deal efficiently with computational problems and programming tasks. Participants will also gain more knowledge on working with R data structures and solving common problems, like working with and manipulating factors, extracting information from statistical models, working with other types of data (textual, time and dates) and handling multiple data files.

Microbial communities are vital to life on Earth.These invisible creatures catalyze global elemental cycles, regulate climate, and support human and environmental health and thereby play a critical role in mitigating climate change.