ToolMatch Service: Finding Tools for Your Data And Data for Your Tools

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Presented at the ESIP Summer Meeting 2014

Abstract:

This breakout session will briefly describe the initial use cases, conceptual model, and assumptions underlying the ToolMatch service being built by Semantic Web Cluster participants in collaboration with the ESIP Energy & Climate Cluster, and the ESIP Products & Services Committee using Semantic Web technologies. The initial phase of the ToolMatch service will be demonstrated from both the tool developer and the data collection user perspectives. Following the demonstration, workshop participants will be asked to input specific information about visualization tools appropriate for Earth Science-focused data collections, and / or specific information about data collections appropriate for the use of Earth Science-appropriate visualization tools. Subsequent successes and failures of the service will be discussed among the ToolMatch development team and workshop participants for purposes of refining the use cases, clarifying assumptions and improving the ToolMatch service.

History

DateCreated ByLink
August 17, 2014
23:21:47
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