Attribution
How to properly credit the original data owner.
- Permissions: What you are allowed to modify, transform, or redistribute.
- License Inheritance (ShareAlike): When derivative datasets must carry the exact same terms as the original.
Just like any other form of intellectual property, research data requires a license to define how individuals and automated tools can legally access, modify, and reuse it.
Just like any other form of intellectual property, research data requires a license to define how individuals and automated tools can legally access, modify, and reuse it. This lesson equips participants with a practical understanding of data licensing frameworks, enabling them to confidently select an appropriate license for their own datasets or communicate effectively with Data Stewards and information professionals.
Grounded in the open research principles established by The Turing Way community, this lesson focuses on three fundamental dimensions of data reuse:
##
4 activities · 80 min total · grouped by phase
How to properly credit the original data owner.
The scenarios will come back at the end of the course in a more in-depth follow-up discussion:
Below are three real-world research scenarios. For each scenario, analyze the researcher’s goals, evaluate the legal constraints, and recommend the best specific license (e.g., CC0, CC BY 4.0, CC BY-NC, ODbL, PDDL). Be prepared to justify your choice to the class in the follow-up discussion at the end.
Researchers, Data Stewards, Postgraduate Students
Instructor Goal:
Walk participants through data licensing choices without drowning them in legal jargon. Focus on practical research impacts.
What to say / emphasize to your class:
“Before we dive into the licenses themselves, we need to clear up a massive myth in open research. Many researchers think that if they upload a dataset to Zenodo, Figshare, or an institutional repository, it’s automatically ‘open’ for anyone to use. **That is legally incorrect.**In most legal jurisdictions, if you post a dataset online without an explicit license attached, the default legal status is ‘All Rights Reserved.’ That means even if your intention was to share it openly, another scientist cannot legally copy, modify, combine, or build upon your dataset without asking your written permission first.A Data License is simply a standardized contract or waiver that tells the world exactly what they can and cannot do with your dataset—saving everyone time and legal headaches.”
Instructor Note:
Start with the permission building blocks before showing full licenses. It keeps students from feeling overwhelmed by acronyms.
Explain that CC licenses act like building blocks using two-letter tags:
Class Example to Use:“If you see CC BY-ND, what does that mean? It means a user must credit you (BY), and they can share exact copies, but they cannot adapt or modify your dataset (ND).”
Emphasize these two primary options for research outputs:
Instructor Note:
Spend extra time here! Researchers often instinctively choose NC or ND without realizing how destructive they are to open science.
Why warn against CC-NC (Non-Commercial)?“Researchers choose NC because they don’t want ‘companies profiting off their work.’ But in practice, ‘non-commercial’ is legally fuzzy. It blocks public-private research partnerships, hinders AI/Machine Learning models trained by mixed teams, and stops non-profit startups or commercial spin-offs from translating research into real-world applications.”Why warn against CC-ND (No-Derivatives)?“ND is a death sentence for data integration pipelines. Cleaning data, changing file formats, translating column headers, or merging two tables technically creates a ‘derivative work.’ An ND license strictly forbids this, making the data useless for automated data pipelines.”
Instructor Transition:
“Now, why do we need Open Data Commons if we already have Creative Commons?”
What to explain to your class:
“Creative Commons was built for traditional copyrighted works like text, images, and music. However, databases have a unique legal structure—especially in places like Europe, which has a specific ‘Database Right’ separate from copyright.Open Data Commons (ODC) licenses were written specifically for databases, separating the structure of the database from the data points inside it.”
Point your participants to the Open Data Commons documentation and cover their 3 core licenses:
Closing advice for the instructor to share:
“Wrap up by telling your participants to always check local and funder mandates before picking a license. Many public funding bodies (like Horizon Europe or NIH) or institutional repositories have default licensing requirements.For example, some government agencies require an Open Government Licence (OGL), and many universities now mandate CC0 for all research metadata.”
Reconvene the class and have groups share their answers for Scenario 3. Emphasize that as future researchers and Data Stewards, their role is to remove legal barriers to data reuse while protecting academic integrity through proper citation.
l'Hénaff P, Vieira B, Vivas-Romero M. Copyright and Licensing . Version 1.0. FAIR Lesson Plan Handbook [Internet]. 2026. Available from: https://doi.org/10.5072/zenodo.627811