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Author

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1		-XWiki.debonisg
	1	+XWiki.robing

Content

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2	2	(((
3	3	(% class="container" %)
4	4	(((
5		-= (% style="color:inherit" %)Slow Wave Analysis Pipeline(%%) =
	5	+= (% style="--darkreader-inline-color:inherit; color:inherit" %)Slow Wave Analysis Pipeline(%%) =
6	6
7	7	(% class="wikigeneratedid" id="HUseCaseSGA2-SP3-002:IntegratingmultiscaledataA0inareproducibleandadaptablepipeline" %)
8		-(% style="color:inherit; font-size:24px" %)**Use Case SGA2-SP3-002: Integrating multi-scale data in a reproducible and adaptable pipeline**
	8	+(% style="--darkreader-inline-color:inherit; color:inherit; font-size:24px" %)**Use Case SGA2-SP3-002: Integrating multi-scale data in a reproducible and adaptable pipeline**
9	9
10	10	To be discussed Author orders, contributions,...:
11	11
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31	31
32	32	This Collab is aimed at experimental and computational neuroscientists interested in the usage of the Neo and Elephant tools in performing data analysis of spiking data.
33	33
34		-== Executing the pipeline ==
	34	+== How the Pipeline works ==
35	35
36		-[[image:pipeline_flowchart.png]]
	36	+The design of the pipeline aims at interfacing a variety of general and specific analysis and processing steps in a flexible modular manner. Hence, it enables the pipeline to adapt to diverse types of data (e.b. electrical EEG, or optical Calcium Imaging recordings) and to different analysis questions. This makes the analyses a) more reproducible and b) comparable amongst each other since they rely on the same stack of algorithms and any differences in the analysis are fully transparent.
	37	+The individual processing and analysis steps (//blocks, //see// //the arrow-connected elements below) are organized in sequential //stages (//see the columns below//). //Following along the stages the analysis becomes more specific but also allows to branch off at after any stage as each stage yields useful intermediate results is autonomous so that it can be reused and recombined. Within each stage, there is a collection of blocks from which the user can select and arrange the analysis via a config file. Thus, the pipeline can be thought of as a curated database of methods on which an analysis can be constructed by drawing a path along the blocks and stages.
37	37
	39	+== [[image:pipeline_flowchart.png]] ==
	40	+
	41	+== Executing the pipeline ==
	42	+
	43	+(% class="wikigeneratedid" %)
38	38	=== In the collab (beta) ===
39	39
40		-* **Copy the collab drive to your drive space**
41		-** Open the Drive from the left menu
42		-** Select the folders 'pipeline' and 'datasets'
43		-** Select 'Copy', and then 'My Library' from the dropdown 'Other Libraries'
	46	+* (((
	47	+**Copy the collab drive to your drive space**
	48	+
	49	+*
	50	+
	51	+Open the Drive from the left menu
	52	+
	53	+* Select the folders 'pipeline' and 'datasets'
	54	+* Select 'Copy', and then 'My Library' from the dropdown 'Other Libraries'
44	44
	56	+)))
45	45	* **Start a Jupyter Hub instance **
46	46	In another browser, open [[https:~~/~~/lab.ebrains.eu>>https://lab.ebrains.eu]]
47	47
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110	110	)))
111	111
112	112
	125	+== Executing the pipeline ==
	126	+
113	113	(% class="col-xs-12 col-sm-4" %)
114	114	(((
115	115	{{box title="**Contents**"}}