This is another episode from our coverage of the Barcamp Open Science. In this episode Konrad talked with Ulrike Wuttke and Erzsébet Tóth-Czifra about their session “Concrete steps towards FAIR data as prerequisite for Open Scholarship in the Arts and Humanities”.
Episode Info: Duration 0:07:06 Recorded on 10-03-2020, Published on 05-07-2020
This work is licensed under a Creative Commons Attribution 4.0 International License
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Konrad Förstner |
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Matthias Fromm |
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A while ago, while being annoyed from poorly available / weakly documented data, I strolled around in the web, hoping for articles/videos that address this problem. I then stumbled across the term ‘frictionless data’, which I interestingly did not heard of before in the many discussions, articles, and podcasts about Open Science, Open Access, …
Maybe that’s the flesh that is needed on the bones of the ‘FAIR’ principles (espacially the ‘R’), so that ‘FAIR’ alone will not be ‘superficial’, as mentioned here in the interview.
https://frictionlessdata.io/guide/
That’s the new page for that topic, newly set up in march 2020. Via web-search you will very likely find older pages with outdated links, so better start with this new site.
BTW: The full meaning of the acronym FAIR was first mentioned at 4m25s. Even though I knew that term before, I think mentioning it at the beginning would be good. Not all people might know it, even though interested in Open Science. Especially, when only listening, not reading (and therefore not seeing that it is all uppercase letters, indicating it might be an acronym), it can be misleading. When I first heard the FAIR-term a while ago, I was confused; others might have the problem as well.
Thanks for the comments, Oliver. Fricionless data, coming from the OKFN world, is definitely something to follow. Sorry regarding the late introduction of the term FAIR. Classical “Expert Blind Spot” – the term is widely used in the scene but clearly needs a general introduction before diving into such specific topic.