TY  - CHAP
AU  - Denker, Michael
AU  - Grün, Sonja
A3  - Amunts, Katrin
A3  - Grandinetti, Lucio
A3  - Lippert, Thomas
A3  - Petkov, Nicolai
TI  - Designing Workflows for the Reproducible Analysis of Electrophysiological Data
VL  - 10087
CY  - Cham
PB  - Springer International Publishing
M1  - FZJ-2017-00395
SN  - 978-3-319-50861-0 (print)
T2  - Lecture Notes in Computer Science
SP  - 58 - 72
PY  - 2016
AB  - 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.
T2  - BrainComp 2015
CY  - ,  ()
LB  - PUB:(DE-HGF)8 ; PUB:(DE-HGF)7
DO  - DOI:10.1007/978-3-319-50862-7_5
UR  - https://juser.fz-juelich.de/record/826140
ER  -