In every bioinformatics project I’ve been part of, I notice a familiar pattern: there are two very different types of scientists in the lab.

The first group are the dedicated biologists who prefer to stay focused on experiments, hypotheses, and the biological questions that excite them. For them, digging into code or pipelines feels like a distraction. They would rather rely on computational experts and keep their attention where they create the most value.

The second group are the hands-on explorers. Some are trained bioinformaticians, others are not, but what they share is curiosity and determination. When faced with a dataset, they are willing to roll up their sleeves, learn the tools, and take responsibility for their own analyses. With the right support, they thrive.

Neither approach is better. Both are essential. Science depends on a variety of working styles, and supporting them requires different strategies.

The Hands-Off Scientist

For those who do not want to manage code or pipelines, the biggest need is trust. They need reliable infrastructure and expert support that ensures results are reproducible. Without reliable systems and expert guidance, bottlenecks and inconsistencies creep in. With them in place, scientists can focus on designing experiments, asking sharper questions, and interpreting results with confidence.

The Hands-On Scientist

Some scientists find themselves wearing the bioinformatician’s hat, even if that’s not their formal training. Driven by curiosity and necessity, they learn new tools, set up their own environments, and take ownership of their analyses to make sense of their data.

For those scientists, the challenge is not motivation but reliability. They need tools that are approachable, scalable, and reproducible. When results can be rerun and verified, these scientists have the freedom to explore, test hypotheses, and adjust parameters without hitting roadblocks.

The Shared Challenge

No matter which camp you fall into, reproducibility is the shared challenge. It ensures that every result, whether produced by code or through a drag-and-drop user interface, can be trusted, shared, and built upon.

That’s why Via Foundry brings these capabilities together in one platform. It combines intuitive web tools for running analyses, scalable cloud infrastructure with standardized environments, and programmatic options such as the SDK and CLI for automation and customization.

Hands-off scientists can run ready-to-use workflows in managed environments that deliver consistent, traceable results, while hands-on scientists can script, automate, and scale analyses in the same reproducible framework.

Tools such as Workbench extend this flexibility further, providing cloud-powered computing beyond the limits of a laptop. Together, these options ensure every scientist can explore, iterate, and collaborate with confidence, all while contributing to the same reproducible foundation.

The Bigger Picture

Science is advancing rapidly, and the diversity of working styles among researchers will only continue to grow. Some will always prefer to stay focused on experiments. Others will dive into data and analysis. Both roles are necessary, and science is stronger when each can succeed.

Reproducibility is the challenge they share, and also the solution. It ensures that, no matter how you engage with data, the results are reliable. And when results are reliable, science moves forward with greater confidence.

Because in the end, it does not matter which type of scientist you are. Reproducible science is stronger science.