Statistics courses

General course flyer

Jump straight to:

• Introduction to our courses
• What type of courses do we teach?
• Our course material
• Attending an open course, or organising a course at your place
• Open courses in 2011 and 2012
• Where have we been in the past?

Introduction

Here are some of the comments from people who have done one of our courses.

• I did not realize that statistics can be fun.
• I wish I had done this course 10 years earlier.
• I need to rewrite all my papers.
• Can you come back for another course?
• I like your step by step didactical approach. 

Not many people enjoy statistics; our course participants did! Well, in as far as statistics can be enjoyed. We will throw protocols and flow charts at you, plus a certain amount of humor. We don't use toy data, but real data.

We have been in nearly every country in Europe for courses. From the warm south (Portugal, Spain, Italy, Greece) to the cold north (Norway, Finland, Lithuania). From the west (Ireland) to the east (Bulgaria). We have also been in the United Arabic Emirates, Mexico, and in the extreme south, Argentina and New Zealand.

All in all we have educated about 5000 ecologist. We have revisited some institutes more than 5 times. Various institutes run 2 courses per year. Some of our courses are part of national or EU funded MSc or PhD programmes. Other courses were funded by UNESCO or were part of EU projects.  

The course instructors are a statistician (Dr. Alain Zuur) and a biologist (Dr. Elena Ieno). Our biological and statistical backgrounds ensure a lively and enjoyable interaction with our participants. One of our strong points is that we can explain statistics in an understandable language.

Which courses do we teach?

Unfortunately, we cannot teach you everything in one week. We can entertain you for about 25 days; should you wish to do so. We provide a series of courses, see the table below.

Course	Content	Days	Pre-knowledge
1	Data exploration and visualization with R	3	-
2	Introduction to R (also available as an online course)	3	-
3	Data exploration, regression, GLM and GAM with R	5	Basic stats
4	Linear mixed effects modelling (dealing with heterogeneity, 1-way and 2-way nested data, temporal correlation and spatial correlation) with R	5	Courses 2 & 3
5	Zero inflated models and GLMM with R. With introduction to Bayesian statistics and MCMC. Keywords: zero inflation, temporal correlation, spatial correlation, 1-way nested data, 2-way nested data. GLMM. Overdispersion.	5	Courses 2 - 4
6	Multivariate analysis with R	5	-

In practice people do course 3, followed by course 4 or 5 six or twelve months later.  

Our course material

Our course material is based on our three Springer books Analyzing Ecological Data (Zuur et al. 2007), Mixed Effects Models and Extensions in Ecology with R (Zuur et al. 2009) and the best-seller A Beginner's Guide to R (Zuur et al. 2009).

       

The zero inflated module is taken from Zero Inflated Models and Generalized Linear Mixed Models with R  (Zuur et al., 2012), and the data exploration and visualization module comes from a book in preparation with the same title (Ieno & Zuur, In prep).

Your university library probably has a copy of one of our books. Have a look at the case study chapters; most are based on data sets from previous course participants and are in fact the exercises that you will be doing during the course.

Note that during the courses we discuss concepts, and we will avoid the (limited and isolated) matrix algebra that is in the books.

Attending or organizing a course

The easiest option is to participate in one of the open courses. Alternative, you can organize an open or in-house course at your institute. In-house courses are for a pre-set fee, and you decide who participates. With open courses, you only have to arrange the room; we do the rest.

However, there is a waiting list for new courses of about 12 months. Just email us if you have any questions.

Can you tailor a course?

Yes of course. In general our audience consists of postgraduate ecologists, and the theory and exercise data sets are relatively easy to grasp. Occasionally, we get requests to organize a course for a specific audience. For example, in October 2009 we ran a course for 12 forensic entomologists at the Institute of Forensic Medicine in Frankfurt, Germany. CSI viewers will know that these folks have quite interesting data sets; but rather different to what you will find in general statistical textbooks. The course organizer gave us a couple of data sets which we used as exercise data during the course, and we changed some of the examples during the lectures. 

Where do we run our courses?

Anywhere. We have given courses in the UK, Portugal, Ireland, Mexico, New Zealand, Argentina, Italy, The Netherlands, Finland, Spain, Germany, Lithuania, Norway, Bulgaria, etc.

