Field: a new meta-authoring platform for data-intensive scientific visualization

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Presented at the AGU Fall Meeting 2012

Abstract:

We demonstrate a new platform for data-intensive scientific visualization, called Field, that rethinks the problem of visual data exploration. Several new opportunities for scientific visualization present themselves here at this moment in time.

We believe that when taken together they may catalyze a transformation of the practice of science and to begin to seed a technical culture within science that fuses data analysis, programming and myriad visual strategies.

Our premise is that it is at integrative levels that the principle challenges exist, for many fundamental technical components of our field are now well understood and widely available. File formats from CSV through HDF all have broad library support; low-level high-performance graphics APIs (OpenGL) are in a period of stable growth; and a dizzying ecosystem of analysis and machine learning libraries abound.

The hardware of computer graphics offers unprecedented computing power within commodity components; programming languages and platforms are coalescing around a core set of umbrella runtimes.

Each of these trends are each set to continue - computer graphics hardware is developing at a super-Moore-law rate, and trends in publication and dissemination point only towards an increasing amount of access to code and data.

History

DateCreated ByLink
December 6, 2012
18:19:37
Peter FoxDownload

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: