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Related Concept Videos

Atomic Emission Spectroscopy: Overview01:20

Atomic Emission Spectroscopy: Overview

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Atomic emission spectroscopy (AES) is an analytical technique used to determine the elemental composition of a sample by analyzing the light emitted from excited atoms. In AES, atoms in a sample are excited to higher energy levels by thermal energy from high-temperature sources, such as plasma, arcs, or sparks. When these excited atoms return to lower energy states, they emit light at specific wavelengths characteristic of each element. The resulting atomic emission spectrum, which consists of...
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When solids, liquids, or condensed gases are heated sufficiently, they radiate some of the excess energy as light. Photons produced in this manner have a range of energies, and thereby produce a continuous spectrum in which an unbroken series of wavelengths is present.
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Flame photometry, also known as flame emission spectrometry, is a technique used for the qualitative and quantitative analysis of elements present in a sample using a flame as the source of excitation energy. The concept of flame photometry was realized in the early 1860s by Kirchhoff and Bunsen, who discovered that specific elements emit characteristic radiation when excited in flames. The first instrument developed for this purpose was used to measure sodium (Na) in plant ash using a Bunsen...
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Voltammograms: Overview01:16

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Voltammograms are current plots as a function of applied potential, offering insights into electrochemical systems. The shape of a voltammogram depends on how the current is measured and whether convection (heat transfer by fluid movement) is present or absent.
Shapes of Voltammograms
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Newman Projections02:06

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Different notations are used to represent the three-dimensional structure of molecules on two-dimensional surfaces. One of the most commonly used representations is the dash-wedge formula. The dashed wedges, solid wedges, and the plane lines indicate the groups situated behind the plane, coming out of the plane, and in the plane, respectively.
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Atomic Emission Spectroscopy: Lab01:29

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AES is a powerful analytical technique, especially effective when used with plasma sources, producing abundant spectra in characteristic emission lines. The Inductively Coupled Plasma (ICP), in particular, yields superior quantitative analytical data due to its high stability, low noise, low background, and minimal interferences under optimal experimental conditions. However, newer air-operated microwave sources are emerging as promising alternatives that could be more cost-effective than...
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Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench
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Watch out for superman: first visualize, then analyze.

Marcin Kozak

    IEEE Computer Graphics and Applications
    |May 9, 2014
    PubMed
    Summary
    This summary is machine-generated.

    Data visualization is crucial before data analysis. Visualizing a dataset before analysis reveals interesting patterns, enhancing understanding and interpretation of quantitative and qualitative variables.

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    Area of Science:

    • Data Science
    • Statistical Analysis
    • Information Visualization

    Background:

    • Traditional data analysis often precedes data exploration.
    • The importance of the order of operations in data interpretation is frequently overlooked.
    • Complex datasets can obscure underlying patterns if not visualized effectively.

    Purpose of the Study:

    • To demonstrate the critical role of data visualization preceding data analysis.
    • To highlight how an initial visual approach can uncover hidden patterns in data.
    • To emphasize the synergistic relationship between visualization and analysis for accurate interpretation.

    Main Methods:

    • A dataset with 100 observations was utilized.
    • The dataset included quantitative variables (y and x) and a qualitative variable (group).
    • Data visualization techniques were applied prior to statistical analysis.

    Main Results:

    • A specific, interesting pattern within the dataset became apparent upon correct visualization.
    • The visualization facilitated a deeper understanding of the interplay between quantitative and qualitative variables.
    • The identified pattern was less obvious through analysis alone.

    Conclusions:

    • Data visualization should be an initial step in the data analysis workflow.
    • Visual exploration enhances the identification of patterns and relationships in datasets.
    • Employing visualization before analysis leads to more robust and insightful findings.