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ISRIC - Spring school on mapping and assessment of soils
Monday 20 - Friday 24 May 2019
From 20 - 24 May 2019, ISRIC - World Soil Information will organise a Spring School on digital soil mapping, classification and assessment for soil and environmental scientists, students, soil experts and professionals in natural resources management. The spring school will take place at the Wageningen Campus in the Netherlands and will consist of two five-day courses that are run in parallel.
ISRIC - Hands-on Digital Soil Mapping
Monday 20 - Friday 24 May 2019
This course introduces methods and software for management, analysis and mapping of soil type and soil properties within the R environment for statistical computing. After this course participants will be able to apply the methods learnt to their own datasets.
Design of Experiments
Tuesday 21 - Thursday 23 May 2019
The design and analysis of experiments, using plants, animals, or humans, are an important part of the scientific process. Proper design of an experiment, apart from its proper analysis and interpretation, is important to convince a researcher that your results are valid and that your conclusions are meaningful.
Machine learning for spatial data
Monday 3 - Friday 7 June 2019
In this course participants will learn how to model patterns and structures contained in data. The course will be focused on statistical and machine learning approaches, where the relationships between the observed data and the phenomenon under study are learned directly from observations. Through a series of lectures and practical exercises (in Matlab), the participants will learn about different strategies and their pertinence for specific problems in environmental sciences. Most applications considered in the course will be remote sensing-based, but the course will remain general for a broader audience.
Linear Models
Wednesday 12 - Friday 14 June 2019
In this module we continue with Regression, ANOVA, and ANCOVA, set in the general framework of Linear Models. We look at topics like parameter estimation and interpretation, checking model assumptions, regression diagnostics, analysis of unbalanced designs and multiple comparisons.
Generalized Linear Models
Thursday 20 - Friday 21 June 2019
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.
Mixed Linear Models
Thursday 27 - Friday 28 June 2019
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!
Animal Movement Analysis
Sunday 30 June – Friday 5 July 2019
The aim of this course is to provide participants with skills to assist them in working with animal movement data including data management and organization, working with large tracking datasets, data exploration, visualization and analysis of movement data. The course combines several guest lectures from international experts in the field of animal movement research.
Geocomputation using free and open source software
Monday 8 - Friday 12 July 2019
This postgraduate course Geocomputation using free and open source software is an immersive 5-day experience opening new horizons in the use of the outstanding power of Linux and the command line approach for processing geospatial data. Jumpstart with R, Grass, Python, Gdal/Ogr library and linux operating system. We will guide newbies who have never used a command line terminal to a stage where they will be able to understand and use very advanced open source data processing routines. Our focus is to give you the tools and competencies to continue developing your skills independently. This self-learning approach allows participants to continue progressing and improving in an ever-evolving technology environment.
R and Big Data
Thursday 26 - Friday 27 September 2019
The main aims of this course are to introduce participants to Big Data and the similarities and differences between regular modeling approaches and big data modeling, to help them understand the possibilities and limitations of R in big data research, to introduce them to high performance computing and to reproducible research. This course is aimed at experienced R users and should not be seen as a course to learn R.