A few things that might be worth looking at:
1. How is your beam divergence varying as you fix mosaicity at different
levels? Does it look relatively stable at a realistic value for the
beamline? If I'm remembering correctly, mosaicity and beam divergence
are highly correlated within mosflm.
2. Is the mosaicity stable when not fixed? If not, this may be a sign
that you need to reduce the resolution limit for refinement (within
integration) - you didn't specify if your different resolution is from a
cutoff applied within mosflm or judging by statistics at a later step.
3. Regarding what Ian mentioned, merging the same observations twice
will cause issues with your statistics. It might be preferable to
combine the results after merging if your concerned about keeping as
many reflections as possible (the statistics from merging will still be
accurate for the respective processing run, but not reflective of the
combined amplitude/intensity set. However, the sigF/sigI values for
each reflection would be less skewed).
Pete
José Trincão wrote:
Hello all,
I have been trying to squeeze the most out of a bad data set (P1, anisotropic,
crystals not reproducible). I had very incomplete data due to high mosaicity
and lots of overlaps. The completeness was about 80% overall to ~3A. Yesterday
I noticed that I could process the data much better fixing the mosaicity to 0.5
in imosflm. I got about 95% complete up to 2.5A but with a multiplicity of 1.7.
I tried to integrate the same data fixing the mosaicity at different values
ranging from 0.2 to 0.6 and saw the trend in completeness, Rmerge and
multiplicity.
Now, is there any reason why I should not just merge all these together and
feed them to scala in order to increase multiplicity?
Am I missing something?
Thanks for any comments!
Jose
José Trincão, PhD CQFB@FCT-UNL
2829-516 Caparica, Portugal
"It's very hard to make predictions... especially about the future" - Niels Bohr
