Reproducibility is a cornerstone of scientific research, enabling others to verify findings, build upon existing work, and ultimately drive progress across disciplines. Yet, in imaging-based studies, reproducibility can be particularly challenging. Complex workflows, diverse software tools, and inconsistent documentation often make it difficult to replicate published results.

To address these challenges, community-driven platforms like Galaxy and the Image Data Resource (IDR) are playing a vital role. Galaxy provides an accessible, web-based environment where researchers can run and share analysis workflows without requiring extensive programming knowledge. IDR, in turn, hosts curated imaging datasets from published studies, serving as a valuable resource for reproducible reanalysis.

A recent Galaxy training tutorial demonstrates how to build reproducible 2D spot detection for single-molecule FISH (smFISH) data using a dataset originally published in the IDR. Developed by Riccardo Massei from NFDI4BIOIMAGE, the tutorial guides users step-by-step through the analysis pipeline, showing how to detect fluorescent spots that correspond to individual RNA molecules in fixed cells.

This hands-on example is not only an excellent resource for learning image analysis techniques – it also exemplifies how open data and open tools can work together to promote transparency and reproducibility in life sciences.

Explore the tutorial here: 2D Spot Detection for smFISH using Galaxy

Thanks to Riccardo Massei from the UFZ Leipzig for contributing to accessible and reproducible bioimage analysis!