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Related Concept Videos

IR-TEx: An Open Source Data Integration Tool for Big Data Transcriptomics Designed for the Malaria Vector Anopheles gambiae08:22

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IR-TEx explores insecticide resistance-related transcriptional profiles in the species Anopheles gambiae. Provided here are full instructions for using the application, modifications for exploring multiple transcriptomic datasets, and using the framework to build an interactive database for collections of transcriptomic data from any organism, generated in any...
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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.
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
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Data Reporting and Recording01:24

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Reporting and recording are crucial in data documentation. The timely, thorough, and accurate documentation of facts is essential when recording patient data. Failure to record findings during an assessment or interpretation of a problem will result in loss of information and make the patient document unreliable. The reader is left with general impressions if the information is not specific. A recording is documenting data of the individual's health information in a traceable, secure, and...
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How Data are Classified: Categorical Data01:11

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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.
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Data collection gathers information needed to make accurate judgments about a patient's present condition. During a health history interview, subjective data is collected from the patient, their caregivers, or family members, and objective data is collected through observations and physical assessment. Patients are the primary source of subjective data. Thus information gathered from patients through interviews, observations, and physical examination is primary data. Secondary sources of...
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Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
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Related Experiment Video

Updated: Jan 19, 2026

IR-TEx: An Open Source Data Integration Tool for Big Data Transcriptomics Designed for the Malaria Vector Anopheles gambiae
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Enabling Web-scale data integration in biomedicine through Linked Open Data.

Maulik R Kamdar1, Javier D Fernández2,3, Axel Polleres2,3

  • 11Center for Biomedical Informatics Research, Stanford University, Stanford, CA USA.

NPJ Digital Medicine
|September 19, 2019
PubMed
Summary
This summary is machine-generated.

Life Sciences Linked Open Data (LSLOD) offers opportunities for integrating fragmented biomedical data. Addressing challenges in LSLOD use can enhance research, improve clinical outcomes, and advance understanding of living systems.

Keywords:
Computational platforms and environmentsData integrationDatabases

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Area of Science:

  • Biomedical Informatics
  • Semantic Web Technologies
  • Linked Data Principles

Background:

  • Biomedical data is fragmented across diverse, heterogeneous sources with varying formats and notations.
  • Researchers face significant challenges in querying, integrating, and analyzing data from multiple sources.
  • Semantic Web and Linked Data principles offer potential for Web-scale semantic processing and data integration.

Purpose of the Study:

  • To explore the opportunities of Life Sciences Linked Open Data (LSLOD) for biomedical data integration.
  • To examine LSLOD applications in pharmacology, cancer research, and infectious diseases.
  • To identify challenges and propose solutions for LSLOD adoption in biomedical research.

Main Methods:

  • Perspective paper discussing the application of LSLOD principles.
  • Analysis of opportunities in specific biomedical domains.
  • Identification of challenges and technical solutions for LSLOD utilization.

Main Results:

  • LSLOD presents opportunities for integrating biomedical data in pharmacology, cancer, and infectious diseases.
  • Several challenges hinder the widespread use and consumption of LSLOD.
  • Technical solutions and insights are proposed to address these challenges.

Conclusions:

  • LSLOD can facilitate the integration of biomedical data and knowledge.
  • Overcoming LSLOD challenges can lead to scalable, intelligent infrastructures.
  • These infrastructures can support AI methods for better clinical outcomes and research quality.