Thank you Phil, for clarification of my point, but it appears as cheating in a current situation, when an author has to fit a three dimensional statistics into a one-dimentional table. Moreover, many of journal reviewers may never worked with the low-resolution data and understand importance of every A^3 counts. It is not clear to me how to report the resolution of data when it is 3A in one direction, 3.5A in another and 5A in the third.
On Apr 9, 2012, at 4:51 AM, Phil Evans wrote:
> On 8 Apr 2012, at 21:18, aaleshin wrote:
>> What I suggested with respect to the PDB data validation was adding some additional information that would allow to independently validate such parameters as the resolution and data quality (catching of model fabrications would be a byproduct of this process). Does the current system allow to overestimate those parameters? I believe so (but I might be wrong, correct me!). Periodically, people ask at ccp4bb how to determine the resolution of their data, but some "idiots" may decide to do it on their own and add 30% of noise to their structural factors. As James mentioned, one does not need to be extremely smart to do so, moreover, such an "idiot" would have less restraints than an educated crystallographer, because the "idiot" believes that nobody would notice his cheating. His moral principles are not corrupted, because he thinks that the model is correct and no harm is done. But the harm is still there, because people are forced to believe the model more than it deserves.
>> The question is still open to me about what percentage of PDB structures overestimates data quality in terms of resolution. Is it possible to make it less dependent on the opinion of persons submitting the data? We all have so different opinions about everything...
>> Alex Aleshin
> Using the weak high resolution data in a structure determination is not cheating. We should use data out to the point where there is no more significant and as long as it helps the structure determination and refinement, provided that we are using appropriate statistical treatment of the errors. We have become addicted to the idea that "resolution" is a single indicator of quality, and that is a gross over-simplification. Resolution tells us how many data were used, not their quality nor the quality of the model.