When microbiology is a data problem: Putting science together to make better pictures of yeast

Short: A Portuguese-based group is suggesting that winemakers could have more useful information about choosing a yeast strain if scientists did a better job of putting together data from different kinds of experiments.

Longer:

Scientific research generates a lot of different shapes and sizes of data. How does anyone make it work together?

Contemporary scientific research has a lot of big challenges, but here are three: funding, replicability, and integration. Funding is a great big gory topic for another day.

Replicability has seen a lot of attention in recent science news: scientists across disciplines have been reporting difficulty duplicating their colleagues’ results when they try to repeat the same experiments. This is worrisome. (Most) science is supposed to be about making observations about the world that remain the same independent of who is making the observations. Two careful people should be able to do the same experiment in two different places and obtain the same results. Well-trained scientists, however, are finding themselves unable to replicate the results described in scientific papers, and the community isn’t sure what to do about it.

Integration – how to fit together large amounts of lots of different kinds of data – looks like a separate kind of problem. Scientists (microbiologists, biochemists, systems biologists, geneticists, physicists…) study a thing – yeast, say – in many, many different ways. They generate data in many different shapes and sizes, using all manner of different kinds of instruments to make numbers that don’t just tidily line up with each other. But, at least in theory, all of those data are about the same thing – the same yeast – and so finding ways to integrate data from different kinds of experiments should massively improve our understanding of how yeast works as a whole.

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