This repository contains a sample research project from a fictional alpine soil study. It looks like a normal project folder — but it's hiding 13 research data management problems based on everything we covered in today's workshop.
Your job is to download find all the problems, and propose a solution.
Time: 20–30 minutes (individually or in pairs)
Step 1 — Get the files. Download and unpack the archive:
This can be direcly downloaded here, but it is often quickly done in your terminal with:
git clone https://github.com/chasenunez/RDM_BASICS.gitthen you can unpack the files by double clicking the file icon, or stay in the terminal and use:
tar -xzf sample_messy_project.tar.gz
cd sample_project/Step 2 — Explore. Open the files. Look at the folder structure. Open the spreadsheets. Read the scripts. Check the images. Look at what's there — and what's missing.
Step 3 — Hunt. Using the scorecard below, try to identify as many problems as you can. For each one you find, write down where you spotted it and how you'd fix it. Be specific — don't just say "bad naming," say which file and what's wrong with the name.
Step 4 — Compare. When time is up, we'll go through the answer key together.
Check off each problem as you find it. There are 13 in total.
| # | Category | Found it? | Where did you spot it? | How would you fix it? |
|---|---|---|---|---|
| 1 | Folder structure | ☐ | ||
| 2 | File naming | ☐ | ||
| 3 | Versioning | ☐ | ||
| 4 | File formats | ☐ | ||
| 5 | README / documentation | ☐ | ||
| 6 | Metadata | ☐ | ||
| 7 | Raw vs. processed data | ☐ | ||
| 8 | Data quality (inside files) | ☐ | ||
| 9 | Code quality | ☐ | ||
| 10 | Licensing | ☐ | ||
| 11 | Data availability statement | ☐ | ||
| 12 | Persistent identifiers | ☐ | ||
| 13 | Backup strategy | ☐ |
Scoring:
10–13 found → RDM expert. Your future self thanks you.
7–9 found → Solid instincts. A few blind spots to work on.
4–6 found → You've got the basics. Review the FAIR principles.
0–3 found → Time to re-watch the workshop slides!