Why we need a semantic web framework for marine ecosystem indicators

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Presented at the Ocean Sciences 2014

Abstract:

Ecosystem-based management of Large Marine Ecosystems (LMEs) involves the sharing of data and information products among a diverse set of stakeholders - from environmental and fisheries scientists to policy makers and the public. Tracing a synthesized data product back to its original data sources is important for verification, understanding, and comparing with similar data products or indicators monitored by different stakeholder groups. Our collaborative use case develops a software framework for the bi-annual Ecosystem Status Report (ESR) for the U.S. Northeast Shelf LME. The ESR provides data and information products for ecosystem drivers and pressures, such as climate forcing and fisheries indicators, and ecosystem status including primary production. Here we present the use of the W3C provenance (PROV) Linked Data standard for data products in the ESR. Furthermore we recommend linking to domain-specific ontologies to give meaning to the source datasets and derived products. Semantically enabling not only the provenance but also the data products will yield a better understanding of the connected web of relationships between marine ecosystem and ocean health assessments conducted by different stakeholder groups.

History

DateCreated ByLink
March 4, 2014
00:31:36
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
Semantic eScience
Lead Professor: Peter Fox
Description:
Science has fully entered a new mode of operation. E-science, defined as a combination of science, informatics, computer science, cyberinfrastructure and information technology is changing the way all of these disciplines do both their individual and collaborative work.
As semantic technologies have been gaining momentum in various e-Science areas (for example, W3C's new interest group for semantic web health care and life science), it is important to offer semantic-based methodologies, tools, middleware to facilitate scientific knowledge modeling, logical-based hypothesis checking, semantic data integration and application composition, integrated knowledge discovery and data analyzing for different e-Science applications.
Partially influenced by the Artificial Intelligence community, the Semantic Web researchers have largely focused on formal aspects of semantic representation languages or general-purpose semantic application development, with inadequate consideration of requirements from specific science areas. On the other hand, general science researchers are growing ever more dependent on the web, but they have no coherent agenda for exploring the emerging trends on the semantic web technologies. It urgently requires the development of a multi-disciplinary field to foster the growth and development of e-Science applications based on the semantic technologies and related knowledge-based approaches.

Concepts: eScience