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Related Concept Videos

Nominal Level of Measurement00:56

Nominal Level of Measurement

The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. Not every statistical operation can be used with every set of data. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
The data that cannot be measured but can be grouped into categories fall under the nominal level of measurement. Data that is measured using a nominal scale is...
Polygenic Traits01:18

Polygenic Traits

When more than one gene is responsible for a given phenotype, the trait is considered polygenic. Human height is a polygenic trait. Studies have uncovered hundreds of loci that influence height, and there are believed to be many more. Due to the high number of genes involved, as well as environmental and nutritional factors, height varies significantly within a given population. The distribution of height forms a bell-shaped curve, with relatively few individuals in the population at the...
Polygenic Traits01:18

Polygenic Traits

When more than one gene is responsible for a given phenotype, the trait is considered polygenic. Human height is a polygenic trait. Studies have uncovered hundreds of loci that influence height, and there are believed to be many more. Due to the high number of genes involved, as well as environmental and nutritional factors, height varies significantly within a given population. The distribution of height forms a bell-shaped curve, with relatively few individuals in the population at the...
Ordinal Level of Measurement00:55

Ordinal Level of Measurement

The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
Data measured using an ordinal scale are similar to nominal scale data, but there is one major difference. The ordinal scale data can be ordered. An example of ordinal scale data is a list of the top five national parks in the...
Data: Types and Distribution01:19

Data: Types and Distribution

In biostatistics, data are the observations collected for analysis. There are two main types: parametric and non-parametric. Parametric data, which include continuous (e.g., weight) and discrete numerical data (e.g., number of tablets), assume a particular distribution pattern, often the normal distribution. Non-parametric data do not adhere to a specific distribution and typically comprise nominal (e.g., gender) and ordinal categorical data (e.g., pain scale ratings).
Distributions in...
Genomics02:02

Genomics

Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...

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Updated: May 21, 2026

Quantification of Orofacial Phenotypes in Xenopus
09:26

Quantification of Orofacial Phenotypes in Xenopus

Published on: November 6, 2014

Three ontologies to define phenotype measurement data.

Mary Shimoyama1, Rajni Nigam, Leslie Sanders McIntosh

  • 1Human and Molecular Genetics Center, Medical College of Wisconsin Milwaukee, WI, USA. shimoyama@mcw.edu

Frontiers in Genetics
|June 2, 2012
PubMed
Summary
This summary is machine-generated.

Researchers can now integrate phenotype data across diverse studies using a new ontology-based framework. This approach unifies data from human and model organism studies for easier access and analysis.

Keywords:
ontologyphenotype

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Area of Science:

  • Biomedical Informatics
  • Genomics
  • Data Science

Background:

  • Growing demand for integrated phenotype data across human and model organism studies.
  • Current data access is limited to single experiments or identical protocols.
  • Lack of standardized methods hinders cross-study data utilization.

Purpose of the Study:

  • To develop an ontology-based framework for integrating phenotype measurement data.
  • To enable unified access to phenotype data from multiple, heterogeneous studies.
  • To facilitate data retrieval and analysis across diverse research contexts.

Main Methods:

  • Creation of three key ontologies: Clinical Measurement Ontology, Measurement Method Ontology, and Experimental Condition Ontology.
  • Development of a framework leveraging these ontologies for data integration.
  • Application of the framework to consolidate rat phenotype data and human epidemiological data.

Main Results:

  • Successful integration of rat phenotype data from multiple studies into a single resource.
  • Facilitation of data integration from multiple human epidemiological studies into a centralized repository.
  • Demonstration of a unified data resource accessible through the ontology-based framework.

Conclusions:

  • An ontology-based framework enables successful integration of phenotype measurement data.
  • This framework overcomes technological barriers, allowing data integration regardless of underlying structures.
  • Users can easily query and retrieve integrated phenotype data from multiple studies, enhancing research capabilities.