How do I determine the best statistical way for data transformation for standardization (like log, sq root) to remove bias between different datasets?
Its unclear what the OP is asking (so this response is somewhat general), but the table below illustrates common contexts and the transformations that are typical:

sales, revenue, income, price --> log(x)

distance --> 1/x, 1/x^2, log(x)

market share, preference share --> (e^x)/(1+e^x)

right-tailed dist --> sqrt(x), log(x) caution log(x&lt;=0)

left-tailed dist --> x^2

You can also use John Tukey's three-point method as discussed in this post.  When specific transformations don't work, use Box-Cox transformation. Use package car to lambda &lt;- coef(powerTransform()) to compute lambda and then call bcPower() to transform. Consider Box-Cox transformations on all variables with skewed distributions before computing correlations or creating scatterplots.