A Data Quality Screening Service for Remote Sensing Data - Annual Review 2012

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Concepts:Provenance & eScience

Abstract:

2012 Annual Review of the DQSS Project.

Contributions from R. Strub, T. Hearty, Y-I Won, M. Hegde, V. Jayanti, S. Zednik, P. West, N. Most, S. Ahmad, C. Praderas, K. Horrocks, I. Tcherednitchenko, and A. Rezaiyan-Nojani

History

DateCreated ByLink
March 11, 2012
17:18:57
Patrick WestDownload

Related Projects:

DQSSData Quality Screening Service (DQSS)
Principal Investigator: Christopher Lynnes
Co Investigator: Peter Fox, Ed Olsen, Shahin Samadi, Bruce Vollmer, and Robert Wolfe
Description: Objective Make data quality information easy to use for the water cycle community Expert and non-specialist users alike Human and machine users alike Connect users seamlessly to best practices in data quality handling, i.e., the science team recommendations for quality screening (filtering) Enable higher and more correct utilization of data quality indicators in data analysis

Related Research Areas:

Knowledge Provenance
Lead Professor: Deborah L. McGuinness
Description: Knowledge Provenance
Concepts: Provenance,
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