Description: Designing Workflows for the Reproducible Analysis of Electrophysiological Data

This title appears in the Scientific Report : 2016

Designing Workflows for the Reproducible Analysis of Electrophysiological Data

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,...

Personal Name(s):	Amunts, Katrin (Editor)
	Grandinetti, Lucio (Editor) / Lippert, Thomas (Editor) / Petkov, Nicolai (Editor) / Denker, Michael (Corresponding author) / Grün, Sonja
Contributing Institute:	Computational and Systems Neuroscience; INM-6 Jülich Supercomputing Center; JSC Strukturelle und funktionelle Organisation des Gehirns; INM-1 Theoretical Neuroscience; IAS-6
Published in:	Brain-Inspired Computing / Amunts, Katrin (Editor) ; Cham : Springer International Publishing, 2016, Chapter 5 ; ISSN: 0302-9743=1611-3349 ; ISBN: 978-3-319-50861-0=978-3-319-50862-7
Imprint:	Cham Springer International Publishing 2016
Physical Description:	58 - 72
ISBN:	978-3-319-50861-0 (print) 978-3-319-50862-7 (electronic)
DOI:	10.1007/978-3-319-50862-7_5
Conference:	BrainComp 2015
Document Type:	Contribution to a conference proceedings Contribution to a book
Research Program:	Kausative Mechanismen mesoskopischer Aktivitätsmuster in der auditorischen Kategorien-Diskrimination Brain-inspired multiscale computation in neuromorphic hybrid systems The Human Brain Project Supercomputing and Modelling for the Human Brain Connectivity and Activity Optogenetische Analyse der für kognitive Fähigkeiten zuständigen präfrontal-hippokampalen Netzwerke in der Entwicklung
Series Title:	Lecture Notes in Computer Science 10087
	Publikationsportal JuSER

Please use the identifier: http://dx.doi.org/10.1007/978-3-319-50862-7_5 in citations.

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.