I’ve spent a lot of time recently thinking about how we can better teach science using data. I believe that 21st Century science is increasingly data-intensive, and that in order to teach science as it is actually being practiced, it should be possible to identify datasets and data stories relevant to most, if not all, topics in modern science to use in the classroom.
So, being a biologist, I began by looking into the world of modern biology – seeking areas where new frontiers are being forged through the collection, visualization, and analysis of data. I didn’t get very far down this path before I realized that the stories found at the intersection of biology and data also involve computers and machine learning, 3-D printing and modeling, and lots of other technologies, some of which felt like they come right out of science fiction.
For example, I started looking more closely at molecular genetics for potential datasets and data stories – knowing that this is an especially mathematical and data-intensive field. But I hadn’t thought about the fact that modern molecular genetics is possible only because of robotics. Automated gene sequencing machines, replicating millions of copies of specific, target strands of DNA so their stored genetic “code” can be read, have enabled us to compare similar genes from organisms across the tree of life. These data tell us not only how all living things are related, but also help us to understand how these most fundamental building blocks of life yield the amazing diversity of life that exists on our planet. This understanding – brought about through brute-force computer analysis of massive datasets—is yielding not only a new, deeper understanding of how life on Earth has come to be as it is, but is also offering new tools for diagnosing and treating disease, from flu to cancer.
This fascinating integration between digital technologies and science is also at play in transplant medicine. Data collected through various radiology techniques (such as CAT scans and MRI imaging) are being used to create templates for 3-D printers, which in turn are used to create living scaffolds on which a patient’s own cells can be grown to create replacement parts (like an esophagus or even a heart) in cases where that part was damaged by injury or infection.
And sophisticated computer models, supplemented by machine learning, are helping ecologists to better understand the subtle dynamics at play in the Earth’s ecosystems, using data to provide a more accurate picture of the subtle relationships among organisms and their surroundings. These models can then be used to better predict how living systems will respond to change, improving our ability to manage natural resources, and to anticipate and plan for changing climates and rising sea levels in the future.
These examples all emphasize the point that technology and data are already fundamental to modern science. Students always like to ask, “How is this relevant to me?” or, “Why should I care?” As technology enables us to peer deeper into the “inner workings” of the world around us, we will increasingly be faced with ethical questions arising from the distinction between what we can do and what we should do. We need to arm the citizens of tomorrow with the critical thinking skills to make informed choices, for themselves and the generations to come.