Alex, I think you are mixing two things here: presenting statistics that characterizes the data and its interpretation.
Looking at data completeness as a single number tells something but not a lot, while looking at these metrics per resolution reveals a whole lot more information (for example, distribution of missing data in reciprocal space may tell you why your maps look funny). Pavel On Mon, Apr 9, 2012 at 11:11 AM, aaleshin <aales...@burnham.org> wrote: > Hi Pavel, > Reporting the table that you suggested would create more red flags for the > reviewers and readers than explaining how to understand the resolution of > my data. We need more studies into this issue (correlation between the > resolution of anisotropic data and model quality). And there should be a > common rule how to report and interpret such data (IMHO). > > Regards, > Alex > > On Apr 9, 2012, at 11:02 AM, Pavel Afonine wrote: > > Hi Alex, > > It is not clear to me how to report the resolution of data when it is 3A >> in one direction, 3.5A in another and 5A in the third. >> > > can't be easier I guess: just switch from characterizing data sets with > one single number (which is suboptimal, at least, as Phil pointed out > earlier) and show statistics by resolution instead. For example, R-factors, > data completeness, <Fobs> shown in resolution bins are obviously much more > informative metrics then a single number. > > If you want to be even more sophisticated, you can. See for example: > > A program to analyze the distributions of unmeasured reflections > J. Appl. Cryst. (2011). 44, 865-872 > L. Urzhumtseva and A. Urzhumtsev > > Pavel > > >
