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相关概念视频

X-ray Crystallography02:18

X-ray Crystallography

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The size of the unit cell and the arrangement of atoms in a crystal may be determined from measurements of the diffraction of X-rays by the crystal, termed X-ray crystallography.
Diffraction
Diffraction is the change in the direction of travel experienced by an electromagnetic wave when it encounters a physical barrier whose dimensions are comparable to those of the wavelength of the light. X-rays are electromagnetic radiation with wavelengths about as long as the distance between neighboring...
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X-ray Diffraction of Biological Samples01:10

X-ray Diffraction of Biological Samples

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X-ray diffraction or XRD is an analytical tool that utilizes X-rays to study ordered structures such as crystalline organic and inorganic samples, polycrystalline materials, proteins, carbohydrates, and drugs.
According to Bragg's law, when X-rays strike the sample positioned on a stage, the rays are  scattered by the electron clouds around the sample atoms. The  X-ray diffraction or scattering is caused by constructive interference of the X-ray waves that reflect off the internal...
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相关实验视频

Updated: Jul 7, 2025

Microcrystallography of Protein Crystals and In Cellulo Diffraction
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结晶学数据和数据库的互操作性

Alice Brink1, Ian Bruno2, John R Helliwell3

  • 1Chemistry Department, University of the Free State, Nelson Mandela Drive, Bloemfontein, South Africa.

IUCrJ
|December 22, 2023
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概括

晶体数据越来越多地在科学学科之间互操作,遵守FAIR原则. 提高数据质量和完整性标准对于强大,可重复使用的科学数据集成至关重要.

关键词:
这些都是CODATA的数据.跨领域的整合.数据数据的数据数据的数据.数据库就是数据库.跨学科的跨学科性互操作性互操作性互操作性的互操作性

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科学领域:

  • 结晶学和基于结构的科学.
  • 数据科学和科学计算.

背景情况:

  • 结晶学数据的互操作性对于在数字时代推进基于结构的科学至关重要.
  • 晶体学界在很大程度上遵循FAIR数据原则 (可查找,可访问,可互操作,可重复使用) 通过建立的信息交换协议.
  • 国际晶体学联盟 (IUCr) 与CODATA合作,促进跨领域的数据集成.

研究的目的:

  • 调查结晶学内外数据互操作性的当前状态.
  • 突出数据质量和完整性标准的重要性,以加强FAIR数据原则.
  • 展示跨科学领域成功数据整合的例子.

主要方法:

  • 审查现有的信息交换协议在晶体学.
  • 分析IUCr和CODATA在数据集成方面的合作.
  • 来自专门的微型研讨会的案例研究和示例的介绍.

主要成果:

  • 晶体学显示了高度遵守FAIR数据原则.
  • 人们认识到需要提高数据质量和完整性的标准.
  • 介绍了跨领域数据互操作性的成功例子.

结论:

  • 结晶学数据与其他学科的整合进展良好,受到FAIR原则的推动.
  • 建立数据质量和完整性的强有力的标准对于最终的,可重复使用的科学数据至关重要.
  • 持续的合作和遵守标准将促进更广泛的科学发现.