Oral Presentation 26th ACMM “2020 Visions in Microscopy”

Invited talk - Fostering open and advanced data analysis in electron microscopy with HyperSpy (#44)

Francisco de la Peña 1 , Eric Prestat 2 3 , Vidar Fauske 4 , Pierre Burdet 5 , Petras Jokubauskas 6 , Magnus Nord 7 , Tomas Ostasevicius 5 , Katherine MacArthur 8 , Michael Sarahan 3 , Duncan Johnstone 5 , Joshua Taillon 9 , Jonas Lähnemann 10 , Vadim Migunov 8 , Alberto Eljarrat 11 , Jan Caron 8 , Thomas Aarholt 12 , Stefano Mazzucco 9 , Michael Walls 13 , Thomas Slater 2 , Florian Winkler 8 , Paul Quinn 14 , Ben Martineau 5 , Gaël Donval 15 , Robert McLeod 16 , Eric Hoglund 17 , Ivo Alxneit 18 , Daniel Lundeby 4 , Trond Henninen 19 , Andreas Garmannslund 4 , Luiz Zagonel 20 , Ida Hjorth 4 , Håkon Ånes 4
  1. University of Lille, Lille, France
  2. University of Manchester, Manchester, United Kingdom
  3. SuperSTEM Laboratory, Daresbury, United Kingdom
  4. Norwegian University of Science and Technology, Trondheim, Norway
  5. University of Cambridge, Cambridge, United Kingdom
  6. University of Warsaw, Warsaw, Poland
  7. University of Glasgow, Glasgow, United Kingdom
  8. Forschungszentrum Jülich, Jülich, Germany
  9. National Institute of Standards and Technology, Gaithersburg, United States of America
  10. Paul-Drude-Institut für Festkörperelektronik, Berlin, Germany
  11. University of Barcelona, Barcelona, Spain
  12. University of Oslo, Oslo, Norway
  13. Paris-Sud University, Orsay, France
  14. Diamond Light Source, Didcot, United Kingdom
  15. University of Nantes, Nantes, France
  16. Entropy Reduction Algorithmics, Victoria, Canada
  17. University of Virginia, Charlottesville, United States of America
  18. Paul Scherrer Institute, Villigen, Switzerland
  19. Swiss Federal Laboratories for Materials Science and Technology, Dübendorf, Switzerland
  20. Brazilian Nanotechnology National Laboratory, Campinas, Brazil

Recent progress in electron microscopy has resulted in the accessibility of large, multi-dimensional and rich datasets but in order to harvest more insightful information from these advanced measurements, new data analysis and workflows are required. In addition, open and reproducible data analysis is of paramount importance in Science and fortunately, this is being enforced by governmental institutions, funding agencies and scientific journals [1].

HyperSpy [2] is an open-source Python library for multi-dimensional data analysis that emerged in the electron microscopy community in 2007. It offers a powerful syntax for visualizing, analysing and processing multi-dimensional datasets – regardless of their size – by combining state-of-the art libraries available in the Python eco-system. HyperSpy also provides a generic framework that can be used by other software in order to support the flexibility and independent development of new analysis methods with the aim of fostering innovation in the field of electron microscopy.

In this presentation, we will discuss how HyperSpy is supporting the scientific community to use open and advanced data analysis but also how it supports the development of new analysis methods to thrive. HyperSpy can be easily installed and distributed on all three major platforms (Windows, MacOS and Linux), and has rich documentation and learning materials for users in order to lower the entry barrier to advanced and interactive data analysis. By following the best practise established in the open source Python community, HyperSpy has managed to build a community of users and contributors, which is one key of its success. For example, HyperSpy’s open structure and community-driven development has enabled users to easily contribute to the library through the addition of new features and documentation improvements. HyperSpy is now a mature, sustainable open source library and its community and extendible structure makes it ready to address upcoming data analysis challenges in the field of microscopy. [3]

  1. Jeffrey Perkel, Nature (2018) 560, https://doi.org/10.1038/d41586-018-05990-5
  2. Francisco de la Peña, et al. HyperSpy v1.5.2 (2019) http://doi.org/10.5281/zenodo.592838, https://hyperspy.org
  3. The HyperSpy project has not received direct funding and all authors are grateful for support within their respective institutions to make the data analysis tools that they have developed available open-source.