Information Models for Development and Evolution of Complex Multi-Scale Knowledge Networks for Marine Ecosystems

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Presented at the Ocean Sciences 2012

Abstract:

Beginning with an NSF INTEROP project whose goal is to facilitate the deployment of an Integrated Ecosystem Approach (IEA) to management in the Northeast and California Current Large Marine Ecosystems (LMEs), the opportunity for a network of LMEs spanning space, time and stakeholder scales is becoming a reality.

These networks define specific components of interest to support the implementation of NOAA's Driver-Pressure-State-Impact Response decision framework and the cyberinfrastructure technologies to ensure data interoperability and reuse.

Until now, what was lacking was a process to bring together existing knowledge networks to identify, review, and synthesize the best assessment and management practices among the community of LME practitioners facilitating exchange of lessons learned.

The scope of the network includes key stakeholders in four areas: scientists and data providers, agencies, national communities of practice, and decision makers/ policy developers.

Key to developing multi-scales network is semantically rich use case and information model development using expertise in semantic web methodologies, especially related to diverse vocabulary needs across the stakeholder areas.

History

DateCreated ByLink
April 13, 2012
14:43:11
Patrick WestDownload
April 13, 2012
14:38:00
Patrick WestDownload

Related Projects:

ECOOP LogoEmploying Cyber Infrastructure Data Technologies to Facilitate IEA for Climate Impacts in NE & CA LME's (ECO-OP)
Principal Investigator: Peter Fox
Co Investigator: Andrew Maffei
Description: The purpose of this INTEROP proposal is to facilitate the deployment of an Integrated Ecosystem Approach (IEA) to management in the Northeast and California Current Large Marine Ecosystems (LMEs). The direct result of the proposed activity will be application-level data and information enhanced communication for developing the consensus networks to define the specific components of interest to support the implementation of NOAA’s Driver-Pressure-State-Impact Response framework (DPSIR) decision framework and the cyberinfrastructure technologies to ensure data interoperability and reuse.

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.

Concepts: eScience