Location, scale, and shape parameters of the lognormal (with notation ambiguity) Main question:
If I read in a paper that a particular dataset's best fit is lognormal with $\mu=7.7$ and $\sigma=1.9$, what is the location, scale, and shape parameter of the lognormal? 
Side questions:
Is the scale parameter $e^\mu$ or $\sigma$? I see different statements in different reference literature.
Is $\mu$ given in the paper equal to the mean of the transformed normal or to some new transformed $\mu$?
 A: Naturally I can't speak authoritatively about any unreferenced papers you (don't) cite, but you appear to be encountering common conventions, apart from one mistake. 
You are being given location and scale on the back-transformed (logged) scale; things are simplest on that scale as the equivalent normal has 


*

*Mean, median and mode identical at $\mu$. 

*A statistically natural scale parameter, namely the standard deviation of logged values $\sigma$. 

*Simple shape properties, those of the normal. 
When the distribution is exponentiated to become lognormal, mean, median and mode now differ. $e^\mu$ is the median, so cannot be regarded as a scale parameter. But the mean and mode and variance all depend on both $\mu$ and $\sigma$. Skewness and kurtosis depend on $\sigma$, so to the best of my knowledge there is no reason to seek or to quote a shape parameter, shape being determined by $\sigma$. 
http://en.wikipedia.org/wiki/Lognormal is useful. (The entries on distributions in Wikipedia seem of higher quality than some of the other statistical content.) 
(The usual terminology of lognormal is entrenched, but it is, I suggest, backwards. A lognormal is an exponentiated normal. Perhaps "expnormal" sounded too ugly, but I guess "lognormal" sounded ugly when introduced. It is also true, naturally, that a lognormal is defined by the fact that if you take logarithms, you get a normal, but it seems more fundamental to define a distribution by how it is generated, not by how you should want to transform it.) 
