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Designing Workflows for the Reproducible Analysis of Electrophysiological Data

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Book cover Brain-Inspired Computing (BrainComp 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10087))

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Abstract

The workflows that cover the experimental recording of neuronal data up to the publication of figures that illustrate neuroscientific analysis results are interwoven and complex. Unfortunately, current implementations of such workflows of electrophysiological research are far from being automatized, and software supporting such a goal is largely in development or missing. In consequence, the level of reproducibility of data analysis is poor compared to other scientific disciplines. Although the problem is well-known and leads to ineffective, unsustainable science, there is no solution in sight in terms of a complete, provenance-tracked workflow. Here, we outline principle challenges that complicate the design of workflows for electrophysiological research. We detail how existing tools can be integrated to form partial workflows which address some of the challenges. On the basis of a concrete workflow implementation we discuss open questions and urgently needed software components.

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Notes

  1. 1.

    http://www.csn.fz-juelich.de/survey.

  2. 2.

    http://neuroshare.sourceforge.net/index.shtml.

  3. 3.

    https://crcns.org/NWB.

  4. 4.

    http://neuralensemble.org/neo.

  5. 5.

    https://github.com/INM-6/python-odmltables.

  6. 6.

    https://github.com/G-Node/nix.

  7. 7.

    http://neuralensemble.org/elephant.

  8. 8.

    https://github.com/NeuralEnsemble/NeuroTools.

  9. 9.

    https://pythonhosted.org/OpenElectrophy.

  10. 10.

    http://www.martinos.org/mne/stable/index.html.

  11. 11.

    http://martinos.org/mne/dev/auto_examples/io/plot_objects_from_arrays.html.

  12. 12.

    http://datalad.org.

  13. 13.

    http://www.vistrails.org.

  14. 14.

    http://pipeline.loni.ucla.edu.

  15. 15.

    https://www.unicore.eu.

  16. 16.

    http://neuralensemble.org/trac/sumatra.

  17. 17.

    https://crcns.org.

  18. 18.

    http://www.carmen.org.uk.

  19. 19.

    https://www.humanbrainproject.eu.

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Acknowledgments

This work was supported by the Helmholtz Portfolio Theme Supercomputing and Modeling for the Human Brain (SMHB), EU grant 604102 (Human Brain Project, HBP) and FP7-ICT-2009-6 (BrainScales), Priority Program 1665 of the DFG (DE 2175/1-1 and GR 1753/4-1).

Author information

Authors and Affiliations

  1. Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA BRAIN Institute I, Jülich Research Centre, Jülich, Germany

    Michael Denker & Sonja Grün

  2. Osaka University, Osaka, Japan

    Sonja Grün

  3. RIKEN Brain Science Institute, Wako, Japan

    Sonja Grün

  4. Theoretical Systems Neurobiology, RWTH Aachen University, Aachen, Germany

    Sonja Grün

Authors
  1. Michael Denker
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  2. Sonja Grün
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Correspondence to Michael Denker .

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Editors and Affiliations

  1. Forschungszentrum Jülich, Jülich, Germany

    Katrin Amunts

  2. University of Calabria , Rende, Italy

    Lucio Grandinetti

  3. Forschungszentrum Jülich, Jülich, Germany

    Thomas Lippert

  4. University of Groningen , Groningen, The Netherlands

    Nicolai Petkov

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Denker, M., Grün, S. (2016). Designing Workflows for the Reproducible Analysis of Electrophysiological Data. In: Amunts, K., Grandinetti, L., Lippert, T., Petkov, N. (eds) Brain-Inspired Computing. BrainComp 2015. Lecture Notes in Computer Science(), vol 10087. Springer, Cham. https://doi.org/10.1007/978-3-319-50862-7_5

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  • DOI: https://doi.org/10.1007/978-3-319-50862-7_5

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