Dear Gergo If you have high multiplicity I would recommend you ignore Rmerge and Rmeas and instead focus on Rpim which tells you the precision of the average data
http://strucbio.biologie.uni-konstanz.de/ccp4wiki/index.php/R-factors If this *increases* as you add more data then adding the data is making the average worse, if this decreases then you are improving the measurements. Simply removing perfectly good data to make reviewers happy seems like a bad idea to me. You mention below that the refinement gives you a good structure – this is a much better indication of the quality of the data than the Rmerge! There is a very good argument (certainly for pixel array detectors) for recording massive multiplicity low transmission data, since you can consider radiation damage a postori and work out where you should have stopped collecting after the experiment, but still have a complete data set (in essence, you are uniformly spreading the “useful photons” around reciprocal space) – this may require careful treatment in the processing however. Your CC-1/2 statistics indicate that the data in the outer shell agree well, so I would argue strongly for keeping the entire data set, and in my opinion you will be doing the community a favour arguing your case with the reviewers if they complain about the Rmerge being “too high” Best wishes Graeme From: CCP4 bulletin board [mailto:[email protected]] On Behalf Of Gergo Gógl Sent: 20 July 2016 16:07 To: ccp4bb Subject: [ccp4bb] weak low resolution data with high R and good CC1/2 Dear all, I am trying to process a weak low resolution data which was crystallized and collected in an other lab but unfortunately with suboptimal crystal handling (cryo...) and data collection strategy (1° oscillation, close detector distance...). The data is highly redundant but the Rmeas is really bad. We already suggested them to collect better data from a better crystal but it seems to be difficult for them... The overall data has a redundancy of 40 (43 in the highest bin) with an overall Rmerge 75% (365% in the highest bin) while the overall CC1/2 is 99% (83% in the highest bin). (The XSCALE.LP is attached for the whole dataset.) I was able to decrease the overall Rmerge to 36% by discarding ~80% of the collected frames but it is still a marginal data (with a redundancy ~9). On the other hand the refinement gave us a reasonable structure with good Rfactors (Rwork 22% Rfree 26%). (It is a protein-peptide complex where we are interested in the bound state of the peptide.) We are in a disagreement in our lab and already asked a few crystallographer but did no reached a clear consensus answer. Is this data acceptable to publication? Can you trust a data like this? Best, Gergo Gogl [https://ssl.gstatic.com/ui/v1/icons/mail/images/cleardot.gif] -- This e-mail and any attachments may contain confidential, copyright and or privileged material, and are for the use of the intended addressee only. If you are not the intended addressee or an authorised recipient of the addressee please notify us of receipt by returning the e-mail and do not use, copy, retain, distribute or disclose the information in or attached to the e-mail. Any opinions expressed within this e-mail are those of the individual and not necessarily of Diamond Light Source Ltd. Diamond Light Source Ltd. cannot guarantee that this e-mail or any attachments are free from viruses and we cannot accept liability for any damage which you may sustain as a result of software viruses which may be transmitted in or with the message. Diamond Light Source Limited (company no. 4375679). Registered in England and Wales with its registered office at Diamond House, Harwell Science and Innovation Campus, Didcot, Oxfordshire, OX11 0DE, United Kingdom
