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Related Concept Videos

Data Collection by Experiments01:13

Data Collection by Experiments

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Data collection is a systematic method of obtaining, observing, measuring, and analyzing accurate information. An experimental study is a standard method of data collection that involves the manipulation of the samples by applying some form of treatment prior to data collection. It refers to manipulating one variable to determine its changes on another variable. The sample subjected to treatment is known as “experimental units.”
An example of the experimental method is a public...
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What is an Experiment?01:12

What is an Experiment?

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An experiment is a planned activity carried out under controlled conditions. The purpose of an experiment is to investigate the relationship between two variables. When one variable causes change in another, we call the first variable the explanatory or independent variable. The affected variable is called the response or dependent variable. In a randomized experiment, the researcher manipulates values of the explanatory variable and measures the resulting changes in the response variable. The...
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Thomson's e/m Experiment01:19

Thomson's e/m Experiment

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In a beam of charged particles created by a heated cathode, the particles move at different speeds. However, many applications need a beam with uniform particle speeds. An arrangement known as a velocity selector uses electric and magnetic fields to pick particles with a particular speed from the beam.
A particle with charge q, speed v, and mass m enters an area from the top, where the magnetic and electric fields are perpendicular both to the particle's motion and to one another. The magnetic...
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How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

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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.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
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How Data are Classified: Numerical Data00:59

How Data are Classified: Numerical Data

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Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
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Controls in Experiments01:13

Controls in Experiments

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When conducting an experiment, it is crucial to have control to reduce bias and accurately measure the dependent variables. It also marks the results more reliable. Controls are elements in an experiment that have the same characteristics as the treatment groups but are not affected by the independent variable. By sorting these data into control and experimental conditions, the relationship between the dependent and independent variables can be drawn. A randomized experiment always includes a...
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Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics
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Exploring Cancer in Colorado using a novel data platform: the ECCO experience.

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    This summary is machine-generated.

    Exploring Cancer in Colorado (ECCO) is a new interactive data platform visualizing cancer burden and disparities in Colorado. It integrates diverse public health data to support research and community outreach efforts statewide.

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    Area of Science:

    • Public Health Data Visualization
    • Cancer Epidemiology
    • Health Disparities Research

    Background:

    • Cancer burden and disparities in Colorado require accessible data for effective research and intervention.
    • Existing data sources are often fragmented, hindering comprehensive analysis.

    Purpose of the Study:

    • To develop ECCO (Exploring Cancer in Colorado), an interactive public data platform.
    • To visualize diverse cancer-related data measures for Colorado residents.
    • To support research and outreach efforts focused on cancer burden and disparities.

    Main Methods:

    • Leveraged public data from sources like Cancer InFocus and State Cancer Profiles.
    • Developed a three-tiered web application with PostgreSQL, FastAPI, and Vue 3.
    • Organized data geographically (county/census tract) with ETL pipelines for updates.

    Main Results:

    • ECCO visualizes socio-demographics, cancer risk factors, screening, incidence, and mortality rates.
    • Platform includes location-specific data on cancer care, health services, and environmental exposures.
    • Achieved 1.1K unique visitors and 19K pageviews.

    Conclusions:

    • ECCO serves as a valuable tool for understanding and addressing cancer disparities in Colorado.
    • Interactive visualization enhances identification of at-risk populations and informs interventions.
    • The platform supports research priorities and community outreach initiatives.