Werner Eugster: Katalogdaten im Herbstsemester 2021 |
| Name | Herr Prof. Dr. Werner Eugster |
| Departement | Umweltsystemwissenschaften |
| Beziehung | Titularprofessor |
| Nummer | Titel | ECTS | Umfang | Dozierende | |||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 751-0441-00L | Wissenschaftliche Datenauswertung und -präsentation  | 2 KP | 2G | W. Eugster | |||||||||||||||||
| Kurzbeschreibung | Arbeiten mit wissenschaftlichen Daten von der Datenübernahme aus Excel über stat. Analyseverfahren bis zu grafischen Darstellungsformen. In Übungen mit der Software R/RStudio wird das methodische Werkzeug zur Daten-Auswertung und -Präsentation in Form von wissenschaftlich adäquaten grafischen Darstellungen erklärt anhand von Daten aus einem Versuch mit Prof. E. Frossard aus dem Vorsemester. | ||||||||||||||||||||
| Lernziel | Diese Veranstaltung soll die Studierenden mit den statistischen Analyseverfahren, die im Rahmen einer Bachelorarbeit benötigt werden (deskriptive Statistik, linear Regression, einfache Varianzanalyse usw.) vertraut machen und ihnen Gelegenheit bieten, im Rahmen geleiteter praktischer Übungen mit der Daten-Analyse-Software R/RStudio anhand ausgewählter Beispiele das methodische Werkzeug zur Daten-Auswertung und -Präsentation kennen zu lernen. Ein wichtiger Schwerpunkt wird die Vermittlung geeigneter grafischer Darstellungsarten sein (wie präsentiert man Daten anschaulich und wissenschaftlich korrekt?). | ||||||||||||||||||||
| Inhalt | Voraussichtliche Kursschwerpunkte: - Einführung - Einführung in 'R' - Daten einlesen und darstellen - Vorbereitung Daten aus Kurs mit Prof. E. Frossard / 4. Sem. - Korrekte und problematische grafische Darstellungen - Verteilungen und Konfidenzintervalle - Statistische Tests - Repetition und Anwendung - Korrelationsanalyse - Lineare Regression - Analysis of Variance (ANOVA) - ANOVA-Diskussion der Resultate mit Prof. E. Frossard In der letzten Doppelstunde: Leistungskontrolle | ||||||||||||||||||||
| Skript | Hauptsächlich Deutsch (mit englischen Abschnitten aus Lehrbüchern) | ||||||||||||||||||||
| Voraussetzungen / Besonderes | Theoretisches Wissen in Statistik aus der Vorlesung mit Übungen des 4. Semesters; erfüllte Leistungskontrolle dieser Veranstaltung | ||||||||||||||||||||
| 751-3801-00L | Experimental Design and Applied Statistics in Agroecosystem Science | 3 KP | 2G | A. Hund, W. Eugster, C. Grieder, R. Kölliker | |||||||||||||||||
| Kurzbeschreibung | Different 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. | ||||||||||||||||||||
| Lernziel | Students 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. | ||||||||||||||||||||
| Inhalt | The 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. | ||||||||||||||||||||
| Skript | Handouts will be available (in English) | ||||||||||||||||||||
| Literatur | A selection of suggested additional literature, especially for German speaking students will be presented in the introductory lecture. | ||||||||||||||||||||
| Voraussetzungen / Besonderes | This 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) | ||||||||||||||||||||
| Kompetenzen |
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| 751-5101-00L | Biogeochemistry and Sustainable Management Findet dieses Semester nicht statt. | 2 KP | 2G | W. Eugster, V. Klaus | |||||||||||||||||
| Kurzbeschreibung | This course focuses on the interactions between ecology, biogeochemistry and management of agro- and forest ecosystems, thus, coupled human-environmental systems. Students learn how human impacts on ecosystems via management or global change are mainly driven by effects on biogeochemical cycles and thus ecosystem functioning, but also about feedback mechanisms of terrestrial ecosystems. | ||||||||||||||||||||
| Lernziel | Students will analyse and understand the complex and interacting processes of ecology, biogeochemistry and management of agroecosystems, be able to analyze large meteorological and flux data sets, and evaluate the impacts of weather events and management practices, based on real-life data. Moreover, students will be able to coordinate and work successfully in small (interdisciplinary) teams. | ||||||||||||||||||||
| Inhalt | Agroecosystems play a major role in all landscapes, either for production purposes, ecological areas or for recreation. The human impact of any management on the environment is mainly driven by effects on biogeochemical cycles. Effects of global change impacts will also act via biogeochemistry at the soil-biosphere-atmosphere-interface. Thus, ecosystem functioning, i.e., the interactions between ecology, biogeochemistry and management of terrestrial systems, is the science topic for this course. Students will gain profound knowledge about biogeochemical cycles and greenhouse gas fluxes in managed grassland and/or cropland ecosystems. Responses of agroecosystems to the environment, i.e., to climate and weather events, but also to management will be studied. Different meteorological and greenhouse gas flux data will be analysed (using R) and assessed in terms of production, greenhouse gas budgets and carbon sequestration. Thus, students will learn about the complex interactions of a coupled human-environmental system. Students will work with real-life data from the long-term measurement network Swiss FluxNet. Data from the intensively managed grassland site Chamau will be used to investigate the biosphere-atmosphere exchange of CO2, H2O, N2O and CH4. Functional relationships will be identified, greenhouse gas budgets will be calculated for different time periods and in relation to management over the course of a year. | ||||||||||||||||||||
| Skript | Handouts will be available on the webpage of the course. | ||||||||||||||||||||
| Literatur | Will be discussed in class. | ||||||||||||||||||||
| Voraussetzungen / Besonderes | Prerequisites: Attendance of introductory courses in plant ecophysiology, ecology, and grassland or forest sciences. Knowledge of data analyses in R and statistics. Course will be taught in English. | ||||||||||||||||||||