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@freeman-lab
Last active September 14, 2016 17:04
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gbl analysis presentation

principles

  • commitment to open source
  • scalability
  • transparency
  • modularity
  • standardization
  • iteration
  • avoiding reinvention
  • sharing of tools and data

proposal 1

standard format for data and metadata

  • build on and expand NWB
  • collect metadata at start of GBL experiments
  • can use custom tools so long as format is the same
  • metadata for processed data includes preprocessing

v.2: tools to streamline metadata creation


proposal 2

standardized preprocessing

  • spike sorting and cell segmentation / spike detection
  • pipeline built out of modular components
  • all tools output to the standard format
  • streamlined interfaces for ease of use

v.2: web-based version that runs in the cloud


proposal 3

benchmarking and ground truth

  • labs collect ground truth data
  • use and improve existing benchmarking systems
  • prior art: neurofinder / spikefinder
  • labs develop and share new algorithms

proposal 4

cloud storage of processed data

  • streamlined uploading tools with format validation
  • full metadata required upon upload
  • raw data stored locally but available upon request

proposal 5

data browsing

  • web-based tools ingest data into a database
  • support public / private views with a common interface
  • APIs will provide access from programming languages
  • interface layer for browsing and querying across labs

v.2: relational queries and analytics


proposal 6

analysis reproducibility

  • analysis modules published to GitHub
  • executable scripts published to GitHub (e.g. Jupyter)
  • use Docker + Binder to make immediately reproducible
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