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Data modeling and data communication in GenomEUtwin.

Jan-Eric Litton1, Juha Muilu, Ann Björklund

  • 1Department of Medical Epidemiology and Biostatistics, Karolinska Insitutet, Stockholm, Sweden. jan-eric.litton@mep.ki.se

Twin Research : the Official Journal of the International Society for Twin Studies
|November 20, 2003
PubMed
Summary
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Life sciences research relies on robust database infrastructure for managing and sharing large datasets from population-based twin cohorts. GenomEUtwin is developing new systems for longitudinal data standardization and storage.

Area of Science:

  • Bioinformatics
  • Genomics
  • Epidemiology

Background:

  • Life sciences research generates massive datasets, necessitating advanced database infrastructure for effective data management.
  • Population-based twin cohorts across Europe aim to collect and share extensive data, posing significant data handling challenges.
  • Longitudinal studies, spanning generations, require novel methods for data acquisition and storage.

Purpose of the Study:

  • To outline a preliminary strategy for database infrastructure within the GenomEUtwin project.
  • To address the need for data standardization and robust computer and storage strategies for large-scale twin cohort research.

Main Methods:

  • Description of the preliminary strategy being implemented in the Database Core.
  • Focus on data loading, access, management, querying, analysis, and sharing.

Related Experiment Videos

Main Results:

  • The GenomEUtwin Database Core is implementing a preliminary strategy to manage large-scale twin cohort data.
  • This strategy aims to standardize research data and establish a comprehensive computer and storage plan.

Conclusions:

  • Effective database infrastructure is crucial for competitive life sciences research.
  • Standardization and strategic data management are essential for successful longitudinal population-based twin studies.