>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

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