ArticleActa crystallographica. Section A, Foundations and advances2023
Optimal estimated standard uncertainties of reflection intensities for kinematical refinement from 3D electron diffraction data.
Article in Acta crystallographica. Section A, Foundations and advances, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Article
- Article
- Robust error calibration for serial crystallography.Acta crystallographica. Section D, Structural biology · 2025Article
- Crystal Structure of NdACS omega · 2025Article
- How to use DIALS to process chemical crystallography 3D ED rotation data from pixel array detectors.Acta crystallographica. Section C, Structural chemistry · 2025Article
- MicroED: Unveiling the Structural Chemistry of Plant Biomineralisation.Molecules (Basel, Switzerland) · 2024Article
- Article
- TAAM refinement on high-resolution experimental and simulated 3D ED/MicroED data for organic molecules.Acta crystallographica. Section C, Structural chemistry · 2024Article
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5 authors.
Funding
Abstract
Estimating the error in the merged reflection intensities requires a full understanding of all the possible sources of error arising from the measurements. Most diffraction-spot integration methods focus mainly on errors arising from counting statistics for the estimation of uncertainties associated with the reflection intensities. This treatment may be incomplete and partly inadequate. In an attempt to fully understand and identify all the contributions to these errors, three methods are examined for the correction of estimated errors of reflection intensities in electron diffraction data. For a direct comparison, the three methods are applied to a set of organic and inorganic test cases. It is demonstrated that applying the corrections of a specific model that include terms dependent on the original uncertainty and the largest intensity of the symmetry-related reflections improves the overall structure quality of the given data set and improves the final R
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