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Related Concept Videos

Time-Series Graph00:54

Time-Series Graph

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A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
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Scatter Plot01:15

Scatter Plot

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The most common and easiest way to display the relationship between two variables, x and y, is a scatter plot. A scatter plot shows the direction of a relationship between the variables. A clear direction happens when there is either:
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Multiple Bar Graph01:07

Multiple Bar Graph

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As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...
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Two-Dimensional (2D) NMR: Overview01:12

Two-Dimensional (2D) NMR: Overview

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The 1D NMR spectrum of large and complex molecules like natural products has complicated splitting patterns and overlapping signals, which can be easily interpreted using 2-dimensional (2D) NMR. Unlike 1D NMR, 2D NMR has two frequency axes that provide the coupling information between the nucleus A and nucleus B in a molecule. The process from which 2D spectra are obtained has four steps.
The first step is the preparation period, during which nucleus A is excited with a radiofrequency pulse....
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Relative Frequency Histogram01:14

Relative Frequency Histogram

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The relative frequency depicts the proportion of data points that have each value. The frequency tells the number of data points that have each value. Like the histogram, a relative frequency histogram also has the same shape with a horizontal scale (the x-axis), but the vertical scale (the y-axis) is marked with relative frequencies (percentages of the whole) instead of actual frequencies. A relative frequency histogram is a graphical representation of a frequency distribution where the...
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Bar Graph01:07

Bar Graph

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A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...
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Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
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Visualizing High-Dimensional Data: Advances in the Past Decade.

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    This summary is machine-generated.

    This survey reviews recent advances in high-dimensional data visualization techniques. It offers guidance for data practitioners and identifies future research directions in exploring complex datasets.

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    Area of Science:

    • Computer Science
    • Data Science
    • Information Visualization

    Background:

    • Massive simulations and sensing devices generate large, complex, high-dimensional datasets across many scientific fields.
    • Effective visualization is crucial for exploring and understanding these complex datasets.
    • The past decade has seen significant advancements in high-dimensional data visualization.

    Purpose of the Study:

    • To provide a comprehensive survey of recent advances in high-dimensional data visualization.
    • To offer guidance for data practitioners in navigating these advancements.
    • To inspire new visualization creation and identify future research opportunities.

    Main Methods:

    • Comprehensive literature review focusing on the last decade of high-dimensional data visualization research.
    • Modular organization of recent visualization advances.
    • Analysis of the visualization pipeline and research opportunities.

    Main Results:

    • A structured overview of key developments in high-dimensional data visualization over the past 10 years.
    • Identification of trends and emerging techniques in the field.
    • A framework for understanding the visualization pipeline for complex datasets.

    Conclusions:

    • Recent advances provide powerful tools for exploring high-dimensional data.
    • Guidance is offered to practitioners for effective use and development of visualizations.
    • Future research should focus on novel techniques and addressing emerging challenges in data visualization.