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Julia for Data Analysis in Ecology and Evolution
Provider: Faculty of Health and Medical Sciences

Activity no.: 3691-22-00-00 
Enrollment deadline: 18/09/2022
Date and time03.10.2022, at: 09:00 - 26.10.2022, at: 17:00
Regular seats14
Course fee7,440.00 kr.
LecturersMichael Krabbe Borregaard
ECTS credits5.40
Contact personKirsten Wivel-Snejbjerg    E-mail address: kws@adm.ku.dk
Enrolment Handling/Course OrganiserPhD administration     E-mail address: phdkursus@sund.ku.dk

Aim and content
This course is free of charge for PhD students at Danish universities (except Copenhagen Business School), and for PhD Students from NorDoc member universities. All other participants must pay the course fee.

Anyone can apply for the course, but if you are not a PhD student at a Danish university, you will be placed on the waiting list until enrollment deadline. This also applies to PhD students from NorDoc member universities. After the enrollment deadline, available seats will be allocated to applicants on the waiting list.


Learning objectives

A student who has met the objectives of the course will be able to:

1. Knowledge: demonstrate a global understanding of the Julia package ecosystem for ecology, geography and evolution

2. Skills: independently build reproducible Julia projects, including functions, documentation, scripts and interfaces with other frameworks

3. Competences: use Julia to analyse most relevant datatypes for ecology and evolution: macroecological datasets, phylogenetic trees, genetic data, raster files and ecological networks

4. Competences: create production-quality visualizations and reproducible reports in Julia


Content

Julia is a new and groundbreaking programming language for scientific computation and data analysis, with version 1.0 released in 2018. Though the syntax of Julia is similar to R or MatLab, the speed is very close to that of C or FORTRAN, the gold standard for speed in scientific programming. This does a lot more than just speed up analyses (which isn't a concern for many scientists): Importantly, it solves the "two-language problem". In R, which is used today by many ecologists, any performance-critical functionality in packages have to be implemented in a faster language such as C or Fortran. This makes library code essentially a black box for scientific users: it is written in a second language they may not know, and must be compiled to be used, preventing rapid iteration and interaction. This restricts package development to a small group of users, and greatly weakens the openness and transparency of scientific computing. Julia also solves the expression problem - removing traditional barriers between modelling projects. This facilitates new workflows an open-ended tools that foster collaboration within and between disciplines, opening new pathways in ecological and interdisciplinary research.

This course will serve as an introduction to the Julia programming language and its package ecosystem for ecological and evolutionary analysis, and the participants will perform a research project on their own data in Julia as the end of the workshop.

The course will start with two online days a week apart. The first day will provide a general introduction to Julia, presenting the most popular working environments (VisualStudio Code as an Integrated Development Environment, and Pluto as an interactive notebook), as well as Julia's syntax and basic concepts. Extra focus will be given to the type system and the idea of "multiple dispatch", which is the key programming paradigm of Julia. The second online day will be an overview and introduction to the package ecosystem for plotting and data manipulation.

The central part will be a 5-day in person course in Copenhagen, which will take a case-based approach to give in-depth introduction to the packages in the EcoJulia and BioJulia organisations, including SpatialEcology, GeoData, Phylo, Diversity, EcologicalNetworks, DynamicGrids/Dispersal and SimpleSDMLayers. Workshop participants are encouraged to submit data and R scripts for analyses they've already published; which may be included in the curriculum to ensure that it is fully relevant.

At the end, the students will do a three-day independent project using Julia on their own data.


Participants

The course is relevant to PhD students working with ecological or genetic analyses, which already have a good understanding of scientific data analysis. It is assumed that students are already proficient users of R, and the level of the course builds on that, though users of Python or Matlab should have no problem following the course. No previous knowledge of Julia is assumed – we will start by installing the software onto our computers.


Relevance to graduate programmes

The course is organized by the graduate programme for Life, Earth and Environmental Science. The course is relevant to PhD students from the following graduate programmes at the Graduate School of Health and Medical Sciences, UCPH:

Life, Earth and Environmental Science

Biostatistics and Bioinformatics.


Language

English


Form

The teaching will blend lectures, live coding demonstrations and practical coding exercises. The online component includes short introductions by the teachers but otherwise independent Julia exercise work.


Course director

Michael Krabbe Borregaard, Associate Professor, GLOBE Institute, mkborregaard@sund.ku.dk


Teachers

Michael Krabbe Borregaard, Associate Professor, GLOBE Institute, University of Copenhagen

Rafael Schouten, PhD candidate, GLOBE Institute, University of Copenhagen

Jakob Nissen, Assistant Professor, Center for Protein Research, University of Copenhagen

Timothée Poisot, Associate Professor, Université de Montreal

Richard Reeve, Reader, University of Glasgow, UK


Dates

Online 3 and 10 October 2022

On-site 17-21 October 2022

Project 24-26 October 2022


Course location

GLOBE Institute, University of Copenhagen


Registration

Please register before 18 August 2022


Seats to PhD students from other Danish universities will be allocated on a first-come, first-served basis and according to the applicable rules.
Applications from other participants will be considered after the last day of enrolment.

Note: All applicants are asked to submit invoice details in case of no-show, late cancellation or obligation to pay the course fee (typically non-PhD students). If you are a PhD student, your participation in the course must be in agreement with your principal supervisor.

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