Open courses in 2011 and 2012

Course	Remaining seats	Price
Data exploration, linear regression, GLM & GAM in R. With introduction to R. 6 - 10 February 2012. Coimbra, Portugal.	limited places available	600 - 650 Euro
Introduction to linear mixed effects modelling course. 13 - 17 February 2012. Lisbon, Portugal.	3 out of 25	550 Euro
Multivariate analysis course. 20 - 24 February. Klaipeda University, Lithuania.	5 out of 25	600 Euro
Data exploration, regression, GLM & GAM course. With Introduction to R. 11 - 15 June 2012. Banff, Canada	9 out of 25	800 CAD
Online follow-up course for participants who have attended our data exploration, regression, GLM & GAM course in R.	Email to register	100 GBP
Online R course based on 'Beginner's Guide to R' by Zuur et al. (2009). With access to on-demand video presentations.	Email to register	300 USD

 

More Detailed information:  

Online follow-up course for participants who have attended our data exploration, regression, GLM & GAM course in R.

• Description: This is an online follow-up course for people who have participated in our data exploration, regression, GLM and GAM course in R. During a period of 12 weeks we discuss a series of 12 exercises (2 exercises per 2 weeks). Participants have access to an online meeting website (we will use Adobe Connect) where they can ask questions on the exercises and discuss the problems/solutions with other participants and the instructors. This course is only open to people who have participated in our data exploration, regression, GLM & GAM course. No statistical theory will be discussed.  
• Video files with audio comments of the instructor are available on-demand (you need a decent internet connection to view these files). You can perfectly see the instructor's screen, R solution code and audio comments on the R coding and statistical approaches.
• Where: Online
• When: From January/February 2012 onwards
• Flyer: Click here.
• Course content (provisional):
  • 2 exercises on data exploration
  • 2 exercises on linear regression
  • 2 exercises on Poisson GLM
  • 2 exercises on negative binomial GLM
  • 1 exercise on binomial (binary) GLM and 1 exercise on binomial GLM
  • 2 exercises on GAM
• Course times:
  • Your own time  
• Pre-required knowledge:
  • R
  • Data exploration, regression, GLM and GAM
• Course fee:
  • 100 GBP. UK customers are subject to 20% VAT.
• Registration:
  • Please contact Dr. Alain Zuur by email: highstat@highstat.com. We will need your name and address for the invoice.

Online R course based on "A Beginner's Guide to R" by Zuur et al. (2009). Springer. With on-demand video presentations of theory and solutions.

• Description: This is an online course. Participants have access to an online meeting website (we will use Adobe Connect) where they can view online (and on-demand) video presentations, ask questions on the exercises and discuss the problems/solutions with other participants and the instructors.
• Examples of on-demand video demonstration files:
  • Sections 1 to 4 of Chapter 1 of A Beginner's Guide to R in Flash format (20 Mb).  Left-mouse click to go to the next slide. If you cannot view this file please contact us for another format (e.g. MP4, AVI). 
  • Sections 5 to 12 of Chapter 1 of A Beginner's Guide to R in Flash format (35 Mb).
  • These files are not streaming. You need to download and then play them.
• Where: Online
• When: From June 2012 onwards. A new course will start every 3th month.
• Flyer: Click here
• Course content (provisional):
  • Chapter 1 - 8 in Zuur et al. (2009). A Beginner's Guide to R. Springer. This book is not included in the course fee.
• Course times:
  • Your own time  
• Organization of the course:
  • This course takes place over the internet
  • During each course week you will be given access to a video presentation based on a chapter from A Beginner's Guide to R. These video files consist of Powerpoint presentations with audio (like a real course). You will also be asked to read the corresponding chapter.
  • Exercises will be provided and participants will be given access to a virtual meeting group (we will be using Adobe Connect). 
  • There are no set times when you must be online.
  • You can post questions and discuss the exercises with the instructors or fellow students.
  • The instructors will check your solution files.
• Detailed course content: See the TOC of our book for a more detailed course content
  • Week 1:
    • Chapter 1: Introduction. Installing R. Editors (Tinn-R versus RStudio).
    • Chapter 2: Getting data into R.
  • Week 2:
    • Chapter 3: Accessing Variables and Managing Subsets of Data
  • Week 3:
    •  Chapter 4: Simple Functions
  • Week 4:
    • Chapter 5: An Introduction to Basic Plotting Tools 
  • Week 5: Break (time to catch up)
  • Week 6:
    • Chapter 6: Loops and Functions
  • Week 7:
    • Chapter 7: Graphing Tools
  • Week 8:
    • Chapter 8: An Introduction to the Lattice Package
• Time required:
  • Weeks 1 - 4: Typically 5 hours per week.
  • Weeks 6 - 8: Typically 5 - 10 hours per week.
• Pre-required knowledge:
  • none
• Course fee:
  • 300 USD. UK customers are subject to 20% VAT.
• Registration:
  • Please contact Dr. Alain Zuur by email: highstat@highstat.com. We will need your name and address for the invoice.

