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 > > On 27 Aug 2013, at 02:49, Emily Golden <10417...@student.uwa.edu.au> > wrote: > > > Hi All, > > > > I have collected diffraction images to 1 Angstrom resolution to the edge > of the detector and 0.9A to the corner. I collected two sets, one for > low resolution reflections and one for high resolution reflections. > > I get 100% completeness above 1A and 41% completeness in the 0.9A-0.95A > shell. > > > > However, my Rmerge in the highest shelll is not good, ~80%. > > > > The Rfree is 0.17 and Rwork is 0.16 but the maps look very good. If I > cut the data to 1 Angstrom the R factors improve but I feel the maps are > not as good and I'm not sure if I can justify cutting data. > > > > So my question is, should I cut the data to 1Angstrom or should I keep > the data I have? > > > > Also, taking geometric restraints off during refinement the Rfactors > improve marginally, am I justified in doing this at this resolution? > > > > Thank you, > > > > Emily > -- *Arka Chakraborty* *ibmb (Institut de Biologia Molecular de Barcelona)** **BARCELONA, SPAIN** *
