↓ Skip to main content

Whole-pattern fitting technique in serial femtosecond nanocrystallography

Overview of attention for article published in IUCrJ, February 2016
Altmetric Badge

About this Attention Score

  • Average Attention Score compared to outputs of the same age
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
4 X users

Citations

dimensions_citation
4 Dimensions

Readers on

mendeley
21 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Whole-pattern fitting technique in serial femtosecond nanocrystallography
Published in
IUCrJ, February 2016
DOI 10.1107/s2052252516001238
Pubmed ID
Authors

R.A. Dilanian, S.R. Williams, A.V. Martin, V.A. Stretsov, H.M. Quiney

Abstract

Serial femtosecond X-ray crystallography (SFX) has created new opportunities in the field of structural analysis of protein nanocrystals. The intensity and timescale characteristics of the X-ray free-electron laser sources used in SFX experiments necessitate the analysis of a large collection of individual crystals of variable shape and quality to ultimately solve a single, average crystal structure. Ensembles of crystals are commonly encountered in powder diffraction, but serial crystallography is different because each crystal is measured individually and can be oriented via indexing and merged into a three-dimensional data set, as is done for conventional crystallography data. In this way, serial femtosecond crystallography data lie in between conventional crystallography data and powder diffraction data, sharing features of both. The extremely small sizes of nanocrystals, as well as the possible imperfections of their crystallite structure, significantly affect the diffraction pattern and raise the question of how best to extract accurate structure-factor moduli from serial crystallography data. Here it is demonstrated that whole-pattern fitting techniques established for one-dimensional powder diffraction analysis can be feasibly extended to higher dimensions for the analysis of merged SFX diffraction data. It is shown that for very small crystals, whole-pattern fitting methods are more accurate than Monte Carlo integration methods that are currently used.

X Demographics

X Demographics

The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 21 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Canada 1 5%
Unknown 20 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 24%
Student > Ph. D. Student 5 24%
Student > Doctoral Student 2 10%
Professor > Associate Professor 2 10%
Student > Master 2 10%
Other 1 5%
Unknown 4 19%
Readers by discipline Count As %
Physics and Astronomy 7 33%
Chemistry 4 19%
Biochemistry, Genetics and Molecular Biology 2 10%
Business, Management and Accounting 1 5%
Medicine and Dentistry 1 5%
Other 3 14%
Unknown 3 14%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 12 June 2017.
All research outputs
#13,966,264
of 22,846,662 outputs
Outputs from IUCrJ
#526
of 855 outputs
Outputs of similar age
#203,956
of 400,467 outputs
Outputs of similar age from IUCrJ
#15
of 23 outputs
Altmetric has tracked 22,846,662 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 855 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.9. This one is in the 34th percentile – i.e., 34% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 400,467 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 23 others from the same source and published within six weeks on either side of this one. This one is in the 34th percentile – i.e., 34% of its contemporaries scored the same or lower than it.