Last modified by robing on 2022/03/25 09:55
From version 22.2
edited by denker
on 2020/04/03 16:35
Change comment: There is no comment for this version
To version 15.2
edited by epastorelli
on 2020/01/20 14:08
Change comment: There is no comment for this version

Summary

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Author
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1 -XWiki.denker
1 +XWiki.epastorelli
Content
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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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27 27  
28 28  === in the collab (beta) ===
29 29  
30 -* **Copy the collab drive to your drive space**
31 -** Open the Drive from the left menu
32 -** Select the folders 'pipeline' and 'datasets'
33 -** Select 'Copy', and then 'My Library' from the dropdown 'Other Libraries'
34 -
35 -* **Start a Jupyter Hub instance **
36 -copy the URL to another browser page: 'jupyterhub-preview.apps-dev.hbp.eu'
37 -
38 38  * **Edit the config files**
39 39  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]).
40 40  ** stage01_data_entry: [README.md](), [config.yaml]()
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43 43  ** stage04_wavefront_detection: [README.md](), [config.yaml]()
44 44  ** stage05_wave_characterization: [README.md](), [config.yaml]()
45 45  
46 -* **Run the notebook**
47 -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.
48 -* Follow the notebook to install the required packages into your Python kernel, set the output path, and execute the pipeline with snakemake.
37 +* **Start a Jupyter Hub instance (copy the URL below in a separate browser page)**
38 +[[jupyterhub-preview.apps-dev.hbp.eu>>jupyterhub-preview.apps-dev.hbp.eu]]
49 49  
40 +* **Follow the notebook**
41 +In the jupyter hub, navigate to `drive/Shared with groups/Slow Wave Analysis Pipeline/pipeline/showcase_notebooks/run_snakemake_in_collab.ipynb`.
42 +Follow the notebook to install the required packages into your Python kernel, set the output path, and execute the pipeline with snakemake.
50 50  * **Coming soon**
51 51  ** Use of KnowledgeGraph API
52 52  ** Provenance Tracking
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83 83  == References ==
84 84  
85 85  
86 -== License (to discuss) ==
87 -
88 -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.
89 -
90 90  == Acknowledgments ==
91 91  
92 -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).
81 +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
82 +under the Specific Grant Agreement No. 785907 (Human Brain Project SGA2).
93 93  )))
94 94  
95 95