Add FAIR training material#253
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Finally finished the remaining modules! Now ready for review. |
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| ## Introduction | ||
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| This module aims to give you an overview of the FAIR principles for scientific |
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I wonder if - as this is the first time FAIR is mentioned, and it may be new to a reader - whether the acronym's meaning should be stated up front, e.g. "FAIR (Findable, Accessible, Interoperable, Reusable)".
Alternatively, it could be moved into the title, e.g. rather than "Understanding FAIR" it could be "Making Research FAIR: Findable, Accessible, Interoperable, and Reusable"
I am hesitant with these suggestions as it makes the paragraph (or title) more stodgy, so it's worth canvassing the team for ideas.
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I see what you mean, but I think the term FAIR is fairly (pun intended) common now in literature, and even the original publication talking about FAIR doesn't spell it out in their title and abstract, so I think I will prefer to be more concise here.
| Free text could vary among researchers while controlled vocabularies use | ||
| standardised terms with predefined meanings, which allow computational agents | ||
| to process and integrate data from different sources. | ||
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At this point, it was not completely clear to me what a controlled vocabulary is in practice so I did some googling, and the top results led to quite wordy definitions, so it would be great to succinctly define here. I wonder if it would be useful to have a brief example data after each of F, A, I and R to help make concrete, or would that add too much noise?
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I try to avoid terse definitions if possible, so the explanation of what 'controlled vocabularies' are (free vs controlled) will hopefully be more useful than any definition here.
| allows future researchers to assess whether the data is fit for their specific | ||
| purpose and evaluate its quality. | ||
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Again here, I feel the ideas coming together but it's not feeling concrete and I wonder if an example would help
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| Extensions to the FAIR principles may exist for some of the above research | ||
| outputs, for instance, [FAIR4RS](https://doi.org/10.1038/s41597-022-01710-x) is | ||
| an extension to the FAIR principles for research software. Websites such as |
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I think it could be worth clarifying what to do with non-data digital assets, e.g. for one of the above specify how one could make it FAIR. The FAIR4RS link is a lot to read so having some sort of light intro or summary here would be helpful.
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I want to lightly touch on the point that there may be extensions to FAIR for different research output, so prefer to just highlight their existence so interested readers can follow up.
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| ::::challenge{id=data-repo-q1 title="Data repo Q1"} | ||
| Go to [re3data](https://www.re3data.org) and | ||
| [FAIRsharing](https://fairsharing.org/search?fairsharingRegistry=Database) to |
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This link appears to be broken
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The whole fairsharing.org seems inaccessible at the moment, hopefully will come back soon.
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| - [Digital Curation Centre](https://www.dcc.ac.uk/guidance/standards/metadata) | ||
| - [RDA Metadata Standards Catalog](https://rdamsc.bath.ac.uk) | ||
| - [FAIRsharing](https://fairsharing.org/search?fairsharingRegistry=Standard) |
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This is great Tim, I've left some notes. I completely forgot about having given previous feedback last year! I would suggest running this by others in OxRSE as I have not had direct experience with research data myself; I'm good as a set of fresh eyes on this but would be more valuable to get feedback from a battle-hardened researcher. As noted, I wonder if an example running throughout these pages would help make this feel more concrete, but that may end up adding more noise. Would be good to discuss with the team. |
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@alexallmont Thanks taking the time to go through this! I struggled to come up with an example throughout all the modules (and potentially materials for practical sessions) that can illustrate FAIR beautifully, as examples of FAIR are highly dependent on the research domain and its context (or scenario!) and I don't want to make up a toy research project that is universal but of no practical value. The current approach is to lay out the principles/ideas and give pointers for researchers to follow up. |
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This PR adds training material about the FAIR guiding principles for scientific data. The plan is to have the following five modules:
Any comment is welcomed, including the high-level structure of the materials, structure of individual module, things that should be discussed, style etc. Ticks above indicate the module is ready for review.