Reducing the Analytical Bottleneck for Domain Scientists: Lessons from a Climate Data Visualization Case Study

Aritra Dasgupta, Jorge Poco, Enrico Bertini, Claudio T. Silva

Research output: Contribution to journalArticle

Abstract

The gap between large-scale data production rate and the rate of generation of data-driven scientific insights has led to an analytical bottleneck in scientific domains like climate, biology, and so on. This is primarily due to the lack of innovative analytical tools that can help scientists efficiently analyze and explore alternative hypotheses about the data and communicate their findings effectively to a broad audience. In this article, by reflecting on a set of successful collaborative research efforts between with a group of climate scientists and visualization researchers, the authors introspect how interactive visualization can help reduce the analytical bottleneck for domain scientists.

Original languageEnglish (US)
Article number7361667
Pages (from-to)92-100
Number of pages9
JournalComputing in Science and Engineering
Volume18
Issue number1
DOIs
StatePublished - Jan 1 2016

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Data visualization
Visualization

Keywords

  • Analytical models
  • big data
  • Biological system modeling
  • Computational modeling
  • Data models
  • Data visualization
  • Data visualization
  • Meteorology
  • scientific computing
  • simulation
  • Visualization

ASJC Scopus subject areas

  • Computer Science(all)
  • Engineering(all)

Cite this

Reducing the Analytical Bottleneck for Domain Scientists : Lessons from a Climate Data Visualization Case Study. / Dasgupta, Aritra; Poco, Jorge; Bertini, Enrico; Silva, Claudio T.

In: Computing in Science and Engineering, Vol. 18, No. 1, 7361667, 01.01.2016, p. 92-100.

Research output: Contribution to journalArticle

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