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Related Experiment Video

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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
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Incorporating Data Citation in a Biomedical Repository: An Implementation Use Case.

Snehil Gupta1, Connie Zabarovskaya1, Brian Romine1

  • 1Washington University in St. Louis, USA.

AMIA Joint Summits on Translational Science Proceedings. AMIA Joint Summits on Translational Science
|August 18, 2017
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Summary

This study introduces a novel informatics system for dynamic research data, enabling reproducible clinical data snapshots. The system successfully recreates past datasets using persistent identifiers (PIDs) for reliable data citation.

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Area of Science:

  • Informatics
  • Data Science
  • Clinical Research Informatics

Background:

  • Research data is dynamic, posing challenges for reproducible citation.
  • Existing data repositories often struggle with point-in-time data snapshots and long-term sustainability.
  • Reproducible research requires the ability to recreate specific historical datasets.

Purpose of the Study:

  • To establish a robust informatics system for dynamic data management and reproducible research.
  • To implement key recommendations from the Research Data Alliance (RDA) for data citation.
  • To enhance a clinical data repository for long-term data sustainability and accurate data reproduction.

Main Methods:

  • Adopted a subset of RDA data citation working group recommendations.
  • Implemented data versioning, time-stamping, query storing, query time-stamping, and query persistent identifiers (PIDs).
  • Developed the system entirely at the database level within an evolving clinical data repository.

Main Results:

  • Successfully implemented a system supporting dynamic data and reproducible research.
  • Demonstrated the ability to reproduce a specific point-in-time dataset using only its PID.
  • Validated the long-term sustainability and reproducibility of dynamic data within the repository.

Conclusions:

  • The developed informatics system effectively addresses the challenges of citing and reproducing dynamic research data.
  • Implementing RDA recommendations at the database level enhances data reproducibility and citation reliability.
  • This approach provides a sustainable solution for managing and citing evolving clinical datasets.