The reproducibility gap in graph neural network workflows for cell dynamics: A checklist‐driven case study

Abstract

As part of a Global BioImage Analysts' Society (GloBIAS) initiative, we evaluated the reproducibility of a Graph Neural Network (GNN) study on cell dynamics using structured, community‐developed checklists from the Quality Assessment and Reproducibility for Instruments and Images in Light Microscopy (QUAREP‐LiMi) initiative. Notably, these checklists were published after the 2022 Target Paper, meaning our assessment is necessarily retrospective. Our assessment revealed a gap between recent reporting standards and practical execution. Reproduction attempts across multiple environments confirmed deficiencies, including the absence of explicit image metadata (pixel size and timestamps), which limits quantitative interpretation. Furthermore, the absence of a software container, which was typical for the time of publication and the incomplete dependency list, necessitated complex manual environment configuration, increasing the barrier to entry. This experience highlights the contrast between the reproducibility expectations at the time and newer standards achieved by minimal compliance and true functional access. We conclude by presenting a detailed discussion of the broader implications for the quantitative biology community and propose actionable recommendations including robust environment containerisation and standardised data deposition to ensure complex computational workflows become scientifically sound and reusable resources. This retrospective case study evaluates a legacy computational paper against modern reproducibility framework, not to critique past non‐compliance, but to extract actionable lessons for future research standards