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The data flood, of course, comes from both tame corporate databases and untamed — "unstructured," in industry jargon — data from Web pages, blogs, social networking messages and elsewhere.
In the DTN jargon, data packets are referred to as bundles [1], since it is often assumed that an overlay layer, called bundle layer, is present above the existing protocol stack for supporting interoperability.
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The real trick is not to use scientific jargon or data that people can't comprehend.
Facebook has talked repeatedly about their "social graph" (computer science jargon for the data structure behind a social network).
But once Gerry whips the smartphone out of his pocket, he starts rattling off tech jargon and analyzing data points across his farm like a 20-something whiz kid from Silicon Valley.
Parents face a test of their own: making sense of a new flood of data, jargon and acronyms.
A writer's job is to turn jargon and scrambled bits of data into information and readable prose.
Mr. Lukacs wisely avoids technical data, wine industry jargon and the tasting notes that pass for so much wine writing.
But for many people here, what seemed most important today was not the scientific data, or engineering jargon, or calls for calm from public officials that invariably accompany any anomalous event at a nuclear power plant.
With the focus on more interdisciplinary collaboration, clear presentations are also essential, because people won't be able to sift through jargon or a mountain of data to understand the importance of your work and how it might benefit them.
However, behind the jargon is a hard fact that data is important.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com