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1		-XWiki.denker
	1	+XWiki.robing

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4	4	(((
5	5	= (% style="color:inherit" %)Slow Wave Analysis Pipeline(%%) =
6	6
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
	7	+= (% style="color:inherit; font-size:24px" %)Integrating multiscale data in a reproducible and adaptable pipeline(%%) =
9	9	)))
10	10	)))
11	11
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13	13	(((
14	14	(% class="col-xs-12 col-sm-8" %)
15	15	(((
16		-== Flexible workflows to generate multi-scale analysis scenarios ==
	15	+= What can I find here? =
17	17
18		-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.
	17	+...
19	19
	19	+= Who has access? =
	20	+
	21	+Describe the audience of this collab.
	22	+
20	20	== Executing the pipeline ==
21	21
22	22	[[image:pipeline_flowchart.png]]
23	23
24		-=== In the collab (beta) ===
	27	+=== in the collab (beta) ===
25	25
26	26	* **Copy the collab drive to your drive space**
27	27	** Open the Drive from the left menu
28	28	** Select the folders 'pipeline' and 'datasets'
29		-** Select 'Copy', and then 'My Library' from the dropdown 'Other Libraries'
	32	+** Select 'Copy', and 'My Library' from the dropdown 'Other Libraries'
30	30
31	31	* **Start a Jupyter Hub instance **
32		-In another browser, open [[https:~~/~~/lab.ebrains.eu>>https://lab.ebrains.eu]]
33		-
	35	+copy the URL below in a separate browser page: 'jupyterhub-preview.apps-dev.hbp.eu'
	36	+
34	34	* **Edit the config files**
35	35	Each stage has a config file to specify which analysis/processing blocks to execute and which parameters to use. General and specific information about the blocks and parameters can found in the README and config files. The default values are set for an example dataset (ECoG, anesthetized mouse, IDIBAPS [ref]).
36	36	** stage01_data_entry: [README.md](), [config.yaml]()
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39	39	** stage04_wavefront_detection: [README.md](), [config.yaml]()
40	40	** stage05_wave_characterization: [README.md](), [config.yaml]()
41	41
42		-* **Run the notebook**
43		-In the jupyter hub, navigate to `drive/My Libraries/My Library/pipeline/showcase_notebooks/run_snakemake_in_collab.ipynb`, or where you copied the 'pipeline' folder to.
	45	+* **Follow the notebook**
	46	+In the jupyter hub, navigate to `drive/Shared with groups/Slow Wave Analysis Pipeline/pipeline/showcase_notebooks/run_snakemake_in_collab.ipynb`.
44	44	* Follow the notebook to install the required packages into your Python kernel, set the output path, and execute the pipeline with snakemake.
45		-
46	46	* **Coming soon**
47	47	** Use of KnowledgeGraph API
48	48	** Provenance Tracking
49	49	** HPC support
50	50
51		-=== Local execution ===
	53	+=== locally ===
52	52
53	53	* **Get the code**
54	54	The source code of the pipeline is available via Github: [INM-6/wavescalephant]('https:~/~/github.com/INM-6/wavescalephant') and can be cloned to your machine ([how to use Github]()).
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79	79	== References ==
80	80
81	81
82		-== License (to discuss) ==
83		-
84		-All text and example data in this collab is licensed under Creative Commons CC-BY 4.0 license. Software code is licensed under a modified BSD license.
85		-
86	86	== Acknowledgments ==
87	87
88		-This open source software code was developed in part or in whole in the Human Brain Project, funded from the European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 785907 (Human Brain Project SGA2).
	86	+This open source software code was developed in part or in whole in the Human Brain Project, funded from the European Union’s Horizon 2020 Framework Programme for Research and Innovation
	87	+under the Specific Grant Agreement No. 785907 (Human Brain Project SGA2).
89	89	)))
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