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.

This blog post discusses the use of PiecewiseInference.jl, a Julia package that enables the use of machine learning to fit complex ecological models on ecological dataset.

One of the challenges modellers face in biological sciences is to calibrate models in order to match as closely as possible observations and gain predictive power. Scientific machine learning addresses this problem by applying optimisation techniques originally developed within the field of machine learning to mechanistic models, allowing to infer parameters directly from observation data.