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Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
09:43

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

Published on: November 22, 2019

Managing data for the international, multicentre INTERGROWTH-21st Project.

E O Ohuma1, L Hoch, C Cosgrove

  • 1Nuffield Department of Obstetrics & Gynaecology, and, Oxford Maternal & Perinatal Health Institute, Green Templeton College, University of Oxford, Oxford, UK; Centre for Statistics in Medicine, Wolfson College Annexe, University of Oxford, Oxford, UK, UK.

BJOG : an International Journal of Obstetrics and Gynaecology
|May 18, 2013
PubMed
Summary
This summary is machine-generated.

The INTERGROWTH-21st Project implemented a robust data management system across eight international centers. This ensured high-quality data collection through standardized processes and rigorous validation for global health research.

Keywords:
Data managementINTERGROWTH-21stfetal growthstandards

Related Experiment Videos

Last Updated: May 11, 2026

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
09:43

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

Published on: November 22, 2019

Area of Science:

  • Global Health
  • Clinical Research
  • Data Management

Background:

  • The INTERGROWTH-21st Project required a robust data management strategy to handle large-scale international research.
  • Standardized data collection and validation were crucial for ensuring data integrity across multiple study centers.

Purpose of the Study:

  • To describe the data management system implemented for the INTERGROWTH-21st Project.
  • To highlight the methods used to ensure high-quality data collection in a multi-center international study.

Main Methods:

  • A hybrid centralized and decentralized data management system was employed, utilizing a common database and standardized instruments.
  • Local data management units handled data entry and validation, feeding into a central system managed by the Data Coordinating Unit.
  • Internal data validations, online data entry, real-time monitoring, and comprehensive audit trails were implemented to maintain data integrity.

Main Results:

  • The system successfully managed large data volumes from eight international study centers.
  • Rigorous validation and monitoring processes were effective in preventing incorrect data capture.
  • An audit trail ensured accountability and traceability of all database updates.

Conclusions:

  • The implemented data management system ensured exceptionally high-quality data for the INTERGROWTH-21st Project.
  • The combination of standardized procedures and centralized oversight proved effective for multi-center global health research.