Discovering accessibility, display, and manipulation of data in a data portal

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Presented at the AGU Fall Meeting 2013

Abstract:

The accessibility of science data products is becoming increasingly easier, with more and more data and scientific community portals coming online all the time. But what can one do with the data product once it has been found? Can I visualize the data product as a map, plot, or graph? Can I import the data into a particular data manipulation tool like MatLab or IDL or iPython Notebook? How is the dataset accessible, and what kind of data products can be generated from it? ToolMatch is a crowd source approach (ontological model, information model, RDF Schema) that allows data and tool providers, and portal developers to enable user discovery of what can be done with a science data product, or conversely, which science data products are usable within a given tool.

Example queries may include "I need data for Carbon dioxide (CO2) concentrations, a climate change indicator, for the summer of 2012, that can be accessed via OPeNDAP Hyrax and plotted as a timeseries.", or "I need data with measurements of atmospheric aerosol optical depth sliced along latitude and longitude, returned as netcdf data, and accessible in MatLab."

This contribution outlines the progress of the ToolMatch development, plans for utilizing its capabilities, and efforts to leverage and enhance the use of ToolMatch in various portals.

History

DateCreated ByLink
December 9, 2013
15:39:21
Patrick WestDownload
November 25, 2013
20:04:19
Patrick WestDownload
November 25, 2013
19:57:38
Patrick WestDownload

Related Projects:

TW LogoToolMatch (ToolMatch)
Description: or a given dataset, it is difficult to find the tools that can be used to work with the dataset. In many cases, the information that Tool A works with Dataset B is somewhere on the Web, but not in a readily identifiable or discoverable form. In other cases, particularly more generalized tools, the information does not exist at all, until somebody tries to use the tool on a given dataset. Thus, the simplest, most prevalent use case is for a user to search for the tools that can be used with a given dataset. A further refinement would be to specify what the tool can do with the dataset, e.g., read, visualize, map, analyze, reformat.

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
X-informatics
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: , eScience