Literature DB >> 26357151

Design Study Methodology: Reflections from the Trenches and the Stacks.

M Sedlmair1, M Meyer, T Munzner.   

Abstract

Design studies are an increasingly popular form of problem-driven visualization research, yet there is little guidance available about how to do them effectively. In this paper we reflect on our combined experience of conducting twenty-one design studies, as well as reading and reviewing many more, and on an extensive literature review of other field work methods and methodologies. Based on this foundation we provide definitions, propose a methodological framework, and provide practical guidance for conducting design studies. We define a design study as a project in which visualization researchers analyze a specific real-world problem faced by domain experts, design a visualization system that supports solving this problem, validate the design, and reflect about lessons learned in order to refine visualization design guidelines. We characterize two axes - a task clarity axis from fuzzy to crisp and an information location axis from the domain expert's head to the computer - and use these axes to reason about design study contributions, their suitability, and uniqueness from other approaches. The proposed methodological framework consists of 9 stages: learn, winnow, cast, discover, design, implement, deploy, reflect, and write. For each stage we provide practical guidance and outline potential pitfalls. We also conducted an extensive literature survey of related methodological approaches that involve a significant amount of qualitative field work, and compare design study methodology to that of ethnography, grounded theory, and action research.

Year:  2012        PMID: 26357151     DOI: 10.1109/TVCG.2012.213

Source DB:  PubMed          Journal:  IEEE Trans Vis Comput Graph        ISSN: 1077-2626            Impact factor:   4.579


  23 in total

1.  Decision-Centered Design of Patient Information Visualizations to Support Chronic Pain Care.

Authors:  Christopher A Harle; Julie DiIulio; Sarah M Downs; Elizabeth C Danielson; Shilo Anders; Robert L Cook; Robert W Hurley; Burke W Mamlin; Laura G Militello
Journal:  Appl Clin Inform       Date:  2019-09-25       Impact factor: 2.342

2.  Lineage: Visualizing Multivariate Clinical Data in Genealogy Graphs.

Authors:  Carolina Nobre; Nils Gehlenborg; Hilary Coon; Alexander Lex
Journal:  IEEE Trans Vis Comput Graph       Date:  2018-03-06       Impact factor: 4.579

3.  Details-First, Show Context, Overview Last: Supporting Exploration of Viscous Fingers in Large-Scale Ensemble Simulations.

Authors:  Timothy Luciani; Andrew Burks; Cassiano Sugiyama; Jonathan Komperda; G Elisabeta Marai
Journal:  IEEE Trans Vis Comput Graph       Date:  2018-08-20       Impact factor: 4.579

4.  Facetto: Combining Unsupervised and Supervised Learning for Hierarchical Phenotype Analysis in Multi-Channel Image Data.

Authors:  Robert Krueger; Johanna Beyer; Won-Dong Jang; Nam Wook Kim; Artem Sokolov; Peter K Sorger; Hanspeter Pfister
Journal:  IEEE Trans Vis Comput Graph       Date:  2019-09-10       Impact factor: 4.579

5.  Activity-Centered Domain Characterization for Problem-Driven Scientific Visualization.

Authors:  G Elisabeta Marai
Journal:  IEEE Trans Vis Comput Graph       Date:  2017-08-29       Impact factor: 4.579

6.  A Virtual Reality Visualization Tool for Neuron Tracing.

Authors:  Will Usher; Pavol Klacansky; Frederick Federer; Peer-Timo Bremer; Aaron Knoll; Jeff Yarch; Alessandra Angelucci; Valerio Pascucci
Journal:  IEEE Trans Vis Comput Graph       Date:  2017-08-29       Impact factor: 4.579

7.  Helium: visualization of large scale plant pedigrees.

Authors:  Paul D Shaw; Martin Graham; Jessie Kennedy; Iain Milne; David F Marshall
Journal:  BMC Bioinformatics       Date:  2014-08-01       Impact factor: 3.169

8.  VisOHC: Designing Visual Analytics for Online Health Communities.

Authors:  Bum Chul Kwon; Sung-Hee Kim; Sukwon Lee; Jaegul Choo; Jina Huh; Ji Soo Yi
Journal:  IEEE Trans Vis Comput Graph       Date:  2016-01       Impact factor: 4.579

9.  Precision Risk Analysis of Cancer Therapy with Interactive Nomograms and Survival Plots.

Authors:  G Elisabeta Marai; Chihua Ma; Andrew Thomas Burks; Filippo Pellolio; Guadalupe Canahuate; David M Vock; Abdallah S R Mohamed; Clifton David Fuller
Journal:  IEEE Trans Vis Comput Graph       Date:  2018-03-20       Impact factor: 4.579

10.  ConTour: Data-Driven Exploration of Multi-Relational Datasets for Drug Discovery.

Authors:  Christian Partl; Alexander Lex; Marc Streit; Hendrik Strobelt; Anne-Mai Wassermann; Hanspeter Pfister; Dieter Schmalstieg
Journal:  IEEE Trans Vis Comput Graph       Date:  2014-12       Impact factor: 4.579

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