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Related Concept Videos

Data Collection by Observations01:08

Data Collection by Observations

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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.
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...
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Data Reporting and Recording01:24

Data Reporting and Recording

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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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Archival Research01:40

Archival Research

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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...
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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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Data Collection I01:30

Data Collection I

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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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Sampling Distribution

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Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...
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Updated: May 5, 2026

Establishment of a Clinic-based Biorepository
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Sample and data sharing: observations from a central data repository.

Mary-Anne Ardini1, Huaqin Pan1, Ying Qin1

  • 1RTI International, PO Box 12194, Research Triangle Park, NC 27709, USA.

Clinical Biochemistry
|December 3, 2013
PubMed
Summary
This summary is machine-generated.

RTI International managed the National Institute of Diabetes, Digestive and Kidney Diseases (NIDDK) Central Repository (CR), facing challenges in data and sample sharing. Future bio-banking requires flexible infrastructure to support evolving research needs.

Keywords:
BiobankClinical data repositoryNIDDK

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Area of Science:

  • Biomedical research
  • Data management
  • Bio-banking

Background:

  • RTI International served as the data repository for the National Institute of Diabetes, Digestive and Kidney Diseases (NIDDK) from 2003 to 2013.
  • Established and maintained the Central Repository (CR) in collaboration with sample repository partners.
  • Facilitated data and sample accessibility for approved researchers.

Purpose of the Study:

  • Recap the establishment of the data and sample sharing mechanism.
  • Detail challenges and limitations encountered in repository operations.
  • Explore the future of resource sharing in research.

Main Methods:

  • Described the operational aspects of the Central Repository.
  • Highlighted the need for flexibility and dynamism in system maintenance.
  • Emphasized compliance with established data standards.

Main Results:

  • Identified difficulties inherent in operating a repository dependent on external parties.
  • Noted the direct impact of external expertise and efficiency on repository functioning.
  • Gained observations on the practical challenges of data and sample sharing.

Conclusions:

  • Anticipated global centralization in bio-banking for disease study and environmental monitoring.
  • Stressed the need for dynamic infrastructure adapting to emerging technologies.
  • Emphasized the importance of flexibility and adherence to standards for future research support organizations.