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Updated: Nov 2, 2025

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Bioimage analysis workflows: community resources to navigate through a complex ecosystem.

Perrine Paul-Gilloteaux1,2, Sébastien Tosi3, Jean-Karim Hériché4

  • 1Université de Nantes, CNRS, INSERM, l'institut du thorax, Nantes, F-44000, France.

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Summary
This summary is machine-generated.

This opinion paper discusses challenges and recommendations for finding, handling, and developing bioimage analysis workflows. It emphasizes FAIR principles for reproducible science in the era of big data and remote collaboration.

Keywords:
Bioimage analysisFAIR principlesNEUBIAScollectionscomponentsknowledge databaseremote computingscientific workflow management systemsworkflows

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Area of Science:

  • Bioimage analysis
  • Scientific workflows
  • Computational biology

Background:

  • Bioimage analysis workflows are crucial but face challenges with big data and remote work.
  • The NEUBIAS community aims to organize information and actors in bioimage analysis.
  • Sharing and reproducibility are key concerns in modern scientific research.

Purpose of the Study:

  • To provide recommendations for finding, handling, and developing bioimage analysis workflows.
  • To address challenges in running workflows in remote and cloud environments.
  • To promote the adoption of FAIR principles for bioimage analysis workflows.

Main Methods:

  • Opinion paper based on community experience and emerging trends.
  • Discussion of challenges related to data storage, computing, and remote execution.
  • Exploration of tools for cloud and High Performance Computing (HPC) execution.

Main Results:

  • Local solutions are limiting due to big data and complex algorithms.
  • Reproducibility and documentation are critical for workflow sharing.
  • FAIR principles (Findable, Accessible, Interoperable, Reusable) are essential for open science.

Conclusions:

  • Navigating the bioimage analysis workflow ecosystem requires strategic approaches.
  • Cloud and HPC resources offer solutions for intensive analysis.
  • Implementing FAIR principles enhances workflow usability and scientific reproducibility.