DCO-VIVO: A Collaborative Data Platform for the Deep Carbon Science Communities

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Presented at the AGU Fall Meeting 2014


Deep Carbon Observatory (DCO) is a decade-long scientific endeavor to understand carbon in the complex deep Earth system. Thousands of DCO scientists from institutions across the globe are organized into communities representing four domains of exploration: Extreme Physics and Chemistry, Reservoirs and Fluxes, Deep Energy, and Deep Life. Cross-community and cross-disciplinary collaboration is one of the most distinctive features in DCO's flexible research framework.

VIVO is an open-source Semantic Web platform that facilitates cross-institutional researcher and research discovery. it includes a number of standard ontologies that interconnect people, organizations, publications, activities, locations, and other entities of research interest to enable browsing, searching, visualizing, and generating Linked Open (research) Data.

The DCO-VIVO solution expedites research collaboration between DCO scientists and communities. Based on DCO's specific requirements, the DCO Data Science team developed a series of extensions to the VIVO platform including extending the VIVO information model, extended query over the semantic information within VIVO, integration with other open source collaborative environments and data management systems, using single sign-on, assigning of unique Handles to DCO objects, and publication and dataset ingesting extensions using existing publication systems. We present here the iterative development of these requirements that are now in daily use by the DCO community of scientists for research reporting, information sharing, and resource discovery in support of research activities and program management.


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December 13, 2014
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December 12, 2014
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December 12, 2014
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December 11, 2014
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December 10, 2014
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December 7, 2014
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Related Projects:

DCO-DS LogoDeep Carbon Observatory Data Science (DCO-DS)
Principal Investigator: Peter Fox
Co Investigator: John S. Erickson and Jim Hendler
Description: Given this increasing data deluge, it is clear that each of the Directorates in the Deep Carbon Observatory face diverse data science and data management needs to fulfill both their decadal strategic objectives and their day-to-day tasks. This project will assess in detail the data science and data management needs for each DCO directorate and for the DCO as a whole, using a combination of informatics methods; use case development, requirements analysis, inventories and interviews.

Related Research Areas:

Data Frameworks
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Description: None.
Concepts: eScience
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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.

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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.

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