We are organizing the sixth edition of our intensive class in data-driven ecological synthesis, with support from the NSERC BIOS² CREATE program and the Fondation Courtois. Enrolment is limited to 30 students. The course will run online only from the first week of May 2021, for six weeks, with three meetings every week.
The class is free for students registered at a Québec university, and the registration fees are (approx.) 800 CAD for other attendees. We will automatically award a no-questions-asked grant equal to this amount to attendees from under-represented minorities in STEM, members of Canada’s “designated groups” for EDI initiatives (women, people with disabilities, Aboriginal peoples, and visible minorities), as well as members of the LGBTQIA+ community; we will also award several grants for the same amount to attendees who can justify the need for it.
In a departure from the usual format of this course, this year’s cohort will work collaboratively on a single project: predicting interactions between viruses and mammals. This project will be led by and done in collaboration with the VERENA consortium. We will put a specific emphasis on building up skills related to machine learning (building recommender systems, ensemble models) and the automation of synthesis of large amounts of data. A group of researchers with expertise in data science, virology, public health, and disease ecology, will be mentoring the different projects over the course of the six weeks.
The application deadline is Dec. 31, 2020 - please apply on-line using this link.
Goals and target audience
This intensive class will give early-career biologists the tools and skills needed to interact with, manage, clean, and analyze data. We will put a great deal of emphasis on the transparency and reproducibility of the data transformation pipeline. We will specifically do so by working on leveraging open data to address novel questions about ecological systems. You can read more about the approach of computational ecology in a paper co-authored by students from the 2017 cohort.
We expect that the attendees can turn the computer on or be willing to learn how to do so. We will take care of the rest from here. A passing knowledge of statistics or mathematics and programming is a good thing, but eagerness to learn is far more important than previous knowledge; we always strive to have a balance between novice and advanced users. Due to this year’s topic, we are particularly eager to receive applications from learners with a background in infectious diseases, virology, public health, species distribution modeling, or genomics.
The class is open to all, and previous attendees included undergraduate and graduate students, staff scientists, data analysts, and post-doctoral fellows. We think it is particularly relevant for Ph.D. students and post-doctoral fellows.
The core skills we will cover during the class are
- best practices for data management
- acceptable practices in scientific computing
- good-enough practices for programming with data
- useful statistical and mathematical tools
- data cleaning and data analysis tools
- parallel and distributed computing for data processing
- data visualization and presentation
- automation and reproducibility of analyses
The class is extremely participatory, where a lot of training modules are delivered based on the learners’ needs. Some of the modules delivered over the last years included Bayesian statistics, code optimization, and introduction to GIS.
The summer school will be led by Timothée Poisot and will include guest appearances by other investigators from the VERENA consortium.
Timothée is a researcher at the Québec Center for Biodiversity Sciences and an associate professor in Quantitative and Computational Ecology at the Université de Montréal. He is a certified Software Carpentry and Data Carpentry instructor, serves on the editorial board of Ecology Letters, Methods in Ecology & Evolution and PLOS Computational Biology, and on the scientific board of Calcul Québec and the Canadian Institute for Ecology & Evolution. He contributes to the artificial intelligence branch of the activities of the GEO BON secretariat. His research focuses on the spatio-temporal dynamics of species interactions and the applications of graph theory to a variety of ecological questions.
Students from Québec universities can get four credits for this class through CRÉPUQ (class code BIO6065). Please indicate if you want to claim these credits when applying. If you are registered to a non-Québécois university, you can be issued a letter of participation with the details of the course content to claim credits in your own institution.