IN21B-3712 Semantic eScience for Ecosystem Understanding and Monitoring: The Jefferson Project Case Study

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Monitoring and understanding ecosystems such as lakes and their watersheds is becoming increasingly important. Accelerated eutrophication threatens our drinking water sources. Many believe that the use of nutrients (e.g., road salts, fertilizers, etc.) near these sources may have negative impacts on animal and plant populations and water quality although it is unclear how to best balance broad community needs. The Jefferson Project is a joint effort between RPI, IBM and the Fund for Lake George aimed at creating an instrumented water ecosystem along with an appropriate cyberinfrastructure that can serve as a global model for ecosystem monitoring, exploration, understanding, and prediction. One goal is to help communities understand the potential impacts of actions such as road salting strategies so that they can make appropriate informed recommendations that serve broad community needs. Our semantic eScience team is creating a semantic infrastructure to support data integration and analysis to help trained scientists as well as the general public to better understand the lake today, and explore potential future scenarios. We are leveraging our RPI Tetherless World Semantic Web methodology that provides an agile process for describing use cases, identification of appropriate background ontologies and technologies, implementation, and evaluation. IBM is adding its Smart Technology to commercially available sensor network infrastructure along with tools to share, maintain, analyze and visualize observation data. In the context of this sensor infrastructure, we will discuss our semantic approach's contributions in three knowledge representation and reasoning areas: (a) human interventions on the deployment and maintenance of local sensor networks including the scientific knowledge to decide how and where sensors are deployed; (b) integration, interpretation and management of data coming from external sources used to complement the project’s models; and (c) knowledge about simulation results including parameters, interpretation of results, and comparison of results against external data. We will also demonstrate some example queries highlighting the benefits of our semantic approach and will also identify reusable components.

Related Projects:

Jefferson Project at Lake George Project LogoE-Science Jefferson Project on Lake George (Jefferson Project)
Principal Investigator: Deborah L. McGuinness
Co Investigator: Paulo Pinheiro
Description: The Jefferson Project at Lake George is building one of the world’s most sophisticated environmental monitoring and prediction systems, which will provide scientists and the community with a real-time picture of the health of the lake. Launched in June 2013, the project aims to understand and manage multiple complex factors—including road salt incursion, storm water runoff, and invasive species—all threatening one of the world’s most pristine natural ecosystems and an economic cornerstone of the New York tourism industry. The project is a three-year, multimillion-dollar collaboration between Rensselaer Polytechnic Institute, IBM, and The FUND for Lake George. The collaboration partners expect that the world-class scientific and technology facility at the Rensselaer Darrin Fresh Water Institute at Lake George will create a new model for predictive preservation and remediation of critical natural systems in Lake George, in New York, and ultimately around the world.

Related Research Areas:

Data Science
Lead Professor: Peter Fox
Description: Science has fully entered a new mode of operation. Data science is advancing inductive conduct of science driven by the greater volumes, complexity and heterogeneity of data being made available over the Internet. Data science combines of aspects of data management, library science, computer science, and physical science using supporting cyberinfrastructure and information technology. As such it is changing the way all of these disciplines do both their individual and collaborative work.

Data science is helping scienists face new global problems of a magnitude, complexity and interdisciplinary nature whose progress is presently limited by lack of available tools and a fully trained and agile workforce.

At present, there is a lack formal training in the key cognitive and skill areas that would enable graduates to become key participants in escience collaborations. The need is to teach key methodologies in application areas based on real research experience and build a skill-set.

At the heart of this new way of doing science, especially experimental and observational science but also increasingly computational science, is the generation of data.