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Archival Research

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...

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Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...

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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when researchers try to extrapolate results...

Longitudinal Research

Longitudinal Research

Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...

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Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

Published on: November 22, 2019

RERF databases and implications for future studies.

¹Department of Information Technology, Radiation Effects Research Foundation, 5-2 Hijiyama-koen, Minami-ku, Hiroshima, 732-0815, Japan. katayama@rerf.jp

Radiation Protection Dosimetry

|August 24, 2012

Summary

This summary is machine-generated.

The Radiation Effects Research Foundation (RERF) is developing a new database to manage extensive data on atomic bomb radiation effects. This system will improve researcher access and enable future bioinformatics analyses.

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Area of Science:

Radiation effects research
Bioinformatics
Data management

Background:

The Radiation Effects Research Foundation (RERF) has collected extensive data since 1947 on atomic bomb survivors.
Data includes medical records, questionnaires, analytical results, biosamples, and documentation.
Existing systems require enhancement for efficient data retrieval and advanced analysis.

Purpose of the Study:

To develop an advanced archival database system for RERF.
To facilitate timely researcher access to diverse datasets.
To support future bioinformatics analyses, including genomics and proteomics.

Main Methods:

Developing a new database system capable of handling large volumes of data.
Integrating various data types: medical examinations, questionnaires, analytical results, biosamples, and documentation.
Implementing features for efficient data retrieval and future analytical capabilities.

Main Results:

A new database system is under development.
The system is designed to manage a very large amount of data.
The system will permit future bioinformatics analyses.

Conclusions:

The new database system is crucial for advancing research on radiation effects.
Enhanced data accessibility and analytical capabilities will support future scientific discoveries.
This initiative ensures the long-term value of RERF's unique data repository.