Big scientific projects amass a lot of data and to make sense of what's been collected, interdisciplinary teams need to be assembled, writes Thomas Lin at Quanta magazine, which is a publication of the Simons Foundation.
"And, as daunting as the looming data crush appears from a technical perspective, some of the greatest challenges are wholly nontechnical," Lin adds. "Many researchers say the big science projects and analytical tools of the future can succeed only with the right mix of science, statistics, computer science, pure mathematics, and deft leadership."
He focuses on, as an example, a large-scale ecology project that seeks to understand how climate change, biodiversity, and land use affect ecosystems and the biosphere. This project, dubbed NEON for National Ecological Observatory Network, brings together dozens of scientists, a hundred research sites, and some 600 billion raw measurements that have to be made each year for 30 years that then have to be analyzed, interpreted, and made publicly available.
Steve Berukoff, the assistant director for data products for NEON, tells Lin that none of the 66 NEON staff do the same thing and come from a variety of academic disciplines. This, Berukoff, adds, means that the researchers have to work to understand one another. "People often think they're talking about the same thing when they're not," Berukoff says. "Or they're talking about the same thing and they're talking about it in two different ways."
Further, while researchers can learn from one the, the differences in their training "can also be frustrating because of this impedance mismatch between what is being said and heard," Berukoff tells Lin. "Bridging that gap is central to the success of a project."