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Designing Workflows for the Reproducible Analysis of Electrophysiological Data

  • Michael DenkerEmail author
  • Sonja Grün
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10087)

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.

Keywords

Workflow Electrophysiology Metadata Data analysis Data storage Reproducibility 

Notes

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).

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA BRAIN Institute IJülich Research CentreJülichGermany
  2. 2.Osaka UniversityOsakaJapan
  3. 3.RIKEN Brain Science InstituteWakoJapan
  4. 4.Theoretical Systems NeurobiologyRWTH Aachen UniversityAachenGermany

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