If you think Wikipedia can't be scientifically analyzed, look no further than Sudha Ram.
Ram - a professor of management information systems at the University of Arizona's Eller College of Management - has studied such topics as who contributes to Wikipedia, how contributions are made, and what kind of information is added. Actually, those three areas make up just a small portion of her research.
But her exploration isn't limited to computer science. Ram is putting her research to use at the iPlant Collaborative, a large interdisciplinary program meant to support a sizable community of scientists, teachers and students, as well as creating a cyber-infrastructure to help plant scientists work better. The idea is to use computational thinking - merging human brainpower with computer resources - to answer big questions and advance understanding of plant science.
For example, the collaborative is now tackling the "tree of life," trying to understand how half a million forms of plant life are related.
The iPlant project is funded with $50 million from the National Science Foundation. That's one of the biggest awards the foundation has made in the name of plant science, iPlant project director Stephen Goff said.
Faculty adviser Nirav Merchant said the project started in 2007 with the goal of supporting plant scientists. He said that with highly ranked biological-science and computer-science departments and a well-written proposal, the University of Arizona beat out schools such as Yale and Stanford universities for the award. But he noted that there was an emphasis on not making it simply a UA project.
"This project had to be for and by the community," he said. "It had to tell us what were the grand challenges in plant science."
The project now involves Cold Spring Harbor Laboratory on Long Island, N.Y.; the Texas Advanced Computing Center at the University of Texas-Austin; and other people and institutions throughout the world, Merchant said.
But merging computational thinking and plant biology is sometimes difficult.
Ram began her research after noticing that scientists weren't comfortable sharing information using collaborative online resources such as "wikis" - easily created and edited websites with interlinked pages.
"If you walk into a lab without support and training and say, 'Here's a wiki' and start using it, it's difficult to wrap their heads around," Merchant said.
The solution, he said, is taking tools that scientists already are fond of, such as Microsoft Excel, and incorporating them into the structure of wikis.
"If you can find a common ground, then production is massive," he said.
iPlant is also taking cues from popular social-networking media. Those involved have created a website called "My-Plant" for gathering data. The website revolves around a tree of life, with different branches representing different fields of study. Users can "friend" a section, called a "clade," to learn more about it, as well as share their own data. Users join discussion forums, upload files and collaborate.
But just like in her Wikipedia study, Ram is analyzing what's happening on My-Plant, too. She is collecting data to see what collaborations are developing.
The iPlant Collaborative is using these methods to answer what members call "grand challenge" questions - difficult questions in plant biology whose solutions are important.
The group is currently focused on two:
• "Assembling the tree of life for plant sciences."
"We have some understanding of how half a million known green plant species are related to each other, but their relationships are diverse," Merchant said. He said the need is understanding how these are related as one family, which requires analyzing up to 500,000 pieces of data on one of the largest supercomputers in the world.
• "Genotype to phenotype."
Merchant cites humans as an example.
"We're 99.9 percent identical, but we're different when we look at each other. It's the same for plants. Why is some corn blue and some yellow?"
The key to iPlant, said Goff, the project director, is that the intersection of plant biology and computers may speed up research.
"It's not doing the science itself," he said, but allowing for theories to bubble up and be tested more quickly.
"It's allowing discovery to accelerate," he said.
Victoria Blute is a UA journalism student and a NASA Space Grant intern. E-mail her at email@example.com