Roland Kölliker: Catalogue data in Autumn Semester 2021

Name Dr. Roland Kölliker
Address
Molekulare Pflanzenzüchtung
ETH Zürich, TAN C 7.2
Tannenstrasse 1
8092 Zürich
SWITZERLAND
Telephone+41 44 632 45 53
E-mailroland.koelliker@usys.ethz.ch
DepartmentEnvironmental Systems Science
RelationshipLecturer

NumberTitleECTSHoursLecturers
751-1010-00LIntroduction to Scientific Methods Part II: Scientific Writing Restricted registration - show details
Only for Agricultural Sciences BSc.
2 credits4GR. Kölliker, J. Anderegg, A. Feurtey, A. K. Gilgen, M. Laub, A. Oberson Dräyer, B. Studer, F. Tamburini, D. J. Wüpper
AbstractDie Studierenden kennen die Grundlagen und die Konventionen des wissenschaftlichen Schreibens in den Naturwissenschaften, können wissenschaftliche Literatur suchen und verwalten sowie wissenschaftliche Publikationen analysieren. Sie setzen das Gelernte beim Schreiben eines eigenen Textes um.
ObjectiveDie Studierenden kennen die Grundlagen und die Konventionen des wissenschaftlichen Schreibens in den Naturwissenschaften. Sie setzen das Gelernte beim Schreiben eines kritischen Literaturberichtes zu einem agrarwissenschaftlichen Thema ihrer Wahl um. Die Lehrveranstaltung bereitet die Studierenden auf weitere schriftliche Arbeiten im Studium der Agrarwissenschaften vor, beispielsweise auf die Bachelor-Arbeit.
Lecture notesEs wird ein Skript abgegeben.
Prerequisites / NoticeDie Note für die LV Wissenschaftliches Arbeiten (Teil I: Grundlagen (WiA) und
Teil II: Wissenschaftliches Schreiben (WiSch)) setzt sich aus den Leistungen der Lehrveranstaltungen im
4. und 5. Semester zusammen. Die Note für WiSch (5. Sem.) zählt zu 80% zur Gesamtnote.
751-3801-00LExperimental Design and Applied Statistics in Agroecosystem Science3 credits2GA. Hund, W. Eugster, C. Grieder, R. Kölliker
AbstractDifferent experimental designs will be discussed and various statistical tools will be applied to research questions in agroecosystem sciences. Statistical methods range from simple analysis of variance to mixed-models and multivariate statistics. Surveys and manipulative field and laboratory experiments are addressed and students learn to analyse data using a hands-on approach.
ObjectiveStudents will know various statistical analyses and their application to science problems in their study area as well as a wide range of experimental design options used in environmental and agricultural sciences. They will practice to use statistical software packages (R), understand pros and cons of various designs and statistics, and be able to statistically evaluate their own results as well as those of published studies.
ContentThe course program uses a learning-by-doing approach ("hands-on minds-on"). The topics are introduced as short lectures, but most of the work is done on the computer using different packages of R – a software for statistical computing and graphics. In addition to contact hours exercises must be finalized and handed in for grading. The credit points will be given based on successful assessments of selected exercises.

The tentative schedule contains the following topics:
Introduction to experimental design and applied statistics in R
Data handling and data exploration with tidyverse
Designs of field and growth chamber experiments theory
Design creation with DiGGer
Fitting linear mixed-effects models with lme4
Marginal means estimation and post-hoc tests with emmeans
Nonlinear regression fits
Statistical learning techniques
Principle component analysis, canonical correpondence analysis (CCA), cluster analysis
Random forest

This course does not provide the mathematical background that students are expected to bring along when signing up to this course. Alternatively, students can consider some aspects of this course as a first exposure to solutions in experimental design and applied statistics and then deepen their understanding in follow-up statistical courses.
Lecture notesHandouts will be available (in English)
LiteratureA selection of suggested additional literature, especially for German speaking students will be presented in the introductory lecture.
Prerequisites / NoticeThis course is based on the course Mathematik IV: Statistik, passed in the 2nd year and the Bachelor's course "Wissenschaftliche Datenauswertung und Datenpräsentation" (751-0441-00L)
CompetenciesCompetencies
Subject-specific CompetenciesConcepts and Theoriesassessed
Techniques and Technologiesassessed
Method-specific CompetenciesAnalytical Competenciesassessed
Media and Digital Technologiesassessed
Problem-solvingassessed