Why science gets harder when more people are involved

Andrew Wyatt, Chief Growth Officer of Sapio Sciences*, discusses how metadata is the solution to the challenges of collaborative scientific research.

We all know the old adage “too many cooks spoil the broth”, but it’s perhaps underappreciated that the same logic is increasingly playing out in the world of scientific research.

Biopharma research is moving faster than ever, and with the rise of powerful technologies like AI, machine learning and computational biology, researchers can now explore questions once thought impossible to tackle. These technologies help teams make sense of the massive amounts of data generated in drug discovery research, something that could overwhelm even the most seasoned scientist working alone.

Whilst the science is complex, it’s not always the hardest part. In practice, the bigger challenge often turns out to be something supposedly simple: working together.

Scientific research was often a solo pursuit, with a single biologist designing the experiment, running it, and analysing the results. This approach gave one decision-maker the full picture. From what materials were used, to how the samples were prepared and processed, to how the final data was analysed, that scientist was fully informed. There was no handoff, or potential for misunderstanding, because there was no one else involved.

That’s no longer the case. Today, research is increasingly a team effort with potentially hundreds of individuals working towards a common goal. Biologists hand data off to computational experts for deeper analysis; projects span departments, disciplines and even continents; and while collaboration unlocks new capabilities, it also introduces friction.

The metadata problem

When a data scientist joins a project, they may not have the full project background needed to understand what the data represents. What sample was tested with which compound, and at what concentration? How long was it incubated, and at what temperature? This kind of information is essential for accurately interpreting the results. But it’s often incomplete, undocumented, or saved in ways that only make sense to the person who collected it.

Without that context, even clean data can become meaningless. Fortunately, metadata offers a solution.

Metadata provides a structured, shareable layer that helps teams interpret experimental data with confidence. As more people are involved in a project, the more complex it gets, and without clear, consistent metadata, it becomes increasingly difficult to uncover meaningful results.

Tools built for one may not work for many

Scientists are already managing complex experiments under tight timelines. While their focus is on getting the science right, adding detailed documentation on top isn’t a matter of administrative resistance; it’s a matter of time.

If we want greater contextual accuracy, we need tools that make capturing it part of the natural flow of work.

That’s where most existing lab software falls short. Many electronic lab notebooks (ELNs) and lab information management systems (LIMS) were designed for processes where one person handled the whole workflow, but in a collaborative, data-driven environment, that’s not enough.

Modern labs supporting large teams need tools that help individuals capture context in the moment, without slowing them down. Systems that don’t just support collaboration but actively guide it. Platforms that prompt scientists to record key details, structure them in useful ways, and make that information accessible to everyone who needs it, now or down the line.

As science becomes more connected, metadata isn’t a nice-to-have — it’s the glue that holds together the team’s work. Without it, even the most powerful analysis tools won’t get teams very far, but with it, collaboration becomes a multiplier, and science moves faster, smarter and more reliably.

*As Chief Growth Officer for Strategic Partnerships at Sapio Sciences, Andrew Wyatt is responsible for growing the company’s international operations. With his deep understanding of the life sciences industry and proven ability to navigate the complexities of scaling operations, Andrew has consistently built organisations that delight customers while maximising shareholder value.

Top image credit: iStock.com/skynesher