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at each iteration until all data points are within this range.
All data points are weighted equally and NBP (normal boiling point) is used as a fixed point since the NBP is important for refrigeration applications.
The lc_sigma_clip routine provided by the lightcurves.py script performs an iterative sigma-clipping algorithm, removing those points that fall outside +/-3 sigma from the mean at each iteration until all data points are within +/-3 sigma (the number of sigma used to clip the data defaults to 3 but can be changed).
All data points are the mean value of twenty samples with a standard deviation of <0.5.
Experiments were performed in triplicate, and all data points are shown.
Here, all data points are assumed to be independent and identically distributed.
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All data points were fitted with the general three-level model (dotted).
All data points were fitted following the general three-level model for the fluorescence CRM=AIe/(1+Ie/Is), with A (=0.82) the pre-factor, Is (=6.5 μW) the saturation intensity and Ie the excitation intensity37.
All data points were retained for non-parametric linear regression analysis.
Overall, the error rate for all data points was maintained at less than 0.25%%.
The weights of all data points were set equal after flattening square values of each data point.
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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