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Related Concept Videos

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

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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.
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Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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Data collection refers to a systematic way of obtaining, observing, measuring, and analyzing accurate information. Observational studies are one of the most widely used methods of data collection. It involves collecting data by observing the behavior and physical characteristics of a sample without making any modifications to the sample.
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A schema is a mental construct consisting of a cluster or collection of related concepts (Bartlett, 1932). There are many different types of schemata, and they all have one thing in common: schemata are a method of organizing information that allows the brain to work more efficiently. When a schema is activated, the brain makes immediate assumptions about the person or object being observed.
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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
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The Semantic Data Dictionary - An Approach for Describing and Annotating Data.

Sabbir M Rashid1, James P McCusker1, Paulo Pinheiro1

  • 1Rensselaer Polytechnic Institute, Troy, NY, 12180, USA.

Data Intelligence
|October 26, 2020
PubMed
Summary
This summary is machine-generated.

We introduce the Semantic Data Dictionary, a machine-readable standard for data interpretation. This approach enhances data discovery, interoperability, and reproducibility across diverse datasets.

Keywords:
CodebookDataData DictionaryData IntegrationDictionary MappingFAIRKnowledge ModelingMapping LanguageMetadata StandardSemantic Data DictionarySemantic ETLSemantic Web

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Area of Science:

  • Data Science
  • Biomedical Informatics
  • Semantic Web Technologies

Background:

  • Traditional data dictionaries lack machine readability and standardization.
  • Interpreting diverse datasets is challenging due to inconsistent data dictionaries.
  • Need for a standardized approach to data description for improved data usability.

Purpose of the Study:

  • Introduce the Semantic Data Dictionary (SDD) specification.
  • Formalize semantic representation of data for standardization and harmonization.
  • Enhance data discovery, interoperability, reuse, traceability, and reproducibility.

Main Methods:

  • Developed a specification for formalizing semantic data representation.
  • Applied the SDD approach to biomedical data, including the National Health and Nutrition Examination Survey dataset.
  • Demonstrated application in sponsored research projects, including an NIH-funded data portal and the RPI-IBM HEALS project.

Main Results:

  • The SDD enables machine-readable data dictionaries.
  • Standardization and harmonization of diverse datasets are achieved.
  • Improved data discovery, interoperability, and reproducibility are demonstrated.

Conclusions:

  • The Semantic Data Dictionary addresses limitations of traditional data dictionaries.
  • SDD promotes enhanced data usability and integration across domains.
  • The approach is applicable to various data domains beyond biomedical research.