Human-Aware Sensor Network Ontology: Semantic Support for Empirical Data Collection

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Abstract:

History

DateCreated ByLink
September 3, 2015
17:23:39
Henrique SantosDownload

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 Frameworks
Lead Professor: Peter Fox
Description: None.
Concepts:
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.

Concepts: