Past events 2012

Oral Presentation Workshop

The participants will train oral scientific presentations. [more]
Which tracking devises for which questions and which animals? [more]

Guided Analyses of Own Data

day1: workshop (short presentation of the project proposal by each participant, 3-4 lectures: topics depend on the participants’ projects; e.g. repetition mixed model or other aspects of linear models, extensions of linear models, spatial models, time series, zero-inflation models, multivariate methods, analyzing time to event data, compositional analyses, two-level ecological models, work individually or in groups on own projects); day 2 workshop (work individually or in groups on own projects, discussion of problems in plenum or in groups); day 3 workshop and presentations (work individually or in groups, presentation of projects and discussion) Requirements Modul 1, basic knowledge in statistics, linear regression, ANOVA, one of module 2 or 3 is recommended; A short proposal of the workshop project has to be sent to Fränzi Korner-Nivergelt three days before the start of the workshop [more]

Introduction of Scientific Paper-Writing

introduction in manuscript-writing course. Participants are expected to work on their own manuscripts during the course. [more]

Writing of Research Statements and Grant Proposals

Successful writing of research statements and grant proposals for the next career steps after the PhD [more]
Grand Challenges in Evolutionary Ecology [more]
The Konstanz and Radolfzell based labs will present their labs, their research focus and methods. [more]

Coping with the Challenges of a PhD

This course provides in-depth guidelines on how to cope with the most common challenges involved in researching and writing a PhD. This training course equips you with the tools and techniques you need to complete your PhD successfully and on schedule. You learn how to work more efficiently, how to save time, and how to identify and focus on the essentials. You learn how to employ management tools to monitor your progress, as well as gaining a greater understanding of how to optimize supervision and how to get the support you need. You learn how to plan the thesis-writing process and how to incorporate writing tasks into your normal working day. You gain an increased awareness of the career choices open to you, and of what might be the best career options for you personally. This training course will help increase your satisfaction with life as a PhD student, and to become a fully professional academic. [more]

Introduction of basic statistics with R (Module 1)

day 1: introduction to R (work with console and editor, read in data, save data, basics in programming R, graphics, classical tests); day 2: basics in statistics (refreshing descriptive statistics (mean, sd, se, median, quartiles), introduction to different schools of statistics (frequentist, information theory, Bayes statistics), theory of statistical tests (example t-test and randomization test), classical tests (U-test, chi-test, binomial test, correlation, etc.); day 3: experimental design (basic theory in experimental design, presentation of an experimental design by each participant (own or prepared examples), discussion of experimental design, use R to plan experiments, power calculations) [more]

Scientific Integrity

This seminar is designed to assist PhD students in gaining a better awareness of the importance of ethics in science; to provide them with a set of criteria for assessing ethical dilemmas; to facilitate a room for free discussion on real and fictitious cases of scientific misconduct (mainly, fabrication and falsification of data), and to offer an overview of current MPS guidelines on scientific integrity. [more]

Outdoor First Aid

Four days of practical first aid training for being in a remote field-situation. Situations like: what to do if my leg is broken and the field station is 10km away and I cannot contact anyone? [more]

R for Biologists I: Introduction course in R programming language

This course will allow for one week of intense introduction in R a powerful opensource programming environment widely used in scientific research. We will begin with understanding how we can wrok with R to make our lifes as biologists from a wider range of subdisciplines easier. Consequently, we will want to undestand how data can be mined, rearranged and basic visualisations made. This is not a statistics course, it is intended to give a general all purpose introduction in R, from where further exploration can be achieved without the usually steep initial learning curve. [more]

Intensive German Class

German class for our new foreign IMPRS students [more]
Intensive manuscript-writing course. Participants are expected to work on their own manuscripts during the course. Requirements: Participation in the Introduction in Scientific Paper-Writing course or experiences in manuscript writing. [more]

Linear Models and Linear Mixed Models with R (Module 2)

day 1: LM (linear regression, multiple regression, ANOVA, ANCOVA, least-square method, parametrisation, interactions, tests (marginal and sequential), model selection, model assumptions, predictions); day 2: LME (linear mixed models, maximum likelihood, restricted maximum likelihood, random and fixed effects, likelihood ratio test / bootstrap, random slopes-random intercept models, depending on participants further model types); day 3 LME (Bayesian way of fitting a linear model, model matrix, simulating posterior distributions of model parameters, predictions, posterior probabilities of hypotheses, preparing data for work on own data); day 4: projects (work on own data and presentations) Requirements Modul 1, basic knowledge in statistics [more]

Generalized Linear and Generalized Linear Mixed Models with R (Module 3)

day 1: binominal model (repetition LM, logistic regression, binomial model, tests, model assumtions, overdispersion, predictions); day 2: poisson model (poisson model, tests, model assumptions, overdispersion, predictions, depending on participants: zero-inflation, mixture models); day 3 GLMM (including random effects, Bayesian way of fitting a model, glmer-function and MCMCglmm-finction, depending on participants: introduction to WinBUGS and further mixture models); day 4: projects (work on own data and presentations) Requirements Modul 1 and 2, basic knowledge in statistics, linear models (ANOVA) and linear mixed models [more]
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