 

Data exploration, regression, GLM and GAM course in R. With introduction to R. University of Coimbra, Coimbra, Portugal

• 6 - 10 February 2012.
• University of Coimbra, Coimbra, Portugal
• Course Flyer (Module 2)
• Course content:
  • Day 1: Introduction to R. Data exploration (outliers, collinearity, transformations, relationships, interactions).
  • Days 2 & 3: Linear regression (theory, model selection; stepwise versus information theory approach, interactions, sketching model fits).
  • Day 4: GLM for count data, binary data and proportional data (Poisson, negative binomial, binomial distributions) and how to deal with overdispersion.
  • Day 5: GAM for count data, binary data and proportional data. What to present in a paper or thesis.
• Course times:
  • 09.15 am -17.15 pm including 1 1/2 hour lunch break and a 20 minutes tea/coffee break both morning and afternoon.
• Pre-required knowledge:
  • Basic statistics
  • No R knowledge is required. You will learn R ‘on the fly’.
• Course fee:
  • 600-650 Euro. See:  http://www1.ci.uc.pt/imar/mvs_12/   (Module 2)
  • The course fee does not contain lunch, tea or coffee.
• Course material:
  • Chapters 4 and 5 of Zuur et al. (2007). Analysing Ecological Data. Springer.
  • Chapters 3, 8 – 10 in Zuur et al. (2009). Mixed effects models and extensions in ecology with R. Springer.
  • Pdfs of these chapters will be supplied.
• Registration:
  • See: http://www1.ci.uc.pt/imar/mvs_12/   (module 2)
• Note that this is an applied course aimed at biologists and ecologists. Approximately 50% of the time is used for exercises. We will hand out various pdfs of papers that we co-authored, R solution code and data sets of all exercises and presentations.

 

Mixed effects modelling course in R; Lisbon, Portugal

• Dates: Monday 13^th to Friday 17^th February 2012
• Location: Lisbon, Portugal.
• Course flyer and registration: http://www.sim.ul.pt/cciam/?id=cciam-courses
• Pdf file with detailed course information
• Course content:
  • Monday:
    • Dealing with heterogeneity in linear regression models (GLS). This is Section 4.1 in Zuur et al. (2009).
    • Exercise 1: Application of GLS, see Section 4.2.
  • Tuesday:
    • Explanation of linear mixed effects models for nested data. We will discuss the random intercept model, random intercept and slope model, REML, intraclass correlation, lme versus lmer, p-values. This is based on Sections 5.1 to 5.9 in Zuur et al. (2009).
    • Exercise 2: Application of mixed modelling on bee data. Chapter 19 contains a 12-page solution, including what to present in a paper.
    • Exercise 3: Application of mixed modelling combined with GLS techniques on a rat data set. 
  • Wednesday:
    • Exercise 4: Application of mixed effects modelling and additive mixed effects modelling on begging behaviour of nestling barn owls. This is Section 5.10.
    • Theory on mixed effect models for 2-way nested data.
    • Exercise 5: Application of mixed effects models on 2-way nested cetacean data. The analyses of this data set is presented as a case study chapter in Zuur et al. (2009), see Chapter 20. We will also hand out a published spin-off paper based on this chapter.
  • Thursday:
    • Adding more complicated correlation structures (spatial/temporal) to linear regression and mixed effects models, and smoothing models. This is based on Chapters 6 and 7 in Zuur et al. (2009).
    • Exercise 6: Analysis of a time series data.
    • Exercise 7: Analysis of a spatial forestry data set.
  • Friday:
    • Introducing GLMMs. This is partly based on Chapter 13 in Zuur et al. (2009).
    • Exercise 8: Application of GLMM and GAMM on the owl data. This is Section 13.2.2
    • Exercise 9: Application of GLMM and GAMM on deer data. This is Section 13.2.1 
• Remarks: Note that this is an applied course aimed at biologists and ecologists. Approximately 50% of the time is used for exercises. We will hand out various pdfs of papers on mixed effect models that we co-authored, R solution code and data sets of all exercises and presentations will be provided. .
• Course times: 09.00 am -17.00 pm including 1 1/2 hour lunch break and a 20 minutes tea/coffee break both morning and afternoon.
• Pre-required knowledge:
  • Data exploration techniques. Our paper on data exploration (Zuur et al., 2010) and Chapter 4 of Zuur et al. (2007) will be emailed to all participants, kindly read this prior to attending the course.
  • Linear regression. See Chapter 5 in Zuur et al. (2007) or Appendix A in Zuur et al. (2009). Or any other book on linear regression.
  • Knowledge of GAM is handy as some modules use smoothing techniques; however we do provide a short revision of GAMs. You may want to read Chapter 7 in Zuur et al. (2007), or Chapter 3 in Zuur et al. (2009). Or any other book on GAMs.
  • Basic R knowledge is required.
• Knowledge of GLM is needed for the GLMM module on Friday.
• Course fee: 550 Euro
  • The course fee does not contain lunch, tea or coffee.
  • To keep the course fee low, we request that participants obtain a copy of the course material themselves (see below) and there are no physical handouts. We will provide pdfs of the essential modules.
• Course material: We will follow:
• Chapters 4 - 7 in Zuur, Ieno, Walker, Saveliev and Smith. (2009). Mixed effects models and extensions in ecology with R. Springer.
• Your university library is likely to have a copy of this book or you may have online access to Springer books. Amazon book orders can take up to two weeks to arrive.
• To register: http://www.sim.ul.pt/cciam/?id=cciam-courses
• This is a non-technical course.
• See below for terms and conditions.

