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Cloud-based relational database for multimodal animal data.

Niklas Pallast1, Frederique Wieters1, Marieke Nill1

  • 1Department of Neurology, University Hospital Cologne, Cologne, Germany.

Database : the Journal of Biological Databases and Curation
|December 22, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a novel cloud-based database for managing complex pre-clinical research data. It enhances data accessibility and standardization for multimodal animal studies.

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Area of Science:

  • Pre-clinical research
  • Multimodal data analysis
  • Bioinformatics

Background:

  • Pre-clinical research utilizes diverse in vivo and ex vivo tools, generating complex multimodal data.
  • Efficient data management is crucial for correlating results across different scales and time points.
  • Most pre-clinical laboratories lack robust electronic databases for handling such data.

Purpose of the Study:

  • To present a novel cloud-based relational database for multimodal animal data management.
  • To improve data accessibility, standardization, and handling efficiency in pre-clinical research.
  • To provide a customizable and user-friendly solution for labs working with laboratory animals.

Main Methods:

  • Development of a cloud-based relational database using commercial software.
  • Implementation of data fields for magnetic resonance imaging (MRI), histology, and behavior.
  • Inclusion of automated procedures for calculations and pre-set features for analysis and documentation.
  • Web browser-based, operating-system independent platform with multi-user capability and user management for data entry control.

Main Results:

  • The database successfully integrates multimodal data from different specimen and time points.
  • Automated procedures and pre-set features streamline analysis and documentation.
  • The system supports multiple users simultaneously and ensures data integrity through monitored entry.
  • Enhanced data accessibility, standardization, and handling efficiency were achieved.

Conclusions:

  • The novel database provides an efficient solution for managing complex multimodal data in pre-clinical research.
  • Its customizable template and user-friendly interface require no prior scripting expertise, making it accessible to various labs.
  • The database improves data standardization, accessibility, and overall research efficiency.