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Discover Ludwig"clean data" is a correct and usable phrase in written English.
It is often used when referring to data that has been checked, organized, and formatted to ensure accuracy and consistency. For example, "We need to ensure that all of the data is clean before we can begin to analyze it."
Exact(60)
"We can clean data as we go".
It took five years' study before scientists found how to extract clean data from it.
"But they are done within a controlled environment with clean data.
To me these imply that there is some kind of pure or clean data buried in a thin layer of non-clean data, and that one need only hose the dataset off to reveal the hard porcelain underneath the muck.
There was no good, clean data set that demonstrated definitively that it worked, so that's why we did this research".
These signals corresponded to "clean data".
Figure 3 Clean data capturing parameters.
uses reverberant and clean data to train the acoustic models.
First, you need to get a clean data set.
Clean data are prepared through running a Thomas' river water quality model and polluted data are synthesized by mixing clean data with white Gaussian noises.
Components that are not reproducible are projected out to obtain clean data.
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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