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Training Workshop: GIS and advanced statistical methods

The objective of the training workshop is to provide the students with a practical introduction to the use of advanced statistical methods and geo-information systems (GIS) for the analysis of Carbon (C) fluxes through the Land-Ocean Aquatic Continuum. It will be held at ULB, Brussels, Belgium, within the framework of the ITN C-CASCADES.

4 January 2016 - 8 January 2016
Belgium

The program will be divided in 5 blocks as follows:
- Short revision of basic statistical concepts and introduction to programming in Matlab (optional).
- Use of multivariate statics, with an emphasis on multiple regression analysis.
- Time-series analysis.
- Introduction to the use of neuronal networks as advanced interpolation methods.
- Use of GIS in hydrology and the analysis of lateral C transfers from soils to the coasts.

Each block will include a theoretical introduction presenting the methodology and an overview of scientific applications of these statistical methods to the C cycle. The emphasis, however, will be on hands-on exercises for which each participant will be provided with a workstation.

Matlab being necessary for the 4th block and a widely spread software within the research community, it will be used in most lessons. The 5th block will require specific GIS software. We selected QGIS/GRASS, which is open source, so that all students will be able to download and install it in their respective institutions. The first block is dedicated to learning the basis of Matlab and refreshing essential statistical principles. It is thus optional and intended for students with little to no experience with the software.

This workshop is open to all young researchers through application by submitting a motivation letter (up to 1 page) and a CV (up to 2 pages). The event itself is free to attend although any other expenses will be supported by the applicant.

More information on our website: http://c-cascades.ulb.ac.be/index.php/events-news/training-workshops

Keywords

Statistical methods, GIS, carbon fluxes, neural network