Four Data Visualization Tools Applied to Life Sciences

Data visualization?is a crucial element of any life science work, and it is especially important in communicating the work coming out of labs. The ability to explore and mine complex datasets is essential for communicating meaningful conclusions. Life sciences researchers rely on a variety of data visualization tools to help them analyze and interpret their data, including graphs, charts, and other more complex digital representations like artificial neural networks (ANN) and machine learning (ML). In this blog post, we’ll discuss four common data visualization techniques.

Scatter Plots

One of the most commonly used?data visualization tools in life sciences labs?is the scatterplot. Scatter plots are simple graphical representations that display the relationship between two variables. They are particularly useful for visualizing correlations between variables, such as the relationship between microbial proliferation levels days after cleaning a manufacturing area. In a scatter plot, each data point represents a single measurement or observation, and the x and y axes represent the two variables being compared. Moreover, if a researcher finds patterns among the data points—such as clustering—they can perform further analysis to parse the differences. This technique is called classification and is significant in more predictive computational methods like ML.