Daniel-Mietchen
commented
6 years ago Literacy concepts as covered in Wikipedia include a thin outline of data literacy: https://en.wikipedia.org/wiki/Template:Literacy https://en.wikipedia.org/wiki/Data_literacy
The basic idea is that
I'd like to explore in a bit more detail what this means for data literacy. On the one hand, each of these dimensions provides more opportunities for interaction between human users and data resources, which should increase their data literacy. On the other hand, FAIRness increases the ability of machines to use data and to hide some of their complexity from human user
This latter scenario is similar to a car's hood hiding much of its engine's technical details. As long as things go fine, this means that fewer car users would take a look under the hood, which would imply that car literacy amongst car users goes down over time. Yet again, such hiding of the technical complexities might actually lure people into using a car (or whatever other technical infrastructure) who would otherwise not even consider using it, thereby increasing the proportion of car users within a population and thereby car literacy.
Things can easily get more complex if we consider cases where users are well-informed about cars and because they are, decide not to use them (e.g. because of financial, environmental and/ or health impacts). In data contexts, similar scenarios would include website operators deciding not to collect user information, even though they could.
Another complication is that — whatever the timeline we're looking at in terms of literacy trends — there are additional variables to take into account: the shear volume of data available (and the other three Vs of big data — variety, velocity, veracity), as well as their significance for society or subsets thereof, which can have significant effects — through things like markets, marketing, press coverage, events etc. — on data literacy, or access to data-literate instructors.
There is likely much to learn in this regard from studies of classical literacy, numerical literacy, computer literacy, car literacy and related concepts, but I have not looked at these in much detail yet.
Finally, what about evolutionary aspects of data literacy? Social insects or microbes, for instance, have been compared to machines, so some version of FAIRness may be relevant in such contexts, whereas the squirrel brain has evolved to cope with the demand for memorizing access protocols to things like acorns, and animals engaging in tool use (or even reuse) or vocal learning are always good to keep in mind in cognitive contexts, for instance for cases like missing tutors during the sensitive period in songbirds.