Reusing other researcher's work is inherent in science. Licensing plays a central role for reuse, so we should know how it works.
In my workshops on open and reproducible research licensing often comes up as a topic of fear.
What am I allowed to do? Should I better let the legal department decide? How do I avoid going to jail?
Most of the time choosing a license is actually quite simple. So let me help you loose your fear of it! 🤗
Choosing a license for texts, data, media, ...
I really like the Creative Commons licenses for most research outputs (and other things I produce, like this newsletter).
They are easy to understand and to pick. On the CC-website you can find a simple tool to help you choose your license.
In research we want to almost always use the CC-BY or CC0 license.
Why not non-commercial?
CC-BY-NC is another license I often see used, but I do not like it in the context of research. Why? The "NC" for non-commerical prohibits commercial use for anyone but the owner. That means, for example, that educators (like me) cannot use it, if there are fees or commercial learning platforms involved. Even blogs and Wikipedia struggle with NC if there are commercial derivatives or components. Read more here.
Choosing a license for software
Software needs specific licenses. So for software there is another website you can use for choosing a license.
Again there is a license that works for most open projects: the MIT license.
That seems pretty simple, right? For more complex situations and when you have to worry about license compatibility of your code with software components that you use, please check out:
For other licenses, such as for hardware, choosealicense.com also has help for you:
I hope this short intro to licensing was helpful to you! Please note that I am not a legal expert but a self taught open science enthusiast.
In other news
Glad to be ending today's newsletter with true faith in the scientific community 🙌 (not in the institutions that would not support their own researchers 🤷♀️).
All the best,
P.S. If you're enjoying this newsletter, please consider recommending it to your friends 💟. This really helps me with this passion project! Just forward it or send them the sign-up link: heidiseibold.ck.page
All things open and reproducible data science.
I have volunteered for many initiatives and was always happy to do so. Recently I started to realize that being able to volunteer is a privileged position to be in. To be truly fair and allow all enthusiasts to join an initiative we have to rethink the way we do things. A constructive criticism. I've got to get this off my chest, you all! I've received yet another email asking whether I could speak at an event. For free. "Why for free?" you ask? It's an event from and for enthusiasts. I am a...
Fraud in science continues to be a highly discussed topic in the scientific community and also in mainstream media. I've always seen Open Science as a way to improve rigor in science, but can it also avoid fraud? I will not discuss cases of (alleged) fraud or scientific misconduct in this post, but focus on how I believe we can avoid it in the future. If you are interested in learning more about one of the major recent scandals, I recommend listening to the Freakonomics radio episode on the...
Code clubs are initiatives where people meet to improve their coding skills and help each other out. I am a big fan of them as I believe that they can help improving the quality of research. Because as we all know: Better Software, Better Research. I recently learned more about the code club at the Max Planck Institue of Psychiatry (MPIP) in Munich and would like to share how they organize it in the hopes that it might inspire you. Thanks to the organizers Vera Karlbauer and Jonas Hagenberg...