A small project. A result someone else can reproduce.
Before adding another analysis, make one result understandable from its input to its final figure.
Start with an output, not a tool
A first neuroscience project becomes easier to finish when its destination is concrete. Choose one figure, one comparison or one short demonstration that a second person can inspect. ‘Explore this dataset’ leaves too many decisions open. ‘Recreate this example plot and explain each processing step’ gives the work a boundary. The exercise can be valuable even when it produces no new scientific finding.
Write the question in a few sentences before opening a notebook. Name the input, describe the expected output, and say what would count as a completed exercise. Keep a separate list of attractive ideas that are outside the scope. That list protects the small project from growing into an unfinished research programme.
Make the route visible
Keep the original input separate from files produced during analysis. Give derived files names that explain their purpose, and record the script or notebook that creates each one. A future reader should not need to guess whether a spreadsheet was entered by hand, downloaded, cleaned or exported from another program.
For an educational example, synthetic data can remove an unnecessary access hurdle. Label it explicitly as synthetic. If a real dataset is essential, record its version, source and access conditions. A successful download is not, by itself, permission to republish the files.
Write for a fresh pair of hands
A useful README answers four questions: what is this project, what does it need, how is it run, and what should appear at the end? Write the instructions as if the reader has none of your local folders. Replace absolute paths with a clear project structure. Record relevant software versions and the time or hardware the example needs.
Keep the instructions alongside the code. If a step requires a manual choice, name that choice instead of hiding it behind ‘preprocess as usual’. Record random seeds where relevant, while recognising that a seed alone does not make every computation identical across environments.
Try the handover before calling it finished
Ask a collaborator to follow the instructions without your verbal help. Watch where they stop. A missing file, ambiguous label or unexplained parameter is useful feedback, not a failure of the reader. Fix those points while the project is still small.
Finally, compare the output with what the instructions promised. Record any expected differences, and explain what the result does not demonstrate. Reproducing a pipeline is evidence that the steps can be repeated; it is not proof that the study design or interpretation is sound. A small, honest handover is a stronger learning artifact than an impressive-looking result nobody can trace.