# Ten common misconceptions about Galaxy (and why they are wrong!)

## TL;DR

This paper addresses 10 common misunderstandings about Galaxy, showing it is a versatile, scalable, and high-quality platform for data analysis in science and beyond.

## Contribution

The paper systematically refutes misconceptions about Galaxy using evidence from its features, use cases, and community.

## Key findings

- Galaxy supports a wide range of scientific domains beyond genomics.
- Galaxy scales effectively for large datasets and maintains high software quality.
- Galaxy is used in education, research, and clinical workflows globally.

## Abstract

Galaxy is a widely used open-source platform for accessible, reproducible, transparent and scalable data analysis in the life sciences and beyond. Despite its growing adoption across domains, several misconceptions persist about its scope, usability, scalability and relevance to academia and industry. In this manuscript, we identify and address 10 common misconceptions about Galaxy, ranging from the belief that it is limited to genomics, lacks scalability, or is only useful for teaching, to doubts about its ability to support secure data analysis or maintain high software quality as a free and open-source project. We refute each misconception with present evidence based on Galaxy’s technical features, real-world use cases, user communities and governance structures. We show that Galaxy is a mature and versatile platform capable of supporting cutting-edge scientific research, education and even clinical workflows across a wide variety of disciplines. By clarifying existing misconceptions, we aim to help researchers, educators, developers and decision-makers better appreciate Galaxy’s capabilities and potential within their fields.

Galaxy is a free, community-built web interface that helps scientists analyse data in a way that’s easy to use, transparent and reproducible. While it is widely used in biology and other fields, many people still do not fully understand its capabilities. Some think it’s only for analysing DNA, or that it’s too slow, too technical, or only useful in classrooms. In this article, we take on 10 common misconceptions about the Galaxy interface and show how it’s a powerful, flexible tool used by researchers, educators and even clinicians worldwide. With real-world examples and current evidence, we explain how Galaxy supports health data analysis, scales to massive datasets and maintains high software quality—all while staying free and open to everyone. We aim to help more people understand Galaxy’s true potential and feel confident using it in their work.