Publishing and Visualizing Large-Scale Semantically-enabled Earth Science Resources on the Web

Printer-friendly version

Presented at the AGU Fall Meeting 2014


The volume and variety of data generated in science is rapidly increasing. Geophysical science is no exception in that various independent projects produce disparate, heterogeneous datasets. While researchers typically make this data available to others, there is a need to make these valuable resources more discoverable and understandable to user communities in order to accelerate scientific research. The cost of making data discoverable and understandable depends on how the original data was curated, transformed, generated, and published. User interfaces and visualizations that support exploration and interaction with the data further enhance understanding of available content.

This presentation describes research and development conducted under the Resource Discovery for Extreme Scale Collaboration (RDESC) project. As part of RDESC we curate, clean, publish, and visualize scientific data following Linked Data principles. Towards enabling discovery and understandability, we curated data from multiple, interdisciplinary science domains and represented the metadata using standard Semantic Web and Web technologies. As a result of this transformation, we generated some 1.4 billion RDF triples that describe these previously existing data resources. These efforts led to our formulation of a number of suggested best practices for data publishers to reduce the cost and barriers to making data discoverable and understandable to research communities. Additionally, we developed a set of tools that provide scalable visualizations of this large-scale metadata to enhance the understandability for prospective users of the data resources.


DateCreated ByLink
December 13, 2014
Patrick WestDownload
December 12, 2014
Patrick WestDownload
December 12, 2014
Patrick WestDownload
December 7, 2014
Patrick WestDownload

Related Projects:

Resource Discovery for Extreme Scale Collaboration (RDESC)
Principal Investigator: Eric Stephan, Jesse Weaver, and Karen Schuchardt
Co Investigator: Alan Chappell and Peter Fox
Description: Our objective is to develop a capability for describing, linking, searching and discovering resources used in collaborative science that is lightweight enough to be used as a component in any software system such as desktop user environments or dashboards but also scalable to millions of resources. A key design goal is to offer local control over resource descriptions thus reducing one of the bottlenecks to widespread adoption. We propose to build a prototype framework and associated services, the Resource Discovery for Extreme Scale Collaboration (RDESC), that meet these objectives.

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
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
Lead Professor: Peter Fox
Description: In the last 2-3 years, Informatics has attained greater visibility across a broad range of disciplines, especially in light of great successes in bio- and biomedical-informatics and significant challenges in the explosion of data and information resources. Xinformatics is intended to provide both the common informatics knowledge as well as how it is implemented in specific disciplines, e.g. X=astro, geo, chem, etc. Informatics' theoretical basis arises from information science, cognitive science, social science, library science as well as computer science. As such, it aggregates these studies and adds both the practice of information processing, and the engineering of information systems.
Concepts: Semantic Web, eScience