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File-based localization of numerical perturbations in data analysis pipelines.

Ali Salari1, Gregory Kiar2,3, Lindsay Lewis2

  • 1Department of Computer Science and Software Engineering, Concordia University, Montreal, QC, Canada.

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A new tool, Spot, identifies numerical instabilities in data analysis pipelines. It pinpoints registration processes as the main cause of these computational errors, aiding the scientific reproducibility crisis.

Keywords:
NeuroimagingOperating SystemsPipelinesReproducibility

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Area of Science:

  • Computational science
  • Data analysis
  • Scientific reproducibility

Background:

  • Data analysis pipelines are susceptible to computational conditions, leading to numerical errors.
  • These errors may significantly contribute to the current reproducibility crisis in science.
  • The exact sources and propagation pathways of these instabilities remain unclear.

Purpose of the Study:

  • To develop a method for identifying specific processes within data analysis pipelines that introduce numerical differences.
  • To investigate the impact of varying computational conditions on pipeline stability.

Main Methods:

  • Introduction of Spot, a novel tool for detecting numerical instabilities.
  • Leveraging system-call interception via ReproZip for provenance graph reconstruction and comparison.
  • Utilizing a pipeline instrumentation-free approach.

Main Results:

  • Spot successfully identified processes causing numerical differences across computational environments.
  • Application to Human Connectome Project structural pre-processing pipelines revealed key instability sources.
  • Linear and non-linear registration processes were identified as major contributors to numerical instabilities.

Conclusions:

  • Spot is an effective tool for diagnosing numerical instabilities in data analysis pipelines.
  • Registration processes are critical points of failure for computational reproducibility.
  • Findings support the need for robust error detection in scientific workflows.