Semantic Specification of Data Type Information in the Deep Carbon Observatory Data Portal

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Presented at the AGU Fall meeting 2015

Abstract:

The global open data initiative is incubating a social-technical system that promotes both the culture and the practice of open data. Within such a system, data are going to be shared and reused across various boundaries, both physically and conceptually. Among the various metadata elements available for describing the shared data, data type information can provide clues for both users and machines on how to parse and use the data. Data types are often treated only as syntax of variables, such as integer, float, Boolean, character, and string, etc. Such declaration does not offer any domain specific meaning to the data types. Our intention is to let a data type include more meanings, such as who create the data type, the source standard that the data type derives from, the operations that can be done on datasets of that data type, and typical scientific domains, software programs and/or instruments that use the data type. We deployed semantic technologies to address this issue and initial results have already been achieved in the Deep Carbon Observatory Data Portal.

History

DateCreated ByLink
February 27, 2016
16:35:59
Patrick WestDownload
February 27, 2016
12:57:36
Patrick WestDownload
December 9, 2015
14:52:45
Xiaogang MaDownload
December 9, 2015
14:35:01
Xiaogang MaDownload

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.
TW LogoResearch Data Alliance Adoption Initiatives (RDA Adoption)
Principal Investigator: Peter Fox
Co Investigator: Xiaogang Ma
Description: The Research Data Alliance (RDA) - Data Type Registry (DTR) Working Group addresses a part of a core problem relevant to interoperability among data management systems: the ability to parse, understand, and potentially reuse data retrieved from others. The RDA - Persistent Identifier Information Types (PIT) Working Group addresses the essential types of information associated with persistent identifiers. We have undertaken an effort to adopt the DTR and PIT outputs in the Data Portal of the Deep Carbon Observatory (DCO) and have received positive results.

Related Research Areas:

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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.

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
Knowledge Provenance
Lead Professor: Deborah L. McGuinness
Description: Knowledge Provenance
Concepts: Provenance, Semantic Web
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
Web Science
Lead Professor: Jim Hendler, Deborah L. McGuinness
Description: Web Science is the study of the World Wide Web and its impact on both society and technology, positioning the Web as an object of scientific study unto itself. Web Science recognizes the Web as a transformational, disruptive technology; its practitioners study the Web, its components, facets and characteristics. Ultimately, Web Science is about understanding the Web and anticipating how it might evolve in the future.
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