There are a few very good online guides on reproducible research.
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The Turing way from the Alan Turing institute. This page contains information and guidelines about most of the things you need to know about reproducible research and we encourage everyone to skim through this site. They also have a template repo that might be relevant.
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Scientific Python is a guide on how to develop scientific software in Python. This guide is a bit more technical than the Turing way and is more focused on the software development part.
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CodeRefinery is a project that aims to teach researchers how to write better code. They have a lot of good resources and workshops that you can attend.
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Sigma2 training is a Norwegian project that provides training in high performance computing and data management. They have links to a lot of good resources.
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Papers with code which is probably more targeted machine learning projects. The also have a pretty comprehensive guide at https://github.com/paperswithcode/releasing-research-code.
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Reproducible Machine Learning Workflows for Scientists with Pixi is practical introduction to using Pixi to easily create scientific and AI/ML environments
Getting inspiration from other project is often a good way. Papers with code also has a web page where you can search for papers with code. For example you, could try to search for FEniCS and you will get a list of projects where FEniCS is mentioned.