hi folks

I’ve been away from data processing for a while, but am I alone in thinking 
that scaling to ~0.6 CC 1/2 cutoff might be ignoring a lot of useful data? I 
seem to remember that AutoProc and xia2.multiplex use a default of >= 0.3.

Harry

> On 7 Sep 2022, at 19:46, Matt McLeod <mjmcleo...@gmail.com> wrote:
> 
> Hi everyone,
> 
> I have a series of datasets at 253K (~2.0A), 273K (2.0A), 293K (2.0A), 313K 
> (2.2A) and I am curious as to the details in determining B-factors.
> 
> I have treated these datasets more-or-less identically for comparison's sake. 
>  I used DIALS to index, integrate, and scale the data.  I scaled the data to 
> a ~0.6 CC1/2 cutoff.  
> 
> After fully refining the datasets, there is an odd trend with respect to 
> temperature (from what has been previously published) and I assume that this 
> is because of "behind-the-scenes" computation rather than a biophysical 
> observation.  The B-factors slightly decrease from 252-293K, and then 
> significantly drop at 313K.  The maps look pretty well identical across the 
> datasets.
> 
> 253K - 53.8 A^2
> 273K - 48.4 A^2
> 293K - 45.5 A^2
> 313K - 18.6 A^2
> 
> I compared the wilson intensity plots from DIALS scaling for 273K and 313K 
> and they are very comparable.
> 
> I am looking for suggestions as to where to look at how these b-factors are 
> selected or how to validate that these B-factor are or are not accurate.  
> Also, any relevant literature would be welcomed.  From what I have read, 
> there is a general trend that as T increase, the atoms have more thermal 
> energy which raises the b-factors and this trend is universal when comparing 
> datasets from different temperatures.
> 
> Thank you and happy to supply more information if that is helpful,
> Matt
> 
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