 

Multivariate Analysis in R. With introduction to R. Klaipėda University Coastal Research and Planning Institute, Lithuania

• Dates: Monday 20^th to Friday 24^th February 2012
• Location: Klaipėda University Coastal Research and Planning Institute, Lithuania
• Course flyer and registration
• Course content:
  • Day 1: Introduction to R. Data exploration following Chapter 4 in Zuur et al. (2007). Outliers, collinearity, transformations, relationships, interactions. Exercises in R.
  • Day 2: Measures of similarity (following Chapter 11 in Zuur et al. (2007). ANOSIM and the Mantel test to detect effects of covariates. MDS and NMDS. Exercises in R.
  • Days 3 & 4: Principal component analysis and redundancy analysis (Chapter 12 in Zuur et al. 2007). Exercises in R.
  • Day 4 & 5: Correspondence analysis and canonical correspondence analysis (Chapter 14 in Zuur et al (2007). Exercises in R.
  • Day 5: Discriminant analysis (Chapter 15 in Zuur et al. 2007) and time allowing, clustering techniques. Exercises in R. 
• Remarks: Note that this is an applied course aimed at biologists and ecologists. Approximately 50% of the time is used for exercises.
• Course times: 09.00 am -17.00 pm including 1 hour lunch break and a 20 minutes tea/coffee break both morning and afternoon.
• Pre-required knowledge:
  • Basic statistics
  • The course will start with an introductory module on R, so no R knowledge is pre-required
• Course fee: 600 Euro
  • The course fee does not contain lunch, tea or coffee.
• Course material. We will follow:
• Chapters 4, 10 - 15 of Zuur et al. (2007). Analysing Ecological Data. Springer.
• Pdfs of these chapters will be supplied.
• Various case study chapters in Zuur et al (2007) will be used as exercises.
• To register:
  • Please contact Jolita Petkuviene (jolita.petkuviene@corpi.ku.lt) and provide your contact details for the invoice. 
• This is a non-technical course.
• See below for terms and conditions.

 

Data exploration, regression and GLM & GAM. With introduction to R. Banff, Canada.

•  Monday 11th to Friday 15th June 2012
• Where: Park Canada, Banff, Canada
• Flyer: Click here.
• Course content:
  • Day 1: Introduction to R. Data exploration (outliers, collinearity, transformations, relationships, interactions).
  • Days 2 & 3: Linear regression (theory, model selection; stepwise versus information theory approach, interactions, sketching model fits).
  • Day 4: GLM for count data, binary data and proportional data (Poisson, negative binomial, binomial distributions) and how to deal with overdispersion.
  • Day 5: GAM for count data, binary data and proportional data. What to present in a paper or thesis.
• Course times:
  • 09.00 am -17.00 pm including 1 hour lunch break and a 20 minutes tea/coffee break both morning and afternoon.
• Pre-required knowledge:
  • Basic statistics
  • No R knowledge is required. You will learn R ‘on the fly’.
• Course fee:
  • 800 CAD
  • The course fee does not contain lunch.
• Course material:
  • Chapters 4 and 5 of Zuur et al. (2007). Analysing Ecological Data. Springer.
  • Chapters 3, 8 – 10 in Zuur et al. (2009). Mixed effects models and extensions in ecology with R. Springer.
  • Pdfs of these chapters will be supplied.
• Registration:
  • Please contact Dr. Alain Zuur by email: highstat@highstat.com. We will need your name and address for the invoice.
• Detailed course outline:
  • See course flyer
• Note that this is an applied course aimed at biologists and ecologists. Approximately 50% of the time is used for exercises. We will hand out various pdfs of papers that we co-authored, R solution code and data sets of all exercises and presentations. For terms and conditions, see: www.highstat.com/statscourse.htm

