Not sure if you ever got an answer to the original question (but I have had some mail mishabs today, so perhaps some messages are missing in my thread) Anyway: for sure NO - you cannot process the data with different parameter settings and then merge the different integrations as a way to gain increased multiplicity. The total oscillation angle determines your multiplicity, nothing else. Every time you reprocess the images it will still be the same data, only for most parts processed with an incorrect mosaicity - so what you gain is only an increased weight of systematic errors. Setting the mosaicity lower than it actually is will only force the program to think that no overlaps occur when they in fact spoil your data (integrated reflections containing also intensity of neighboring, overlap reflections, i.e. with systematic errors)
Poul On 28/01/2011, at 12.46, 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
