Struggling with making your data FAIR?
Here's a practical guide (written in the language of a researcher)
FAIR stands for Findable, Accessible, Interoperable and Reusable. Implementing the FAIR principles for data can be a challenge though. In this post I want to dive into how to do it in practice. The FAIR principles are at the core of many current initiatives in research and beyond. For example the...
Nice read, indeed.
Yesterday, I was in a most peculiar discussion (see. https://fediscience.org/@rupdecat/110589043914216535 ). One of the things which came up, was the remark, that "these days no one uses archiving solutions of local institution 'cause of Zenodo and domain-specific solutions". Yep, that's right. But what about your 50 TB raw data? How do you store them?
I think it's time to acknowledge that sometimes it is better to archive all data together and have one single DOI.
Opionions?
@rupdecat I encourage looking at the data at hand and discussing with the team what the best solution is.
Sometimes the institution provides the best solution. Sometimes the community does. Sometimes you can just use general purpose platforms.
Yep, that's the point.
@rupdecat @HeidiSeibold or datasets in the petabytes, as are being created by #latticeFieldTheory simulations on the regular now...
I agree with @HeidiSeibold that if it is infeasible/impractical to make the data publicly accessible, it becomes imperative to publish metadata and a way to obtain the datasets (this means that the institution *have* to keep it on archive, but that's still cheaper in the long run than an online database)
Institutions often don't care about that, sadly.
@HeidiSeibold quick tip to make your toot more FAIR: add a caption as metadata to your image.
candidate: Visual breakdown of the acronym FAIR: Findable (a search box), Accessible (a “download” icon) Interoperable (two snapped-together puzzle pieces), and Reusable (a circular cycle, like the common icon for recycling consumer goods).
Thanks for the post!
Heidi Seibold
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