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| Main Authors: | , |
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| Format: | Recurso digital |
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Zenodo
2026
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| Online Access: | https://doi.org/10.5281/zenodo.19449658 |
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Table of Contents:
- <h1>Abstract</h1> <p dir="ltr">Reproducibility in biological microscopy relies on data quality metadata completeness analysis transparency and cross laboratory consistency. Despite the availability of public repositories like IDR and BBBC reproducible re-analysis remains limited especially in plant nuclear imaging. We introduce the Reproducibility Risk Assessment Framework RRAF a five dimensional metric integrating metadata completeness data quality analysis transparency segmentation reproducibility and cross dataset generalisability to quantify reproducibility risk. Applying RRAF to six representative plant nuclear imaging datasets 50 percent scored below the high risk threshold mean RRAF = 0.486 with analysis transparency being the most frequent bottleneck. Cross dataset comparisons show inter dataset variability dominates biological signal nuclear area CV > 0.75. RRAF scores strongly correlate with mean image SNR r = 0.88 p = 0.020 and simple interventions such as adding code repositories can substantially improve reproducibility +0.18 mean RRAF. RRAF is lightweight OMERO compatible and FAIR compliant providing a practical tool to benchmark and enhance reproducibility in bioimaging datasets.</p> <p dir="ltr">Keywords: reproducibility; bioimaging; Image Data Resource; BBBC; RRAF; plant nuclear architecture; FAIR data; OMERO; nuclear segmentation; computational biology; open science; metadata; signal-to-noise ratio; Arabidopsis thaliana</p> <p> </p>