Admission requirements
This course is for MSc students in Biology.
Contact information
Coordinator: Dr. R.A. Vos
Email: rutger.vos@naturalis.nl
Description
The aim of this course is to introduce methods and techniques that are applied to the large-scale analysis of biodiversity data. Common threads are the management and analysis of large volumes of biodiversity data and the challenges posed by this in terms of reproducibility and scalability, but these are applied to cases of different dimensionality, namely:
sequential (1D) data, e.g. DNA sequence data sets for assessing species diversity in samples
grid and pixel (2D) data, e.g. images for assessing phenotypic diversity
physical (3D) objects, e.g. from scanning, to study functional adaptation and evolution
the physical through time (4D), e.g. geospatial time series to study species distributions
Learning goals
Course objectives:
To provide practical experience in handling and analyzing data in ways that meet the requirements of modern, open, scientific research. This means to be able to manage the provenance of data from the point of acquisition, to be able to analyze data in a reproducible manner, and to be able to share data, analysis workflows, and analytical environments with other researchers.
Final qualifications:
Theoretical understanding and hands-on experience in managing and analyzing biodiversity data, including DNA sequences, images, 3D objects, and geospatial data. Hands-on experience will include computational skills such as basic scripting, using HPC-like architectures, and rationally managing project inputs, processes, and outputs.
Timetable
From 27 November 2017 to 22 December 2017. Programme details will be announced via Blackboard.
Mode of instruction
Lectures, computer exercises, literature study, demonstrations.
Assessment method
Written reports, oral presentation, and examination.
Blackboard
All information of lectures and papers will be available on Blackboard.
Reading list
Relevant literature will be made available on Blackboard.
Registration
via USIS and enroll in Blackboard
Exchange and Study Abroad students, please see the Prospective students website for more information on how to apply.