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In the meantime, to ensure continued support, we are displaying the site without styles and Java Script.Manuscripts may have a rigidly defined structure, but there’s still room to tell a compelling story — one that clearly communicates the science and is a pleasure to read.Examples such as this are not uncommon: “Though not inclusive, this paper provides a useful review of the well-known methods of physical oceanography using as examples various research that illustrates the methodological challenges that give rise to successful solutions to the difficulties inherent in oceanographic research.” Why not this instead: “We review methods of oceanographic research with examples that reveal specific challenges and solutions”?
The most important information should be in the main text.
To avoid distraction, writers should put additional data in the supplementary material.
Always think of your busy, tired reader when you write your paper — and try to deliver a paper that you would enjoy reading yourself. Author Helen Sword coined the phrase ‘zombie nouns’ to describe terms such as ‘implementation’ or ‘application’ that suck the lifeblood out of active verbs. Once the paper has a clear message, I suggest that writers try some vivid language to help to tell the story.
Why does scientific writing have to be stodgy, dry and abstract? If we don’t engage that aspect of ourselves, it’s hard to absorb the meaning of what we’re reading. We should engage readers’ emotions and avoid formal, impersonal language. For example, I got some pushback on the title of one of my recent papers: ‘Eight habitats, 38 threats, and 55 experts: Assessing ecological risk in a multi-use marine region’. There’s probably less resistance out there than people might think.
Everything in the paper should logically and structurally support that idea.
It can be a delight to creatively bend the rules, but you need to know them first.
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There is a narrow line between speculation and evidence-based conclusions.
A writer can speculate in the discussion — but not too much. 2017) that lays out structural details for using a context–content–conclusion scheme to build a core concept.