You are a PhD student starting a new project. Your supervisor asks for a short Data Management Plan (DMP) to clarify what data you’ll create, where it will live, and who’s responsible for each task.

Quick tips:

• Draft a one‑page DMP: what data, where stored, who can access, and how you’ll share/preserve.
• Assign roles (who collects, documents, reviews backups, deposits).
• Budget time/costs for storage and documentation.

Myth → A DMP is a long form to please funders.

Fact → A short, living plan saves time and avoids last‑minute crises.

Do this next:

• Horizon Europe – DMP template
• NCN – Guidelines for completing the Data Management Plan

You will interview participants. You prepare a clear consent process and check PWr/RODO rules early to avoid re‑consenting later.

Quick tips:

• Plan consent wording for future sharing (or justified restrictions).
• Choose durable, open formats where possible (e.g. use CSV/TSV (or ODS) instead of XLS/XLSX or PNG instead of JPEG).
• Record data quality steps (calibration, naming conventions, who can edit).

Myth → If I anonymize later, I don’t need to think about privacy now.

Fact → Early planning prevents data you cannot lawfully share.

Do this next:

• RODO/GDPR official text
• PWr – Data protection & IOD contact: iod@pwr.edu.pl 

After her first dataset, you write a short README explaining variables, units, file structure, and any codes. You add creator, date, and methods to the metadata.

Quick tips:

• Add a README/codebook; explain variables, units, and missing values.
• Use a discipline standard when available; include creator/date/methods.
• Keep a simple change log for versions.

Myth → Good data speak for themselves.

Fact → Without documentation, data are hard to understand – even for future you.

Do this next:

• CESSDA – Documentation & metadata checklist
• Data README guide

You keep working copies on approved storage, with automatic backups. Sensitive files have restricted access.

Quick tips:

• Follow the 3–2–1 rule: 3 copies, 2 media, 1 off‑site.
• Review access (especially for external collaborators).
• Test restore from backup before you need it.

Myth → Cloud = backup.

Fact → Sync ≠ backup; keep independent copies.

Do this next:

• ENISA backup/resilience guidance
• gov.pl: backups in a nutshell

At sharing, you deposit your dataset, get a DOI, choose a license, and set a short embargo to align with the article.

Quick tips:

• Prefer a disciplinary repository; otherwise use PWr collection in RepOD or a reputable generalist (Zenodo/Figshare).
• Choose a data license (CC BY/CC0) or document restrictions.
• Check funder/journal rules and retention requirements.

Myth → Open means giving up control.

Fact → Licensing sets the terms; you decide how others can use your data.

Do this next:

• RepOD – PWr collection
• DataCite – DOI basics
• Creative Commons – License Chooser

Other teams can find and cite your dataset because it has a DOI, clear license, and good metadata. You list your dataset on your CV and in reports.

Quick tips:

• Cite your data (creators, year, title, repository, version, DOI).
• Add dataset DOIs to your profiles (ORCID, CV, grant reports). 
• Track impact via repository metrics and citations.

Myth → Data sharing only helps others.

Fact → Shared, citable data increase your visibility and reduce duplicate effort.

Do this next:

• DataCite – How to cite data
• ORCID – Add works (via dataset DOIs)