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A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
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Data are individual items of information obtained from a population or sample. Data may be classified as qualitative (categorical), quantitative continuous, or quantitative discrete. Because it is not practical to measure the entire population in a study, researchers use samples to represent the population. A random sample is a representative group from the population chosen by using a method that gives each individual in the population an equal chance of being included in the sample. Random...
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Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
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Toward a Taxonomy for Adaptive Data Visualization in Analytics Applications.

Tristan Poetzsch1, Panagiotis Germanakos2, Lynn Huestegge1

  • 1Department of Psychology, Julius-Maximilians-University Würzburg, Würzburg, Germany.

Frontiers in Artificial Intelligence
|March 18, 2021
PubMed
Summary
This summary is machine-generated.

Data visualization should adapt to users and context, moving beyond generic solutions. A new taxonomy helps tailor visualizations, though further research is needed for user-adaptive data analysis.

Keywords:
analyticsdata visualizationgraph adaptivitygraph ergonomicsrecommendation engineuser model

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Area of Science:

  • Data analytics and visualization
  • Human-computer interaction
  • Information visualization

Background:

  • Data analytics faces commoditization due to increasing data, user diversity, and automated solutions.
  • Current data visualization methods often lack user and context specificity.
  • Interindividual variability exists in expert data visualization choices.

Purpose of the Study:

  • To investigate the hypothesis that data visualizations should be adapted to both the user and the context.
  • To develop a systematic, taxonomic approach for user-adaptive data visualization.
  • To provide a theoretical framework and empirical evaluation for adapting visualizations.

Main Methods:

  • Empirical studies (Studies 1-4) involving data visualization choices and user modeling.
  • Development of a user model incorporating traits, states, strategies, and actions.
  • Creation and preliminary validation of a taxonomy for adaptive visualization recommendations.

Main Results:

  • Significant interindividual variability in data visualization choices among experts.
  • Statistical expertise identified as a key user trait for adaptation.
  • User intentions (e.g., monitoring, analysis) are crucial for adapting to user states.
  • A taxonomy was developed to guide adaptive visualization recommendations.

Conclusions:

  • User-adaptive data visualization, guided by a taxonomy considering user traits and states, shows promise.
  • The approach requires further validation with more participants and diverse contexts.
  • Tailoring visualizations to users and context is essential for effective data analysis.