Three Lessons from Accelerating Scientific Insight Discovery via Visual Querying

Doris Jung Lin Lee; Tarique Siddiqui; Karrie Karahalios; Aditya Parameswaran

doi:10.1016/j.patter.2020.100126

Three Lessons from Accelerating Scientific Insight Discovery via Visual Querying

Doris Jung Lin Lee
, Tarique Siddiqui
, Karrie Karahalios
, Aditya Parameswaran

Research output: Contribution to journal › Comment/debate › peer-review

Abstract

Exploratory data analysis is a crucial part of data-driven scientific discovery. Yet, the process of discovering insights from visualization can be a manual and painstaking process. This article discusses some of the lessons we learned from working with scientists in designing visual data exploration system, along with design considerations for future tools. Exploratory data analysis is a crucial part of data-driven scientific discovery. Yet, the process of discovering insights from visualization can be a manual and painstaking process. This article discusses some of the lessons we learned from working with scientists in designing visual data exploration system, along with design considerations for future tools.

Original language	English (US)
Article number	100126
Journal	Patterns
Volume	1
Issue number	7
DOIs	https://doi.org/10.1016/j.patter.2020.100126
State	Published - Oct 9 2020

ASJC Scopus subject areas

General Decision Sciences

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abstract = "Exploratory data analysis is a crucial part of data-driven scientific discovery. Yet, the process of discovering insights from visualization can be a manual and painstaking process. This article discusses some of the lessons we learned from working with scientists in designing visual data exploration system, along with design considerations for future tools. Exploratory data analysis is a crucial part of data-driven scientific discovery. Yet, the process of discovering insights from visualization can be a manual and painstaking process. This article discusses some of the lessons we learned from working with scientists in designing visual data exploration system, along with design considerations for future tools.",

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AB - Exploratory data analysis is a crucial part of data-driven scientific discovery. Yet, the process of discovering insights from visualization can be a manual and painstaking process. This article discusses some of the lessons we learned from working with scientists in designing visual data exploration system, along with design considerations for future tools. Exploratory data analysis is a crucial part of data-driven scientific discovery. Yet, the process of discovering insights from visualization can be a manual and painstaking process. This article discusses some of the lessons we learned from working with scientists in designing visual data exploration system, along with design considerations for future tools.

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Three Lessons from Accelerating Scientific Insight Discovery via Visual Querying

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