Staring at a spreadsheet of results and unsure what to do next? Data analysis for biologists comes down to a few habits: keep raw data safe, tidy it, script every step and document it.
| Item | Value |
|---|---|
| Raw data | Keep faithfully, never overwrite with cleaned versions |
| Accidental edits | Use read-only permissions or spreadsheet protection |
| Data layout | Tidy data speeds up analysis |
| Backups | Store copies in more than one location |
| Reproducibility | An attainable minimum standard for judging claims |
These points come from the Carpentries lesson on good enough practices and a summary of the reproducible computational research rules. Guidance can change, so check the sources before you rely on the wording.
This guide is part of our wider set of lab skills resources, and it focuses on the computer side of the bench.
Why data analysis is now a core biology skill
Most experiments end as digital files. Plate reader exports, sequencing output, microscopy measurements and qPCR tables all need handling before anyone can draw a conclusion.
The Carpentries lesson puts it plainly: “In biology, as in other sciences: All your raw data are digital files”. How you manage those files affects whether your analysis can be checked and reproduced.
The skills below do not need a computer science degree. They are habits you can start on your next project.
Protect your raw data first
Every later step depends on the original files. If you overwrite them without a recoverable copy, you may lose the ability to reproduce the original analysis.
The lesson explains that “faithful retention is essential for re-running analyses from start to finish; for recovery from analytical mishaps; and for experimenting without fear.”
- Save the raw export exactly as it came off the instrument.
- Make a working copy for cleaning and analysis.
- Set the raw file to read-only, or use spreadsheet protection, so it is harder to damage it by accident.
- If you downloaded the data, record the exact procedure, plus any version number or the date of download.
The same lesson stresses backups in more than one location, so a single failed drive or lost laptop cannot erase a project.
Organise and tidy your data
A messy table can cause analysis errors. Merged cells, colour-coding as data and notes inside number columns all trip up software.
The Carpentries authors describe the key features of “tidy data”, which they say “can be a powerful accelerator for analysis”. In a tidy table, each variable is a column and each observation is a row.
- Use clear, consistent file and column names.
- Put one measurement per cell, with units in the column header.
- Record missing values the same way every time.
- Write a short README describing what each file contains.
Script your analysis so others can repeat it
Manual spreadsheet actions can be difficult to retrace. A script in R, Python or a shell records the steps implemented in its code.
The reproducible computational research rules describe reproducibility as “an attainable minimum standard for assessing the value of scientific claims”, because full replication on independently collected data is often not feasible. The same summary warns that a lack of reproducibility “can also be a burden for you as an individual researcher”.
In practice, that means a few simple moves:
- Avoid editing numbers by hand; do the cleaning in code.
- Keep the script next to the raw data and comments that explain each step.
- Re-run it from start to finish before you trust a figure.
- Use version control for code, which works best on plain text files.
A useful test of your setup is to imagine handing the folder to a new lab member. Could they find the raw files, run the script and get the same figures without asking you a single question? If not, the gaps you find are your next tasks.
Statistics and checking your results
Software will give you a p-value for almost anything. Your job is to choose a sensible design and test before you collect data, and to look at the data before you test it.
- Plot every dataset first, to spot outliers, batch effects and entry errors.
- Match the test to the data type and the number of groups.
- Report the sample size, the test used and the effect size, not just a p-value.
- Ask a statistician early, ideally while you plan the experiment.
Biological data also carry hidden structure. Replicates may be technical or biological, samples may share a batch, and measurements from the same animal or culture are not independent. Treating them as independent inflates confidence, so describe the structure of your design in plain words before you choose a test.
Clear reporting also helps when you write up. Our guide to scientific writing skills covers how to present methods and results so that readers can follow them.
A practical learning path
You do not have to learn everything at once. Build skills in the order that your current project demands, and let each small win carry into the next.
- Spreadsheet discipline: learn to lay out tables cleanly, with one header row and no merged cells.
- Plotting: make quick exploratory plots of every dataset before testing anything.
- One scripting language: pick R or Python, whichever your lab or field already uses, and stay with it long enough to become comfortable.
- Version control: track your scripts so you can see what changed and return to an earlier version.
- Workflow habits: organise folders, name files consistently and write a README for each project.
Pairing with a colleague who already scripts can help you learn. Ask to read their code, and offer to share yours.
Common mistakes to avoid
Poor workflow habits can cause analysis problems. Spotting them early can reduce rework.
- Overwriting the original file with a cleaned version, then losing track of what was changed.
- Fixing outliers by hand in a spreadsheet with no note of what was removed or why.
- Saving figures without the script that made them, so no one can regenerate them.
- Choosing a statistical test after seeing which one gives the answer you hoped for.
- Keeping the only copy of a dataset on one laptop.
Each of these is fixed by the habits earlier in this guide. A short checklist taped near your desk can be enough to keep them in view.
Documenting for regulated and team settings
In industry, analysis often sits inside a quality system where records must be traceable. The habits above, such as untouched raw data, scripted steps and documented decisions, carry over directly.
On a team, documentation also means other people can pick up your project when you are away. Write down where the raw data live, which script produces each figure, and which software versions you used.
If you are heading toward manufacturing or quality roles, our overview of gmp basics explains why that traceability matters.
Frequently asked questions
Do I need to learn programming to analyse biological data?
Show answer
Not at first, but scripting in R or Python makes your work repeatable and scales to large datasets. Start with small tasks such as cleaning one table.
Is Excel good enough?
Show answer
It is fine for quick looks and simple tables. Keep raw data untouched, avoid manual edits, and move to scripts when the analysis needs to be repeated.
What is tidy data?
Show answer
A layout where each variable is a column and each observation is a row. The Carpentries lesson calls it a powerful accelerator for analysis.
How do I make my analysis reproducible?
Show answer
Keep raw data unchanged, script every step, document your project, and confirm that you can re-run it from start to finish.
