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001     826140
005     20240313103120.0
020 _ _ |a 978-3-319-50861-0 (print)
020 _ _ |a 978-3-319-50862-7 (electronic)
024 7 _ |a 10.1007/978-3-319-50862-7_5
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024 7 _ |a 0302-9743
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024 7 _ |a 1611-3349
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037 _ _ |a FZJ-2017-00395
041 _ _ |a English
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100 1 _ |a Amunts, Katrin
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111 2 _ |a BrainComp 2015
245 _ _ |a Designing Workflows for the Reproducible Analysis of Electrophysiological Data
260 _ _ |a Cham
|c 2016
|b Springer International Publishing
295 1 0 |a 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
300 _ _ |a 58 - 72
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490 0 _ |a Lecture Notes in Computer Science
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520 _ _ |a 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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536 _ _ |a DFG project 237833830 - Optogenetische Analyse der für kognitive Fähigkeiten zuständigen präfrontal-hippokampalen Netzwerke in der Entwicklung (237833830)
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700 1 _ |a Grandinetti, Lucio
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700 1 _ |a Lippert, Thomas
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700 1 _ |a Petkov, Nicolai
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700 1 _ |a Denker, Michael
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700 1 _ |a Grün, Sonja
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