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Related Concept Videos

Overview of Microscopy Techniques01:22

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The early pioneers of microscopy opened a window into the invisible world of microorganisms. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes that leveraged nonvisible light, such as fluorescence microscopy that uses an ultraviolet light source and electron microscopy that uses short-wavelength electron beams. These advances significantly improved magnification, image resolution, and contrast. By comparison, the...
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Transmission electron microscopy (TEM) can be used to determine the 3D structure of biological samples with the help of techniques such as electron microscope tomography and single-particle reconstruction. While single-particle reconstruction can examine macromolecules and macromolecular complexes in vitro conditions only, tomography permits the study of cell components or small cells in vivo.
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Atomic force microscopy (AFM) is a type of scanning probe microscopy that can analyze topographic details of various specimens like ceramics, glass, polymers, and biological samples. AFM offers over 1000 times more resolution than the optical imaging system. Images generated from AFM are three-dimensional surface profiles, offering an advantage over the flat, two-dimensional images from other imaging techniques.
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Hystorian: A processing tool for scanning probe microscopy and other n-dimensional datasets.

Loïc Musy1, Ralph Bulanadi1, Iaroslav Gaponenko1

  • 1University of Geneva, Department of Quantum Matter Physics, 1204, Geneva, Switzerland.

Ultramicroscopy
|July 2, 2021
PubMed
Summary

Hystorian is a new Python package for materials science data analysis. It enhances data traceability, reproducibility, and archival by converting proprietary formats to HDF5 and storing workflows and datasets together.

Keywords:
Big dataData processingImage registrationPiezoresponse force microscopyScanning probe microscopy

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Area of Science:

  • Materials Science
  • Data Science
  • Computational Science

Background:

  • Modern materials science research generates large, complex datasets from multiple characterization techniques.
  • Efficient data processing, storage, and reliable tracking are critical for reproducibility and open science.
  • Existing methods often struggle with managing diverse data types and ensuring workflow traceability.

Purpose of the Study:

  • Introduce Hystorian, a Python package designed to improve data processing in materials science.
  • Enhance the traceability, reproducibility, and archival capabilities of scientific data management.
  • Provide a flexible and extensible toolkit for analyzing materials characterization data.

Main Methods:

  • Developed Hystorian as a generic Python package for materials science data analysis.
  • Implemented conversion of proprietary data formats into the open Hierarchical Data Format (HDF5).
  • Integrated automatic storage of datasets and associated processing workflows into a unified location.

Main Results:

  • Hystorian facilitates the management of multiple data types within a single, organized structure.
  • The package ensures that both raw data and processing steps are version-controlled and accessible.
  • Initial toolkits for scanning probe microscopy and X-ray diffraction analysis are included.

Conclusions:

  • Hystorian offers a robust solution for managing and analyzing complex materials science datasets.
  • The package promotes open science principles by enhancing data reproducibility and traceability.
  • Hystorian's extensible design allows for integration into existing workflows and adaptation to various user needs.