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It is well accepted in medical research, for example: when scientists test new medical treatments, they compare a new drug to an existing one or to a placebo by testing it on people who are randomly chosen and who do not know which drug they are getting.
Its robustness has been confirmed by testing it on synthetic data.
Its applicability can be further evaluated by testing it on a larger population of strains, from different clinical settings and geographical locations.
By testing it on simulated and in vivo data, we demonstrated its ability to accurately find the position of the part of these perturbations which is on the surface of the cortex.
The power of the new approach is demonstrated by testing it on several micro-world problems.
The performance of the proposed architecture is assessed by testing it on several benchmark problems.
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The merits, applicability and efficacy of the proposed framework is demonstrated by validating and testing it on data from a mid-size city in the United States and by benchmarking its interpretability and computational performance for a typical urban analytics scenario against current practice (i.e., a relational database).
In this way we followed established practice by developing the questionnaire clinically, then testing it on patients with known disorders, and finally validating it on a large clinical sample.
To objectively evaluate the accuracy of the modules detected by Qcut, we tested it on a large collection of synthetic gene expression data.
Tells how he isolated the bacteria, which had been discovered by Dr. J. Robin Warren, and tested it on himself.
Hibert iterates by sketching each piece, tests it on the computer to nail the colors and ratios, then either carbon copy draws or projects the design onto canvas.
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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