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There are enough free tools out there to ensure that visualising and analysing the data is simple and easy.
The model illustrated here is simple because the data is simple.
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While this method of pooling the data is simpler and has greater statistical power than the one based on summing the x(M) statistics, the latter method has the advantage of being capable of providing a p-value associated with each scan.
Operations on the data are simple and limited: there are put, get and delete commands [10, 60].
The predictions agree well with both measured pressures and estimations using Eaton's empirical equation, but the application of the new equation to seismic data is simple and convenient.
We decided to evaluate this technique because it allows one to better explore the nature of the numerical data, is simpler and more flexible than classical regression modeling because it did not require testing preconceived mathematical formulae, and also because it may yield estimates of individual tree carbon as accurate as those obtained from classical allometry.
The result turned out to be bad because the input data are simple sperm images and the SIFT algorithm cannot extract enough feature descriptors for classification.
Tracking, managing, and organizing the incoming data was simple and was ideal for the short project timeframe.
The data is beautifully simple, Dr. Famiglietti said.
The first idea to give a pictorial representation of the data is very simple.
However, with some other data types, finding mismatches between the gene structure and the data is often not simple.
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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