Course 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.
Scientists are facing global problems of a magnitude, complexity and interdisciplinary nature that progress is limited by a trained and agile workforce.
At present, there is a lack of formal training in the key cognitive and skill areas that would enable graduates to become key participants in e-science collaborations. The purpose is to teach methodologies, and provide application experience and skill-sets in an inter-disciplinary forum to students and interested participants.
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.
Scientists are facing global problems of a magnitude, complexity and interdisciplinary nature that progress is limited by a trained and agile workforce.
At present, there is a lack of formal training in the key cognitive and skill areas that would enable graduates to become key participants in e-science collaborations. The purpose is to teach methodologies, and provide application experience and skill-sets in an inter-disciplinary forum to students and interested participants.
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.
Professors: Deborah L. McGuinness
Course Goals
To fill the gaps that are currently present in the integrative nature of informatics for the translation of science into requirements for the underlying and largely syntactic e-infrastructure.