Hence we make such use of metaphors and analogies when it comes to describing complex concepts. Tying a concept (for example, quantum superposition) to a real world “thing” (for example, a cat in a box ) allows people unfamiliar with the original concept to connect it with something they have experience of, and provides a foundation which can be elaborated on. If, upon further examination, it is found that the analogy gets stretched beyond all reason, then that is acceptable, as long as those using it don’t simply rely on it as an article of blind faith. Analogies and metaphors require critical thinking.
Scientific concepts are formulated in human language, and as such, are intended to be processed by the human brain (even if that brain needs to be highly trained before it can properly grasp the concepts being described). Scientific data, on the other hand, is designed to be machine consumable (as well as predominantly machine produced). Measurements are often not useful without the context surrounding them. It is one thing to know that a particular river level rose by 10cm. It is only by knowing where this happened, how high the river was to begin with, and how high the rise would have to be at that location to flood the houses built there, that we are able to put the data into context, and make it useful.
Yet we still need that data. If a homeowner who got flooded wished to claim on their insurance for flood repairs, having that data and context available means they’d have proof that it was river flooding that caused the damage, rather than a burst pipe.
We also need to have the research data which underpins key research findings available and understandable, both for reproducibility and to prevent fraud/misuse. Making data usable by others takes effort and time and is often unrewarded by the current system for gaining academic credit.
Metaphors and Analogies
“No one metaphor satisfies enough key data system attributes and that multiple metaphors need to co-exist in support of a healthy data ecosystem”(Parsons & Fox, 2013)
Data publication as a metaphor has been addressed extensively in (Parsons & Fox, 2013), leading to the quote above. But before we dive into examples of metaphor and analogy in the data domain, it is helpful to review what they mean.
From (Gentner & Jeziorski, 1993):
‘Analogy can he viewed as a kind of highly selective similarity. In processing analogy, people implicitly focus on certain kinds of commonalities and ignore others. Imagine a bright student reading the analogy “a cell is
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