The need for better workflows in data analysis of electrophysiological signals
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What is a typical workflow for data analysis?

  * Data is recorded (or simulated)
  * Data is stored
  * Data is pre-processed
  * Data is analyzed
  * A paper is written

Where are the complications in such a seemingly simple workflow?


New challenges in dealing with new qualities of electrophysiological data: complexity
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  * Stimulus sets and behavior with difficult or high-dimensional parametrization
  * Multi-modal approaches
  * Massively parallel data streams up to 200 electrodes or more
  * Research questions that aim to exploit the parallel/complex nature of such data: correlations
  
Impacts on data analysis workflows
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**Features of modern experiments:**

  * Large size
  * Various data types
  * Complicated stimuli/behavior
  * Involved, complex analysis
  * →Collaborative effort

**Impacts:**

  * Managing data/results
  * Linking analyses
  * Computational load
  * Reproducibility
  * Code verification
  * Methods validation
  * Experimental complexity
  * Workflow and collaboration
  
  
Managing data/results: Dramatic increase in data volume
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Centralized data storage necessary:

  * Raw signals at 30kHz, 200 channels, 150GB per day, for 6 months → 40TB
  * Experimenter-side post-processing (e.g., spike sorting) adds to storage load
  * Transport of data sets between collaborators / labs / computer systems

Needed:

  * Uniform data structures
  * On-demand loading on laptops and HPC environments
  * Multi-user, persistent, versioned, database-like organization


TO BE COMPLETED

Summary
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We face new dimensions of data analysis workflow complexity!

Verification and validation in data analysis of electrophysiological data occur on different levels

  * dealing with special data features or artifacts
  * advancement and calibration of analysis methods
  * validation of a specific hypothesis by a combination of methods

The complexity of the analysis workflow is inflated by

  * dependencies between analysis parameters
  * dependencies between different signal types
  * advanced, non-trivial surrogate technique
  * additional calibration analyses tuned to parameters
  * combinatorial explosion via (stacked) surrogate techniques
  * sensitivity of parameters leading to complex parameter spaces

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