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A complete scientific project involves multiple aspects, including data, code, and paper reports. Organizing these elements effectively contributes to project management, reproducibility, backups, and traceability. Based on practical experience, here is a project organization method inspired by the work of Mario Krapp/semic-project and Joshua Cook. This method is suitable for the following types of projects: Core code written mainly in Python; Paper reports primarily written in LaTeX and Markdown; Version control using Git.

Some Principles Divided into schematic diagrams representing concepts and data charts representing results. Data Charts Prioritize using position to represent quantity, followed by length and area. Try to avoid pie charts (unless it’s essential to show proportions). Color is not the first choice (gray is very misleading; viridis is recommended). Use three-line tables. Schematic Diagrams Illustrations are better than engineering drawings or photographs. Image Formats Bitmaps are safer.