 

 

Course registration

To register for a course, please contact us by email (highstat@highstat.com). We need to know your name and address for the invoice and which course you would like to attend. Please read the terms and conditions below before registering.

Terms and conditions:

1. All fees are payable 6 weeks in advance and no admission to the course will be permitted until payment has been received. 
2. Course literature and software is copyright protected and may not be reproduced or distributed without permission.
3. For some courses, participants will be given a personal license for the software package Brodgar. The license will be valid for a period of 1 year. 
4. The course content might be modified or improved prior to the course start date. 
5. In most courses, we are able to cover all advertised modules. Unexpected factors like power cuts, food poissoning, airline strikes etc. may slow us down. Occasionally the numerical knowledge of the participants is slightly lower than expected. We will do our best to cover all modules, but sometimes this is not possible. No refund will be given if we cannot cover all modules.
6. Accommodation is not included as part of the course fee and participants are expected to make their own arrangements. 
7. Refunds (after deducting a 15% bank and administration fee) will only be given if a place is cancelled at least 6 weeks before the start date of the course.
8. A course participant can be substituted at any time before the start of the course. 
9. We reserve the right to cancel any course without liability other than a refund of the full course fee. 
10. Unfortunately, we cannot assist with VISA application. 
11. The courses are designed for biologists and we will only discuss concepts; no detailed mathematics. As such, these courses are  less suitable for statisticians who wish to learn all the mathematical details.  

Calendar  

The calendar below shows where we have been, are and will be.

 

Past, present and future courses

Here is a list of past, present and future courses. We have seen a lot of people and it was (is) great fun! 

2010 

• January 2010: In-house PhD course University of Aberdeen, UK.
• February 2010: Open course: regression, GLM & GAM. Dep. Zoologia da Universidade de Coimbra, Coimbra. Portugal.
• February 2010: Open course mixed effects modelling course in Balmedie, UK. Organised by Highland Statistics Ltd.
• March 2010: In-house course at the University of Hamburg, Germany.
• April 2010: In-house course at NIOZ, The Netherlands.
• April 2010: In-house course at the University of Helsinki, Finland
• May 2010: In-house course at the University in Ciudad Real, Spain
• May 2010: Open mixed effects modelling course in Coimbra, Portugal
• May 2010: In-house course at the University of the Azores, Portugal
• June 2010: In-house course at FEM, Italy
• August 2010: Open regression & GLM course in Norwich, UK
• August 2010: In-house mixed effects modelling course at IEO, Vigo, Spain
• September 2010: In-house regression, GLM course at UCC, Cork, Ireland.
• October 2010: In-house course regression, GLM & GAM at IMR, Norway
• October 2010: In-house course regression, GLM & GAM at NIOZ, The Netherlands
• November 2010: In-house R course at IMR, Norway
• November 2010: In-house data visualisation course at IMR, Norway.
• November 2010: Open R course in Evora, Portugal
• November 2010: Open zero inflation course in Evora, Portugal
• December 2010: In-house data exploration, regression, GLM, GAM and mixed modelling course at NIOZ, The Nehterlands (8 days).

2009

• January: In-house PhD course University of Aberdeen, UK.
• January: Two in-house courses at IMARES, IJmuiden, The Netherlands
• February 2009: In-house course at CSL, York, UK
• February 2009: Open course: regression, GLM & GAM. Dep. Zoologia da Universidade de Coimbra, Coimbra. Portugal.
• March 2009: Two in-house courses at National Avian Research Center, Abu Dhabi, UAE.
• April 2009: In-house course at NIWA, Auckland, New Zealand April/May 2009: Three courses at Cawthron, Nelson, New Zealand
• June 2009: In-house course at AZTI, Spain
• June 2009: In-house course Instituto de Investigación en Recursos Cinegéticos (Ciudad Real).
• July 2009: In-house course at University of York, UK
• August 2009: In-house course at IMR, Bergen, Norway
• September 2009: In-house course at UCC, Cork, Ireland.
• September/October 2009: Open course: Statistics in forensic entomology, Zentrum der Rechtsmedizin, Frankfort am Main, Germany.
• November 2009: In-house course at NIOZ
• November 2009: In-house course at IMR, Tromso, Norway
• December 2010: Course in Vigo
• December 2011: Course in Newburgh, UK

