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The Android platform enabled us to showcase our data using the localization features available in mobile platforms.
These findings encouraged us to investigate such a measure in our data using a metabolic endpoint.
We analysed our data using SPSS 14.0.
These generalized dimensions and D q spectra are evaluated for our data using (5).
We fitted a kernel probability function to our data using a Gaussian distribution, as seen in Figure 3.
We used a randomized block design with three replicates of each of the treatments and controls, and analyzed our data using multivariate repeated measures analysis of variance.
An additional study of our data using socio-technical networks as a construct to structure our data might have revealed additional insights in social debt.
The six different nucleotide substitution rates and the proportion of invariant sites were empirically estimated from our data using the software Garli with default settings.
Prior to analysis our approach uses CPCC to find the best distance metric that can be used to cluster our data using agglomerative hierarchical clustering.
We used clearly defined entry requirements, randomized participants, and analyzed our data using intention-to-treat.
We first validated our data using previously published nucleosome occupancy data.
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CEO of Professional Science Editing for Scientists @ prosciediting.com