Three Lessons from Accelerating Scientific Insight Discovery via Visual Querying
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 exploratio...
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Veröffentlicht in: | Patterns (New York, N.Y.) N.Y.), 2020-10, Vol.1 (7), p.100126-100126, Article 100126 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | 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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ISSN: | 2666-3899 2666-3899 |
DOI: | 10.1016/j.patter.2020.100126 |