Before you start your next Google search, stop for just a minute. You may not know it, but whether you’re looking for the latest Hokie football score or cheap airline tickets, you’re about to unleash a powerful data discovery, retrieval, and organizing process made possible by the agreed-upon rules for defining information that drive search engines.
Now pause again and imagine if every website used a different set of rules and search engines weren’t available. Given the mind-boggling amount of information on the internet that is at our fingertips, how would you ever find what you need to make decisions and plan your life? Take that query up a couple notches for scientists navigating a plethora of environmental data scattered across the web, and you’ll understand the impact of a new Virginia Tech research project.
Quinn Thomas, who holds dual appointments in the College of Natural Resources and Environment and the College of Science, is the principal investigator for Virginia Tech’s part of a $3.2 million research project funded by the National Science Foundation’s (NSF) Office of Advanced Cyberinfrastructure.
The Democratized Cyberinfrastructure for Open Discovery to Enable Research (DeCODER) aims to standardize and facilitate how environmental data and model predictions are described and shared so that, ultimately, more individual researchers and scientific communities can utilize these resources.
Data is the key driver for the project and for the ecological forecasting research of Thomas, an associate professor in the Department of Forest Resource and Environmental Conservation, an affiliated faculty member of the Global Change Center, and the Data Science Faculty Fellow in the College of Science. He is a researcher with a bold objective: predicting the natural environment just like we predict the weather through the use of shared data tools and a computational infrastructure.
As the project lead for Virginia Tech, Thomas will put the university’s $535,000 share of the NSF grant to work to aid researchers interested in predicting environmental change. “My portion of the project is to advance the discoverability of ecological forecasts through the development of protocols and software to archive and document model predictions of ecological dynamics,” Thomas said. “Much like we use internet search engines (like Google) to find information, our work will help a researcher ask questions and initiate searches like ‘Find forecasts of algae in lakes across the U.S.’ in order to find current forecasts to help guide decision making and support environmental management.”
This grant advances work already done on the EarthCube GeoCODES platform. EarthCube is an NSF-funded environment that improves access, sharing, and visualization of data. GeoCODES is a program specifically for researchers working in the field of geoscience that offers evolving methods for organizing data so it can be easily accessed, as well as a framework for new computational tools, a registry, and best practices for the user community.
The new DeCODER platform will build upon and leverage the work that has already been done as part of the EarthCube effort. Thomas will take the next steps to help researchers working specifically in ecological forecasting to more easily access data and create better models.
Again, considering the example of researchers needing to forecast algae growth across the U.S., the DeCODER platform will allow researchers to not only gather data and forecasts, but also to “then compare these predictions to actual measurements of algae to quantify the strengths and weakness of the forecasts that have been generated to date.”
In addition, Thomas said, “Rather than requiring all ecological forecasters to use a single archive location on the internet, the technology we are developing allows for many archiving locations to be used, thus democratizing the storage and discovery of the results of the forecasting expertise.”
This new platform is especially valuable to researchers, like Thomas, whose work involves utilizing data and modeling because it will allow them to more easily discover what has already been done in the field in order to improve models over time.
“Think about weather forecasts,” said Thomas. “They have been getting better over time. A 10-day forecast is as good as an eight- or nine-day forecast was a decade ago. We know this improvement has occurred by comparing past forecasts to data. Now we want to do this evaluation of other environmental forecasts, and we can’t do that if we can’t find all the historical data.”
He also said individual scientists are producing incredible amounts of data about the environment, but, unfortunately, it’s not all in one centralized place. This new technology will allow data to be discovered wherever it is and enable researchers to determine if they are getting better at forecasting environmental change.
Thomas will be working closely with Associate Professor Carl Boettiger of the University of California at Berkeley (UCB) on the application of the DeCODER platform to ecological forecasting. A primary focus will be developing the software and protocols that will allow people to discover needed data. “The DeCODER project will democratize research pipelines such as the production and assessment of ecological forecasts, helping to bridge scientific communities and better inform decision makers,” Boettiger said.
To meet this ambitious goal, the project involves a collaborative research effort between several teams with specific areas of expertise. In addition to the Virginia Tech and UCB focus on ecological forecasting data, the University of Illinois Urbana-Champaign (the lead institution) and the University of California San Diego (UCSD) are developing the cyberinfrastructure used by all the teams to tie the work together. Syracuse University and Texas A&M University are working on low-temperature geochemistry data, and the Scripps Institution of Oceanography, along with UCSD, is focusing on deep ocean science data.
According to Thomas, this newly-funded NSF grant project will both complement and advance the ongoing research agenda in ecological forecasting at Virginia Tech. The DeCODER platform will ultimately accelerate work on current forecasting projects related to water quality, forest carbon storage, fall colors, and environmental dynamics in the context of a changing environment.
“By focusing on a democratized approach to data and forecast discovery, the advances are designed to outlive the duration of the project. This places Virginia Tech’s ecological forecasting research at the vanguard of the field,” said Thomas.
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