Julia

Documentation serves multiple purposes and may be useful for various audiences, including your future self, collaborators, users and contributors - should you aim at packaging some of your code into a general-purpose library.

Your future self and others should be able to recreate the minimal environment to run the scripts in your research project. This is best achieved using package managers and virtual environments.

In this post, we’ll explore various methods for structuring a research project folder that includes code, data, publications, and more. Additionally, we’ll look into the specifics of organizing your code folder.

Code testing is essential to identify and fix potential issues, to maintain sanity over the course of the development of the project and quickly identify bugs, and to ensure the reliability and sanity of your experiment overtime.

In this post, I explore the benefits and drawbacks of using empirical (ML)-based models versus mechanistic models for predicting ecosystem responses to perturbations, and further develeop a hybrid approach combining their strengths.

In this tutorial, you’ll learn the basics of approximate Bayesian computation (ABC). ABC is an inference method with very little requirements in terms of the model structure - yet it can be very powerful. It is very simple to apply to any model, and to understand. We’ll play around with Julia, and we will visualize graphically the inference results, so that you can build an intuition of the inference method.