Computational Modeling Skills
Computational Modeling Skills is a collection of ten skills for scientific workflows in theoretical biophysics, from setting up a research project to developing models, checking published results, and preparing notebooks, documentation, and figures.
I made these skills to aid the scientist. If you are new to research, use them to learn the process while developing your own scientific judgment.
The aim is to automate repetitive work so researchers can focus on generating hypotheses and finding smart ways to test them. The point is not simply to increase productivity, but to make computational research reproducible, reviewable, tractable, and structured.
The full instructions, templates, and examples are available in the GitHub repository (opens in a new tab).
Included skills
Research Project Setup (opens in a new tab)
Set up and organize research project repository and automate standard Git workflow
Computational Modeling (opens in a new tab)
Develop biophysical models with hypothesis testing, validation and analysis
Model Documentation (opens in a new tab)
Write model documents explaining the physical assumptions, equations, and numerical methods
Scientific Notebook (opens in a new tab)
Create, maintain, execute, and review Jupyter notebooks for data analysis designed for model development
Linear Stability Analysis (opens in a new tab)
Linear stability of dynamical models in 1D or 2D continuum systems through analytical derivations, numerical calculations and plots
Data Visualization (opens in a new tab)
Publication-quality quantitative plots from scientific data including notebook and plot figures
Schematic Designer (opens in a new tab)
Design schematic figure in SVG
Paper Reproduce (opens in a new tab)
Reproduce research paper claims, methods, results and analysis for biophysical model
Let's Be Clear (opens in a new tab)
Clarify decisions, confirm the scope, and complete the agreed task
Slurm (opens in a new tab)
Run jobs in HPC using Slurm