>Based on the simulations I've done the data should be "cut" at CC1/2 = 0. >Seriously. Problem is figuring out where it hits zero.
But the real objective is – where do data stop making an improvement to the model. The categorical statement that all data is good is simply not true in practice. It is probably specific to each data set & refinement, and as long as we do not always run paired refinement ala KD or similar in order to find out where that point is, the yearning for a simple number will not stop (although I believe automation will make the KD approach or similar eventually routine). >As for the "resolution of the structure" I'd say call that where |Fo-Fc| >(error in the map) becomes comparable to Sigma(Fo). This is I/Sigma = 2.5 if >Rcryst is 20%. That is: |Fo-Fc| / Fo = 0.2, which implies |Io-Ic|/Io = 0.4 or >Io/|Io-Ic| = Io/sigma(Io) = 2.5. Makes sense to me... As long as it is understood that this ‘model resolution value’ derived via your argument from I/sigI is not the same as a <I/sigI> data cutoff (and that Rcryst and Rmerge have nothing in common)…. -James Holton MAD Scientist Best, BR On Aug 27, 2013, at 5:29 PM, Jim Pflugrath < <mailto:jim.pflugr...@rigaku.com> jim.pflugr...@rigaku.com> wrote: I have to ask flamingly: So what about CC1/2 and CC*? Did we not replace an arbitrary resolution cut-off based on a value of Rmerge with an arbitrary resolution cut-off based on a value of Rmeas already? And now we are going to replace that with an arbitrary resolution cut-off based on a value of CC* or is it CC1/2? I am asked often: What value of CC1/2 should I cut my resolution at? What should I tell my students? I've got a course coming up and I am sure they will ask me again. Jim _____ From: CCP4 bulletin board [CCP4BB@JISCMAIL.AC.UK] on behalf of Arka Chakraborty [arko.chakrabort...@gmail.com] Sent: Tuesday, August 27, 2013 7:45 AM To: CCP4BB@JISCMAIL.AC.UK Subject: Re: [ccp4bb] Resolution, R factors and data quality Hi all, does this not again bring up the still prevailing adherence to R factors and not a shift to correlation coefficients ( CC1/2 and CC*) ? (as Dr. Phil Evans has indicated).? The way we look at data quality ( by "we" I mean the end users ) needs to be altered, I guess. best, Arka Chakraborty On Tue, Aug 27, 2013 at 9:50 AM, Phil Evans <p...@mrc-lmb.cam.ac.uk> wrote: The question you should ask yourself is "why would omitting data improve my model?" Phil