2008

• January 2008: In-house PhD course University of Aberdeen, UK. 
• February 2008: In-house SPSS course University of York, UK.
• February 2008: In-house mixed modelling, GLMM and GAMM course at IMARES, The Netherlands.
• March 2008: Open course: regression, GLM & GAM. Dep. Zoologia da Universidade de Coimbra, Coimbra. Portugal.
• March 2008: In-house regression GLM & GAM course, University of York, UK.
• April 2008: In-house time series course, IMR, Bergen, Norway.
• April 2008: Open course mixed modelling, GLMM and GAMM, Newburgh, UK
• May 2008: In-house ECOSUMMER training course.
• May 2008: In-house course at CSL in York, UK.
• June 2008: In-house course: Instituto de Investigación en Recursos Cinegéticos (Ciudad Real).
• July 2008: In-house regression, GLM & GAM course, AZTI, Spain.
• July 2008: Summer School "Statistics in Biology", Institute of Oceanology, Bulgarian Academy of Sciences in Varna, Bulgaria.
• September 2008: Open course: Regression and mixed modelling course, CSC, Helsinki, Finland
• September 2008: In-house mixed modelling course at UCC, Cork, Ireland.
• October 2008: In-house time series course at IMR, Bergen.
• October 2008: MSc course at Aberdeen Univertsity.
• November 2008: In-house course, University of York, UK.
• November 2008: In-house course for BioChange students, Galway, Ireland
• December 2008: In-house R course at IMR, Bergen, Norway.

2007

• February 2007: In-house course NIOZ, Holland.
• February 2007: In-house course Evora, Portugal.
• February 2007: In-house course Marine Laboratory, UK.
• March 2007: In-house course Marine Laboratory, UK.
• March 2007: In-house course NIOZ, Holland.
• March 2007: In-house course Newcastle University.
• April 2007: Open course regression, GLM & GAM course, Newburgh, UK.
• May 2007: Open course mixed modelling, GLMM and GAMM, Newburgh, UK.
• May 2007: R course at SAMS, Oban, UK.
• May 2007: Marie-Cure training course in Greece: ECOSUMMER training course
• June 2007: UNESCO funded course in Sofia, Bulgaria. Subject: regression, GLM & GAM.
• August 2007: In-house R course in Vigo.
• September 2007: In-house course at SAMS, Oban, UK. Subject: Linear mixed modelling and additive mixed modeling with emphasis on temporal correlation.
• September 2007: In-house course Zoology department in Cork, Ireland.
• October 2007: OPEN course: Mixed modeling, Newburgh, UK.
• October 2007: In-house course for MSc students at the University of Aberdeen, UK.
• October/November 2007: In-house time series analysis course at IMARES, The Netherlands.
• November 2007: OPEN course; Mexico (a series of 2 courses). 
• Instituto de Oceanografia, Faculdade de Ciências, Universidade de Lisboa, Portugal (2x)

2006 - 2002

• Zoology department in Cork, Ireland.
• As part of a Marie-Cure training site: Course in Vigo, Spain: ECOSUMMER training course
• Centro Interuniversitario di Biologia Marina and Agenzia Regionale per la Protezione Ambientale della Toscana, Livorno, Italy.  
• Universidad Nacional de Mar del Plata, Argentina. University of Aberdeen, Aberdeen, UK (7x).
• Newburgh, UK (5x). Also with Professor Ian T. Jolliffe as guest lecture. Faro, Portugal (4x).
• AWI, Helgoland, Germany. Instituto Superior de Agronomia, Centro de Informática, Tapada da Ajuda, Lisboa, Portugal.
• CNR-ISMAR, Ancona, Italy. Marine Institute, Ireland (4x).
• CORPI, Klaipeda University, Klaipeda, Lithuania.
• Universidad Autonoma de Mexico, Mexico City, Mexico.
• Central Science Laboratory, York, UK (4x).
• FRS Marine Laboratory, Aberdeen, UK (3x). Course as part of the EU WESTHER project at the 'Institut fuer Seefischerei Hamburg', Germany.
• FIMR, Helsinki, Finland.