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Archival Research01:40

Archival Research

Some researchers gain access to large amounts of data without interacting with a single research participant. Instead, they use existing records to answer various research questions. This type of research approach is known as archival research. Archival research relies on looking at past records or data sets to look for interesting patterns or relationships. For example, a researcher might access the academic records of all individuals who enrolled in college within the past ten years and...
Ethics in Research01:56

Ethics in Research

Today, scientists agree that good research is ethical in nature and is guided by a basic respect for human dignity and safety. However, this has not always been the case. Modern researchers must demonstrate that the research they perform is ethically sound.
Data Reporting and Recording01:24

Data Reporting and Recording

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...
Data Collection by Experiments01:13

Data Collection by Experiments

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 clinical trial...
Data Collection by Observations01:08

Data Collection by Observations

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.
An astronomer viewing the motion and brightness of stars in the sky and recording the data is an example of observational data collection. A botanist recording...
Cross-Sectional Research01:50

Cross-Sectional Research

In cross-sectional research, a researcher compares multiple segments of the population at the same time. If they were interested in people's dietary habits, the researcher might directly compare different groups of people by age. Instead of following a group of people for 20 years to see how their dietary habits changed from decade to decade, the researcher would study a group of 20-year-old individuals and compare them to a group of 30-year-old individuals and a group of 40-year-old...

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Related Experiment Video

Updated: Jun 13, 2026

TBase - an Integrated Electronic Health Record and Research Database for Kidney Transplant Recipients
09:00

TBase - an Integrated Electronic Health Record and Research Database for Kidney Transplant Recipients

Published on: April 13, 2021

Integration of data for research.

Marienne Hibbert1, Jason Lohrey, Steve Melnikoff

  • 1BioGrid, Melbourne Health, University of Melbourne and VPAC, Australia. Marienne.hibbert@biogrid.org.au

Studies in Health Technology and Informatics
|April 22, 2010
PubMed
Summary
This summary is machine-generated.

This chapter explains the clinical research lifecycle, data management, and linkage technologies. It highlights the importance of data standardization and identifiers for improving health outcomes through collaborative research.

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Last Updated: Jun 13, 2026

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Area of Science:

  • Health Informatics
  • Clinical Research Management
  • Data Science

Background:

  • The clinical research lifecycle involves complex data management challenges.
  • Effective data handling is crucial for reproducible and impactful research.
  • Current data practices often lack standardization, hindering collaboration and data reuse.

Purpose of the Study:

  • To provide an educational overview of the clinical research lifecycle.
  • To discuss the critical need for contextual data standardization.
  • To explore information management principles and data linkage technologies for sustainable research data.

Main Methods:

  • Overview of the clinical research lifecycle stages.
  • Discussion of various research data sources.
  • Explanation of data standardization principles.
  • Exploration of data linkage technologies and health identifiers.

Main Results:

  • Standardization of contextual data is essential for retaining meaning.
  • Sustainable data management requires robust information management principles.
  • Data linkage technologies facilitate collaborative research for better health outcomes.
  • Effective use of health identifiers is key for data integration.

Conclusions:

  • Implementing standardized data practices and leveraging linkage technologies are vital for advancing health outcomes.
  • A comprehensive understanding of the research lifecycle and data management is necessary for efficient and collaborative scientific endeavors.
  • The strategic use of identifiers enhances data interoperability and research value.