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]

Successful writing of research statements and grant proposals for the next career steps after the PhD [more]

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]

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]

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]

German class for our new foreign IMPRS students [more]

This two-day workshop enables life scientists to communicate their research clearly and effectively. Through numerous writing examples and relevant exercises as well as class discussions, participants learn how to describe their work in a flowing narrative with a clear “take home message”. The interactive nature of the workshop means participants benefit not only from the experience of both instructors but also from the ideas of other participants. Additionally, writing samples from each participant are edited by the class instructors. The workshop teaches participants not only to tell the story of their research but also to direct their research using the writing process. [more]

Evaluation of the IMPRS for Organismal Biology [more]

The annual Grand Challenges in ... Symposium focus this year on Communication in Social Communities. [more]

Awarding the best IMPRS student paper of 2013 [more]

The Seewiesen based labs will present their labs, their research focus and methods. [more]

Concisely introducing yourself: practice your “pitch”, Engaging the audience in one’s talk, Affirming the strengths and individual style of the speaker, Improving body language, Effectively promoting oneself, Develop strong tactics for effective communication, Receiving video-feedback [more]

day 1: LM Linear Regression, multiple Regression ANOVA, ANCOVA (least-square method, parameterisation, interactions, tests (marginal and sequential), model selection, model assumptions, predictions, introduction to Bayesian data analysis); day 2: LME linear mixed models (maximum likelihood, restricted maximum likelihood, random and fixed effects, likelihood ratio test / bootstrap, random slopes-random intercept models, evt. further model types depending on the participants wishes); day 3: LME (model matrix, simulating posterior distributions of model parameters, predictions, posterior probabilities of hypotheses, preparing data for work on own data); day 4: work on own data and presentations. Prerequisite for participation: basic knowledge in statistics [more]

This five-day course is aimed at Biologists (PhD students and Master students) who work with medium to large datasets. The course goal is to learn how to re-arrange and query the data and how to best manage data. This course will teach researchers how to use the Unix shell, Python programming language, what databases are for and how to use them, to become more efficient at the conduction of the common but often time-consuming scientific task to deal with data. We will spend two days learning different techniques, and then we will move on and deal with your own data sets for two days. We will develop solutions for individual problems in the group. If the time allows it, we will move on to relational databases on the last day. When signing up, please send an exemplary data file that you work with, and which you need to re-arrange or query on a regular basis, but that you find difficult or time-consuming to do in Excel. You do not need to send a complete dataset, what we need to know is the main structure of the dataset, and the task that needs doing. Incomplete or exemplary datasets are sufficient. This course will use the operating systems of OS X (on a Mac) or in a Linux environment. Windows users should be prepared to install Linux on a partition of their laptop, or to install a software that emulates Linux (both are free of charge). Requirements: None. This course aims at people who find using Excel for data management time-consuming, boring and inefficient, but do not know how to do better. No previous experience in scripting is required. After completing this course, you will be able to use the power of your computer to time-efficently handle your data, which will allow you to spend more time doing actual research and analyses. [more]

Research workers in many fields are realizing the substantial limitations of statistical tests, test statistics, arbitrary α-levels, P-values, and dichotomous rulings concerning “statistical significance.” These traditional approaches were developed at the beginning of the last century and are being replaced by modern methods that are much more useful. These methods rely on the concept of information loss and formal evidence. They provide easy-to-compute quantities such at the probability of each hypothesis/model and evidence ratios. Furthermore, simple methods allow formal inference (e.g. prediction/forecasting) from all the models in an a priori set (“multimodel inference”). This course on the Information-Theoretic approaches to statistical inference focuses on the practical application of these new methods and is based on Kullback-Leibler information and Akaike’s information criterion (AIC). The material follows the recent textbook: Anderson, D. R. 2008. Model based inference in the life sciences: a primer on evidence. Springer, New York, NY. 184pp. A copy of this book, a reference sheet, and several handouts are included in the registration fee. These courses stress science and science philosophy as much as statistical methods. The focus is on quantification and qualification of formal evidence concerning alternative science hypotheses. The courses are informal and discussion and debate is encouraged.Registration deadline: September, 15. [more]

This five-day course is aimed at Biologists (PhD students and Master students) who work with medium to large datasets. The course goal is to learn how to re-arrange and query the data and how to best manage data. This course will teach researchers how to use the Unix shell, Python programming language, what databases are for and how to use them, to become more efficient at the conduction of the common but often time-consuming scientific task to deal with data. We will spend two days learning different techniques, and then we will move on and deal with your own data sets for two days. We will develop solutions for individual problems in the group. If the time allows it, we will move on to relational databases on the last day. When signing up, please send an exemplary data file that you work with, and which you need to re-arrange or query on a regular basis, but that you find difficult or time-consuming to do in Excel. You do not need to send a complete dataset, what we need to know is the main structure of the dataset, and the task that needs doing. Incomplete or exemplary datasets are sufficient. This course will use the operating systems of OS X (on a Mac) or in a Linux environment. Windows users should be prepared to install Linux on a partition of their laptop, or to install a software that emulates Linux (both are free of charge). Requirements: None. This course aims at people who find using Excel for data management time-consuming, boring and inefficient, but do not know how to do better. No previous experience in scripting is required. After completing this course, you will be able to use the power of your computer to time-efficently handle your data, which will allow you to spend more time doing actual research and analyses. [more]

Day 1: Binomial model - refreshing LM and LMM - introduction Bayesian data analysis - logistic regression, binomial model - model assumptions, overdispersion - tests, predictions Day 2: Poisson model - Poisson model - model assumptions, overdispersion - tests, predictions - depending on participants wishes: zero-inflation Day 3: GLMM - including random effects - glmer-function - depending on participants wishes: introduction to WinBUGS and more complex models Day 4: projects - work on own data and presentationsPrerequisite for participationModul 1 and 2, basic knowledge in statistics, linear models (ANOVA) and linear mixed models [more]