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Specification Overview

The signalJourney specification provides a standardized JSON format for describing biosignal processing pipelines and their outputs. Its primary goal is to enhance reproducibility and data sharing by capturing detailed provenance information.

Core Concepts

  • Pipeline Description: Defines the overall goal, software environment, and execution context of the processing workflow.
  • Processing Steps: Details each individual operation performed on the data, including the specific software, function calls, parameters used, inputs, and outputs.
  • Data Provenance: Explicitly links processing steps, defining dependencies and tracking data flow.
  • Quality Metrics: Allows for embedding quantitative or qualitative metrics about the data quality or processing outcomes at various stages.
  • Extensibility: Uses a namespace system to allow for domain-specific extensions (e.g., for EEG, MEG) while maintaining a core standard.

File Structure

A signalJourney file is a JSON object with several key top-level fields:

  • sj_version: The version of the signalJourney specification the file adheres to.
  • schema_version: The version of the JSON schema file itself.
  • description: A brief, human-readable description of the pipeline documented in the file.
  • pipelineInfo: An object containing metadata about the overall pipeline (name, version, type, execution date, etc.).
  • processingSteps: An array of objects, each detailing a single step in the pipeline.
  • summaryMetrics (optional): An object containing summary quality metrics for the entire pipeline output.
  • extensions (optional): An object containing namespaced, domain-specific extensions.
  • versionHistory (optional): An array documenting changes to the signalJourney file itself.

Purpose

By standardizing how processing pipelines are documented, signalJourney aims to:

  • Improve reproducibility by capturing exact parameters and software versions.
  • Facilitate data sharing and meta-analysis by providing rich, machine-readable provenance.
  • Enable automated analysis of processing pipelines across different studies and labs.
  • Provide a clear audit trail for complex data transformations.

See the Fields section for a detailed description of each component.