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000826140 0247_ $$2ISSN$$a1611-3349
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000826140 1001_ $$0P:(DE-Juel1)131631$$aAmunts, Katrin$$b0$$eEditor$$ufzj
000826140 1112_ $$aBrainComp 2015
000826140 245__ $$aDesigning Workflows for the Reproducible Analysis of Electrophysiological Data
000826140 260__ $$aCham$$bSpringer International Publishing$$c2016
000826140 29510 $$aBrain-Inspired Computing / Amunts, Katrin (Editor) ; Cham : Springer International Publishing, 2016, Chapter 5 ; ISSN: 0302-9743=1611-3349 ; ISBN: 978-3-319-50861-0=978-3-319-50862-7
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000826140 4900_ $$aLecture Notes in Computer Science$$v10087
000826140 520__ $$aThe 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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000826140 7001_ $$0P:(DE-HGF)0$$aGrandinetti, Lucio$$b1$$eEditor
000826140 7001_ $$0P:(DE-Juel1)132179$$aLippert, Thomas$$b2$$eEditor$$ufzj
000826140 7001_ $$0P:(DE-HGF)0$$aPetkov, Nicolai$$b3$$eEditor
000826140 7001_ $$0P:(DE-Juel1)144807$$aDenker, Michael$$b4$$eCorresponding author$$ufzj
000826140 7001_ $$0P:(DE-Juel1)144168$$aGrün, Sonja$$b5$$ufzj
000826140 773__ $$a10.1007/978-3-319-50862-7_5
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