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DACETS: research data management for individual...

Ivan
June 14, 2013

DACETS: research data management for individual scientists

Handling of research data (raw data and all associated experimental meta-data) is still very hard for individual scientist and small research groups and few tools are available to lessen the burden.

Ivan

June 14, 2013
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  1. The mission of the WLCG project is to provide global

    computing resources to store, distribute and analyse the ~25 Petabytes (25 million Gigabytes) of data annually generated by the Large Hadron Collider (LHC) at CERN on the Franco-Swiss border.
  2. 60 hrs of video / min 60 *100MB * 60min

    * 24 = 8TB/day (3 PB/year)
  3. original brain extract brain split GM/WM segm sulci skeleton simple

    surfaces 3D recon & labeling data processing pipeline
  4. data organization ivan@pavlov:~/mri_data/fMRI_Lang_var $ tree -L 2 . ├── Anat_variability/

    │ ├── 01_GRV/ │ ├── 02_GLZ/ │ ├── 03_LIB/ │ ├── 04_RAC/ │ ├── 05_DUB/ │ └── 06_WIL/ └── fMRI_Lang/ ├── 01_GRV/ ├── 02_GLZ/ ├── 03_LIB/ ├── 04_RAC/ ├── 05_DUB/ └── 06_WIL/ ivan@pavlov:~/mri_data/fMRI_Lang_var $ tree fMRI_Lang/ fMRI_Lang/ ├── 01_GRV/ │ ├── analyze/ │ │ ├── fmri_noun.hdr │ │ ├── fmri_noun.img │ │ ├── fmri_verb.hdr │ │ ├── fmri_verb.img │ │ ├── t1_3d.hdr │ │ ├── t1_3d.img │ │ ├── t1_axe.hdr │ │ └── t1_axe.img │ ├── maps/ │ │ ├── zscore_noun.hdr │ │ ├── zscore_noun.img │ │ ├── zscore_verb.hdr │ │ └── zscore_verb.img │ └── raw/ │ ├── DICOM/ │ │ ├── 0000001.ima │ │ ├── 0000002.ima │ │ └── ... (~1000 files) │ └── DICOMDIR ├── 02_GLZ/ ├── 03_LIB/ original project image sequence header file derived project
  5. data organization ivan@pavlov:~/mri_data/fMRI_Lang_var $ tree fMRI_Lang/ fMRI_Lang/ ├── 01_GRV/ │

    ├── analyze/ │ │ ├── fmri_noun.hdr │ │ ├── fmri_noun.img │ │ ├── fmri_verb.hdr │ │ ├── fmri_verb.img │ │ ├── t1_3d.hdr │ │ ├── t1_3d.img │ │ ├── t1_axe.hdr │ │ └── t1_axe.img │ ├── maps/ │ │ ├── zscore_noun.hdr │ │ ├── zscore_noun.img │ │ ├── zscore_verb.hdr │ │ └── zscore_verb.img │ └── raw/ │ ├── DICOM/ │ │ ├── 0000001.ima │ │ ├── 0000002.ima │ │ └── ... (~1000 files) │ └── DICOMDIR ├── 02_GLZ/ ├── 03_LIB/ ivan@pavlov:~/mri_data/fMRI_Lang_var $ tree Anat_variability/ Anat_variability/ ├── 01_GRV/ │ ├── READMEs/ │ ├── anatomy/ │ │ ├── T1.hdr │ │ ├── T1.img │ │ ├── T1_norm.hdr │ │ └── T1_norm.img │ ├── graphe/ │ ├── segment/ │ │ ├── T1_brain.hdr │ │ └── T1_brain.img │ └── tri/ │ ├── LHemi.dat │ └── RHemi.dat ├── 02_GLZ/ ├── 03_LIB/ ├── 04_RAC/ ├── 05_DUB/ └── 06_WIL/
  6. data organization $ find . -name \*3d.img -exec script.py {}\;

    useful, but clearly not enough (binary headers)
  7. what’s needed? help with data org structure tracking notes/comments about

    data tracking provenance search (beyond filename/{c,m}time) efficient local/remote sync ...
  8. “Before someone suggests OME, we don’t have the wherewithal to

    move to OMERO – the server setup is beyond me and not something that we can implement easily through our IT support. This is one for the future…”
  9. isn’t it a solved problem? Amazon AMIs Media management (iTunes

    et al.) SW packages (deb, rpm, egg, npm,...)
  10. setup.py setup(name='beets', version='1.1.1', description='music tagger and library organizer', author='Adrian Sampson',

    author_email='[email protected]', url='http://beets.radbox.org/', license='MIT', long_description=_read('README.rst'), packages=[ 'beets', 'beets.ui', ] install_requires=[ 'mutagen>=1.21', 'musicbrainzngs>=0.4', 'pyyaml', ] classifiers=[ 'Topic :: Multimedia :: Sound/Audio', 'License :: OSI Approved :: MIT License', 'Environment :: Console', 'Programming Language :: Python :: 2.7', ], )
  11. package.json { "name": "http-server", "version": "0.3.0", "description": "a simple command-line

    http server", "license": "MIT", "author": "Nodejitsu <[email protected]>", "contributors": [ { "name": "Marak Squires", "email": "[email protected]" } ], "repository": { "type": "git", "url": "https://github.com/nodejitsu/http-server.git" }, "keywords": [ "cli", "http", "server" ], "dependencies" : { "flatiron" : "0.1.x", } }
  12. data-package.json { # general "metadata" name: "a-unique-human-readable-and-url-usable-identifier", title: "A nice

    title", licenses: [...], sources: [...], resources: [ { ... resource info described below ... } ], # optional "metadata" ... additional information ... }
  13. data-package.yaml --- project: name: "project name" license: ... description: ...

    resources: ... experiment: sample: "sample_id", date: "2013-01-01 ", equipment: ... acquisition: # (simulation, data-analysis) settings: ... data: - path: url
  14. dacets $ dct import /path/to/project/ $ dct add /path/to/file $

    dct remove /path/to/file $ dct find “mri+brain+3T+T1” -o dacets.json $ dct sync -f dacet.json <src> <dest> >>> from dacets import Dacets >>> dc1 = Dacets.load(‘dacets.json’) >>> dc2 = Dacets.find(‘mri+brain+3T+T1’) >>> run_pipeline(on=dc2)