IMPRS Events 2015

IMPRS Events 2015

Location: Möggingen/Radolfzell
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Statistics Module 2: Linear Models and Linear Mixed Models with R

Statistics Module 2: Linear Models and Linear Mixed Models with R
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 R programming is required. Particularly, it is assumed that you are familiar with working with the R Console and an editor, reading the data and producing the most common graphics (histogram, scatterplot, boxplot). [more]
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Mastering your PhD

Mastering your PhD
The workshop aims to support PhD students during their first big research project. Generally a PhD student is well adapted to conduct his/her research but encounters difficulties with i.e. time management, lack of coordination/cooperation with others, insecurities if and how to ask for support and is maybe also not prepared to fulfil the different roles which are expected by him/her. This workshop will strengthen the management and communication skills of the participants and help them to deal with unexpected and frustrating situations. Participants will learn management and communication skills by: - defining goals and objectives in a smart way - planning their workload with efficiency and effectiveness - being clear about their different roles during a PhD project - improving their collaboration ability - learning techniques for a successful delegation. First day: Planning a project: Setting goals + Time management Introduction to project management, Defining aims and objective according to SMART principle, Planning and Structuring the workload of a project, Using time management to be more efficient, Defining different roles Second day: Delegation and communication Dealing with the supervisor, Successful delegation, How to prevent and avoid risks, How to deal with stress and frustration, Communication exercises Dr. Valeska Russo holds a PhD in inorganic Chemistry. After 10 years as project manager and trainer at Siemens and Nokia Siemens Networks, she started to teach project management and science related soft skills for PhD students, Postdocs and young group leaders in 2009. She focuses on management, communication and presentation and also teaches methods of awareness and stress reduction to reach a good work-life balance. [more]
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Experimental Design

Experimental Design
Day 1: Indroduction Introduction to experimental design theory (difference between experiment and observation, confounding, importance of randomisation, types of experimental designs, implications for data analysis); practical ( randomization with the software R, simple power calculations); Day 2: Introduction to experimental design theory (power); practical (simple power calculations) Exercise: students design an experiment based on a given research question. The proposed designs are discussed in class. Day 3: Applications - Each participant is asked to send a description of one planned experiment (or one running experiment if none is planned) to steffi.vonfelten@oikostat.ch until February 21st, 2015 (e.g., drawing of the spatial layout and text description, what measurements are taken and when?). Course participants will present the design of their own experiments. Each experimental design will be discussed in class and will also receive some feedback by the course teacher. Prerequisite for participation: basic knowledge of statistics and the software package R would be an advantage but is not absolutely required. [more]
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Introduction in Scientific Writing

Introduction in Scientific Writing
This two-day workshop enables life scientists to write clearly and with impact! The participants learn how to construct a “take-home” message that tells the story of their research, choose words that communicate their science clearly, and structure their paper into a flowing narrative. [more]
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Social Network Analyses

Social Network Analyses
Social network analysis is becoming a widely used tool for studying social behaviour of animals. However, getting started with animal social networks is often challenging. This workshop will introduce the fundamental concepts that are central in using this method correctly, including: - How to define edges and construct networks - How to visualise networks - How to interpret network metrics - How to perform permutation tests - How to test hypotheses with network data - Outstanding questions and future directions in animal social networks The formal parts of the workshop will be mostly discussion-based. This will be followed by some worked examples, and plenty of time for students to work on their own data. By the end of this workshop, participants should have a better understanding of considerations and assumptions that arise from using social data, and how statistical methods can address issues such as non-independence and sampling bias. Damien Farine will be starting as a Primary Investigator at the Max Planck Institute for Ornithology in December. Damien specialises on using quantitative approaches for studying the evolution and maintenance of sociality. He has been involved both in the developed of quantitative tools and their application in a range of empirical studies. His research on birds (both in wild and in captive experiments) and wild baboons uses simultaneous tracking of animal groups to gain insight into mechanisms that underpin the formation of social networks and the evolutionary consequences of group living. [more]
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Data Visualisation Workshop

Data Visualisation Workshop
This three-day workshop enables life scientists to effectively create figures based on quantitative data that add impact to their publications. The workshop is divided into two one-day modules: Principles and Applications. On the first day, the Principles module focuses on understanding the purpose of a figure, choosing the most appropriate plot type, and the science of perception. The first day is primarily concerned with the art of visual communicaiton and integrates participants’ own examples into the teaching process. On the second and third day, the Applications module focuses on the practical implementation of the data visualisation principles discussed on the first day. This is done using the R statistical programming environment with the participants’ own data. Sample Submission Participants are asked to provide a sample visualisation of their own results that will be used as a teaching example on the first day. Requirements Participants should already be proficient with R. A detailed list of suggested R functions and concepts is provided in a pre-workshop self-assessment for participants. Participants are strongly encouraged to bring in their own data sets and computers for practical work on the second day and should have the following cross-platform software pre-installed: R – v3.0 or later (http://www.r-project.org/) RStudio – v0.97 or later (http://rstudio.org/download/desktop) JGR – v1.7-14 or later (http://rforge.net/JGR/index.html) [more]
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Statistics Module 3: Generalised linear models and generalised linear mixed models

Statistics Module 3: Generalised linear models and generalised linear mixed models
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 presentations Prerequisite for participation Modul 1 and 2, basic knowledge in statistics, linear models (ANOVA) and linear mixed models [more]
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