National Science Foundation Awards Biocomplexity Institute $4 Million Collaborative Grant for Network Science Cyberinfrastructure (CINES)
Over the last 20 years, network science has evolved from a niche domain to one with widespread recognition, relevance, and application throughout our culture. “Network” is now a common term in our everyday lexicon. Companies with technology we use daily like Google, Facebook, and Twitter were built on concepts from network science. Its application ranges from epidemiology to politics, and the field has become standard in most university curricula. Despite its widespread application and universal recognition, there are no centralized, go-to network science resources where researchers, educators, scientists, professionals, and anyone doing network science can access data and computing materials. But, that will soon change.
The University of Virginia’s Biocomplexity Institute was recently awarded a five-year $4 million collaborative grant from the U.S. National Science Foundation (NSF) to build a self-sustaining cyberinfrastructure (CINES – pronounced “science”) to serve as an open-source, web-based repository for developing, trading, analyzing, and sharing network science resources. Given the multi-disciplinary nature of the field, CINES will provide a collaborative space for users, ranging from novice to expert, in various disciplines to access and analyze network science data.
“What we are building is an extensive network science data repository – we think of it as a data library for networks,” said Madhav Marathe, director of the Biocomplexity Institute’s Network Systems Science and Advanced Computing division and CINES project principal investigator. “We will collect data from all available networks, synthesize it from various sources, and make it available to researchers and educators throughout the world. This will be an online lab-like environment where people can use existing data to analyze networks, access existing code that can be applied in a different way, and share data and tools across an environment that is self-sustaining and ever-improving.”
The NSF grant funding will span through 2024 and enable the Institute’s research team and its partners to expand upon the work it has already done building the network science cyberinfrastructure archetype, CINET, that was released in 2012. CINET introduced the concept of supporting network analysis as a service, allowing experts in various disciplines to focus on what they do best instead of programming details and allocation of computational resources for analysis.
“The idea for CINET originated with the Biocomplexity Institute 13 or 14 years ago,” Marathe said. “It came about organically as we started building networks and writing pieces of code for the research we were doing. Over a few years, even within our own group, people wanted to use what had already been created, so we saw the need to start organizing a type of infrastructure to share resources. That need was reinforced by colleagues in other roles related to network science, so we knew this was something that could have widespread impact in the field. ”
Over the next five years, galvanized by the NSF grant funding, the team plans to release a public version of CINES with periodic updates that enable it to continue to grow and evolve. According to Christopher Kuhlman, researcher in the Network Systems Science and Advanced Computing division and CINES co-principal investigator, the Institute’s transdisciplinary team science approach provides a distinct advantage in developing the cyberinfrastructure.
“We’re in a unique position because we work across several application domains and have a reasonable understanding of what the problems are,” Kuhlman said. “From a scientific perspective, there are people in the group who develop software, algorithms, theory, have computational abilities, and those who understand applications – all of these things come to bear on developing a cyberinfrastructure for network science. We’re bringing it all together in an end-to-end network science system people can utilize for a variety of purposes.”
“Our value proposition is to develop a software infrastructure that is agnostic to the application,” he said. “We hope that people from various areas will be able to go to one place – a one-stop shop – and have a wide range of tools available for them to use so they can focus on their strengths.”
CINES most important and distinguishing feature is its open-source, user-contributed nature. The question, however, is why would someone want to contribute their own novel codes, algorithms, or other techniques to the system? According to S.S. Ravi, senior collaborator on the CINES project and researcher in the Institute’s Network Systems Science and Advanced Computing division, the infrastructure’s strong attribution system is a notable draw.
“When people contribute to CINES, they are given 100 percent credit,” Ravi said. “People will know where it was sourced and how many times it has been used, providing contributors with additional exposure and recognition for their work. Also, because CINES is a community resource, others can see, use, improve, and build upon the work, making it a no-lose situation to contribute.”
The Biocomplexity Institute is proud to collaborate with a distinguished list of partners from the academic, scientific, and corporate sectors on the CINES project, including: Indiana University, Jackson State University, North Carolina A&T State University, Stanford University, Virginia Tech, Los Alamos National Laboratory, Kitware, Inc., and Network Repository. The team is working toward its collective vision for CINES to bring about fundamental changes in the way researchers study and teach complex networks. The five-year $4 million grant from NSF will enable progress toward this vision, and building an infrastructure that is scalable and self-sustaining for years to come.
“We are committed to producing an infrastructure that will help us unify our approach to finding and utilizing network science data across the domain, and we’re grateful to NSF for helping to make this possible,” Marathe said. “Our goal is to make doing network science more efficient, so that ultimately, we can enable greater advancements in the field.”